Three-dimensional data encoding method, three-dimensional data decoding method, three-dimensional data encoding device, and three-dimensional data decoding device

Abstract

The invention provides a three-dimensional data encoding method, a three-dimensional data decoding method, a three-dimensional data encoding device, and a three-dimensional data decoding device. A three-dimensional data encoding method acquires first three-dimensional point group data including a plurality of three-dimensional points and second three-dimensional point group data including a plurality of three-dimensional points, encodes the first three-dimensional point group data by a first encoding process, encodes the second three-dimensional point group data by a second encoding process, and encodes the second three-dimensional point group data by a third encoding process. The first encoding process is an encoding process suitable for dense three-dimensional point group data.

Description

[0001] This application is a divisional of the patent application with the filing date of January 16, 2019, application number 201980013434.6, and invention title "3D Data Encoding Method, 3D Data Decoding Method, 3D Data Encoding Apparatus, and 3D Data Decoding Apparatus". Technical Field

[0002] The present disclosure relates to a 3D data encoding method, a 3D data decoding method, a 3D data encoding apparatus, and a 3D data decoding apparatus. Background Art

[0003] In large fields such as computer vision, map information, monitoring, infrastructure inspection, or video distribution for autonomous operation of automobiles or robots, devices or services that make flexible use of 3D data will be popularized in the future. 3D data is obtained by various methods such as distance sensors such as rangefinders, stereo cameras, or combinations of multiple monocular cameras.

[0004] As a representation method of 3D data, there is a representation method called point cloud, which represents the shape of a 3D structure by a point group in a 3D space (for example, refer to Non-Patent Document 1). In the point cloud, the positions and colors of the point group are stored. Although it is expected that the point cloud will become the mainstream as a representation method of 3D data, the data volume of the point group is very large. Therefore, in the storage or transmission of 3D data, like 2D dynamic images (as an example, MPEG-4 AVC or HEVC standardized by MPEG), data volume compression needs to be performed by encoding.

[0005] In addition, regarding the compression of point clouds, part of it is supported by publicly available program libraries (PointCloud Library: PCL) that perform point cloud association processing.

[0006] In addition, there is a well-known technology that uses 3D map data to retrieve facilities around a vehicle and display them (for example, refer to Patent Document 1).

[0007] Prior Art Documents

[0008] Patent Documents

[0009] Patent Document 1 International Publication No. 2014 / 020663 Summary of the Invention

[0010] Problems to be Solved by the Invention

[0011] It is desired to improve the encoding efficiency in the encoding of 3D data.

[0012] The object of the present disclosure is to provide a three-dimensional data encoding method, a three-dimensional data decoding method, a three-dimensional data encoding apparatus, or a three-dimensional data decoding apparatus capable of improving the encoding efficiency.

[0013] Means for solving the problem

[0014] Regarding a three-dimensional data encoding method according to an aspect of the present disclosure, first three-dimensional point group data including a plurality of three-dimensional points and second three-dimensional point group data including a plurality of three-dimensional points are obtained, the first three-dimensional point group data is encoded by a first encoding process, and the second three-dimensional point group data is encoded by a second encoding process, and the first encoding process is an encoding process suitable for dense three-dimensional point group data.

[0015] Regarding a three-dimensional data encoding method according to an aspect of the present disclosure, first three-dimensional data including a plurality of three-dimensional points and second three-dimensional data including a plurality of three-dimensional points are obtained, the first three-dimensional data is encoded by a first encoding process, and the second three-dimensional data is encoded by a second encoding process, and the density of the plurality of three-dimensional points included in the first three-dimensional data is greater than the density of the plurality of three-dimensional points included in the second three-dimensional data.

[0016] Regarding a three-dimensional data encoding method according to an aspect of the present disclosure, first three-dimensional data including a plurality of dense three-dimensional points and second three-dimensional data including a plurality of non-dense three-dimensional points are obtained, the first three-dimensional data is encoded by a first encoding process, and the second three-dimensional data is encoded by a second encoding process.

[0017] Regarding a three-dimensional data decoding method according to an aspect of the present disclosure, first three-dimensional point group data including a plurality of encoded three-dimensional points and second three-dimensional point group data including a plurality of encoded three-dimensional points are obtained from a bitstream, the first three-dimensional point group data is decoded by a first decoding process, and the second three-dimensional point group data is decoded by a second decoding process, and the first decoding process is a decoding process suitable for dense three-dimensional point group data.

[0018] Regarding a three-dimensional data decoding method according to an aspect of the present disclosure, three-dimensional point group data including a plurality of encoded three-dimensional points is obtained from a bitstream, the three-dimensional point group data is separated into dense three-dimensional point group data and non-dense three-dimensional point group data, the dense three-dimensional point group data is decoded by a first decoding process, and the non-dense three-dimensional point group data is decoded by a second decoding process.

[0019] A three-dimensional data decoding method according to an aspect of the present disclosure obtains first three-dimensional data including a plurality of encoded three-dimensional points and second three-dimensional data including a plurality of encoded three-dimensional points, decodes the first three-dimensional data through a first decoding process, decodes the second three-dimensional data through a second decoding process, and the density of the plurality of encoded three-dimensional points included in the first three-dimensional data is greater than the density of the plurality of encoded three-dimensional points included in the second three-dimensional data.

[0020] A three-dimensional data decoding method according to an aspect of the present disclosure obtains first three-dimensional data including a plurality of densely encoded three-dimensional points and second three-dimensional data including a plurality of non-densely encoded three-dimensional points, decodes the first three-dimensional data through a first decoding process, and decodes the second three-dimensional data through a second decoding process.

[0021] Advantageous Effects of the Invention

[0022] The present disclosure can provide a three-dimensional data encoding method, a three-dimensional data decoding method, a three-dimensional data encoding device, or a three-dimensional data decoding device that can improve the encoding efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 Shows the configuration of the encoded three-dimensional data according to Embodiment 1.

[0024] Figure 2 Shows an example of the prediction structure between SPCs belonging to the lowest layer of the GOS according to Embodiment 1.

[0025] Figure 3 Shows an example of the inter-layer prediction structure according to Embodiment 1.

[0026] Figure 4 Shows an example of the encoding order of the GOS according to Embodiment 1.

[0027] Figure 5 Shows an example of the encoding order of the GOS according to Embodiment 1.

[0028] Figure 6 Is a block diagram of a three-dimensional data encoding device according to Embodiment 1.

[0029] Figure 7 Is a flowchart of the encoding process according to Embodiment 1.

[0030] Figure 8 Is a block diagram of a three-dimensional data decoding device according to Embodiment 1.

[0031] Fig. 9 Is a flowchart of the decoding process according to Embodiment 1.

[0032] Fig.10 Shows an example of the meta information related to Embodiment 1.

[0033] Fig.11 Shows a configuration example of the SWLD related to Embodiment 2.

[0034] Fig.12 Shows an operation example of the server and the client related to Embodiment 2.

[0035] Fig.13 Shows an operation example of the server and the client related to Embodiment 2.

[0036] Fig.14 Shows an operation example of the server and the client related to Embodiment 2.

[0037] Fig.15 Shows an operation example of the server and the client related to Embodiment 2.

[0038] Fig.16 Is a block diagram of the three-dimensional data encoding device related to Embodiment 2.

[0039] Fig.17 Is a flowchart of the encoding process related to Embodiment 2.

[0040] Fig.18 Is a block diagram of the three-dimensional data decoding device related to Embodiment 2.

[0041] Fig.19 Is a flowchart of the decoding process related to Embodiment 2.

[0042] Fig. 20 Shows a configuration example of the WLD related to Embodiment 2.

[0043] Fig.21 Shows an example of the octree structure of the WLD related to Embodiment 2.

[0044] Fig. 22 Shows a configuration example of the SWLD related to Embodiment 2.

[0045] Fig.23 Shows an example of the octree structure of the SWLD related to Embodiment 2.

[0046] Fig.24 Is a block diagram of the three-dimensional data production device related to Embodiment 3.

[0047] Fig.25 Is a block diagram of the three-dimensional data transmission device related to Embodiment 3.

[0048] Fig.26 is a block diagram of the three-dimensional information processing apparatus according to Embodiment 4.

[0049] Fig. 27 is a block diagram of the three-dimensional data creation apparatus according to Embodiment 5.

[0050] Fig.28 Shows the configuration of the system according to Embodiment 6.

[0051] Fig.29 is a block diagram of the client device according to Embodiment 6.

[0052] Fig.30 is a block diagram of the server according to Embodiment 6.

[0053] Fig.31 is a flowchart of the three-dimensional data creation process performed by the client device according to Embodiment 6.

[0054] Fig.32 is a flowchart of the sensor information transmission process performed by the client device according to Embodiment 6.

[0055] Fig.33 is a flowchart of the three-dimensional data creation process performed by the server according to Embodiment 6.

[0056] Fig.34 is a flowchart of the three-dimensional map transmission process performed by the server according to Embodiment 6.

[0057] Fig.35 Shows the configuration of a modification of the system according to Embodiment 6.

[0058] Fig.36 Shows the configuration of the server and the client device according to Embodiment 6.

[0059] Fig.37 is a block diagram of the three-dimensional data encoding apparatus according to Embodiment 7.

[0060] Fig.38 Shows an example of the prediction residual according to Embodiment 7.

[0061] Fig.39 Shows an example of the volume according to Embodiment 7.

[0062] Fig.40 Shows an example of the octree representation of the volume according to Embodiment 7.

[0063] Fig.41 Shows an example of the bit string of the volume according to Embodiment 7.

[0064] Fig.42 Shows an example of the octree representation of the volume related to Embodiment 7.

[0065] Fig.43 Shows an example of the volume related to Embodiment 7.

[0066] Fig.44 Is a diagram for explaining the intra prediction processing related to Embodiment 7.

[0067] Fig.45 Is a diagram for explaining the rotation and translation processing related to Embodiment 7.

[0068] Fig.46 Shows an example of the syntax of the RT application flag and RT information related to Embodiment 7.

[0069] Fig.47 Is a diagram for explaining the inter prediction processing related to Embodiment 7.

[0070] Fig.48 Is a block diagram of the three-dimensional data decoding device related to Embodiment 7.

[0071] Fig.49 Is a flowchart of the three-dimensional data encoding process performed by the three-dimensional data encoding device related to Embodiment 7.

[0072] Fig.50 Is a flowchart of the three-dimensional data decoding process performed by the three-dimensional data decoding device related to Embodiment 7.

[0073] Fig.51 Shows the configuration of the distribution system related to Embodiment 8.

[0074] Fig.52 Shows a configuration example of the bitstream of the encoded three-dimensional map related to Embodiment 8.

[0075] Fig.53 Is a diagram for explaining the improvement effect of the encoding efficiency related to Embodiment 8.

[0076] Fig.54 Is a flowchart of the process performed by the server related to Embodiment 8.

[0077] Fig.55 Is a flowchart of the process performed by the client related to Embodiment 8.

[0078] Fig.56 Shows a syntax example of the sub-map related to Embodiment 8.

[0079] Fig.57Shows the switching process of the coding type related to Embodiment 8 in a schematic diagram.

[0080] Fig.58 Shows a syntactic example of a sub-map related to Embodiment 8.

[0081] Fig.59 Is a flowchart of the 3D data encoding process related to Embodiment 8.

[0082] Fig.60 Is a flowchart of the 3D data decoding process related to Embodiment 8.

[0083] Fig.61 Shows the operation of a modified example of the switching process of the coding type related to Embodiment 8 in a schematic diagram.

[0084] Fig.62 Shows the operation of a modified example of the switching process of the coding type related to Embodiment 8 in a schematic diagram.

[0085] Fig.63 Shows the operation of a modified example of the switching process of the coding type related to Embodiment 8 in a schematic diagram.

[0086] Fig.64 Shows the operation of a modified example of the calculation process of the difference value related to Embodiment 8 in a schematic diagram.

[0087] Fig.65 Shows the operation of a modified example of the calculation process of the difference value related to Embodiment 8 in a schematic diagram.

[0088] Fig.66 Shows the operation of a modified example of the calculation process of the difference value related to Embodiment 8 in a schematic diagram.

[0089] Fig.67 Shows the operation of a modified example of the calculation process of the difference value related to Embodiment 8 in a schematic diagram.

[0090] Fig.68 Shows a syntactic example of the volume related to Embodiment 8.

[0091] Fig.69 Is a diagram showing an example of an important area related to Embodiment 9.

[0092] Fig.70 Is a diagram showing an example of an occupancy rate code related to Embodiment 9.

[0093] Fig.71 Is a diagram showing an example of a quadtree structure related to Embodiment 9.

[0094] Fig.72It is a diagram showing an example of the occupancy code and position code related to Embodiment 9.

[0095] Fig.73 It is a diagram showing an example of three-dimensional points obtained in the LiDAR related to Embodiment 9.

[0096] Fig.74 It is a diagram showing an example of the octree structure related to Embodiment 9.

[0097] Fig.75 It is a diagram showing an example of the hybrid coding related to Embodiment 9.

[0098] Fig.76 It is a diagram for explaining the switching method of the position coding and occupancy coding related to Embodiment 9.

[0099] Fig.77 It is a diagram showing an example of the bitstream of the position coding related to Embodiment 9.

[0100] Fig.78 It is a diagram showing an example of the bitstream of the hybrid coding related to Embodiment 9.

[0101] Fig.79 It is a diagram showing the tree structure of the occupancy code of the important three-dimensional points related to Embodiment 9.

[0102] Fig.80 It is a diagram showing the tree structure of the occupancy code of the non-important three-dimensional points related to Embodiment 9.

[0103] Fig.81 It is a diagram showing an example of the bitstream of the hybrid coding related to Embodiment 9.

[0104] Fig.82 It is a diagram showing an example of the bitstream including the coding mode information related to Embodiment 9.

[0105] Fig.83 It is a diagram showing the syntax example related to Embodiment 9.

[0106] Fig.84 It is a flowchart of the encoding process related to Embodiment 9.

[0107] Fig.85 It is a flowchart of the node encoding process related to Embodiment 9.

[0108] Fig.86 It is a flowchart of the decoding process related to Embodiment 9.

[0109] Fig.87 It is a flowchart of the node decoding process related to Embodiment 9.

[0110] Fig.88 It is a diagram showing an example of the tree structure related to Embodiment 10.

[0111] Fig.89 It is a diagram showing an example of the number of valid leaf nodes each branch has related to Embodiment 10.

[0112] Fig.90 It is a diagram showing an application example of the encoding method related to Embodiment 10.

[0113] Fig.91 It is a diagram showing an example of the dense branch region related to Embodiment 10.

[0114] Fig.92 It is a diagram showing an example of the dense three - dimensional point group related to Embodiment 10.

[0115] Fig.93 It is a diagram showing an example of the sparse three - dimensional point group related to Embodiment 10.

[0116] Fig.94 It is a flowchart of the encoding process related to Embodiment 10.

[0117] Fig.95 It is a flowchart of the decoding process related to Embodiment 10.

[0118] Fig.96 It is a flowchart of the encoding process related to Embodiment 10.

[0119] Fig.97 It is a flowchart of the decoding process related to Embodiment 10.

[0120] Fig.98 It is a flowchart of the encoding process related to Embodiment 10.

[0121] Fig.99 It is a flowchart of the decoding process related to Embodiment 10.

[0122] Fig.100 It is a flowchart showing the separation process of three - dimensional points related to Embodiment 10.

[0123] Fig.101 It is a diagram showing an example of the syntax related to Embodiment 10.

[0124] Fig.102 It is a diagram showing an example of the dense branch related to Embodiment 10.

[0125] Fig.103 It is a diagram showing an example of the sparse branch related to Embodiment 10.

[0126] Fig.104 It is a flowchart of the encoding process of a modification example of Embodiment 10.

[0127] Fig.105 It is a flowchart of the decoding process of a modification example of Embodiment 10.

[0128] Fig.106 It is a flowchart of the separation process of three-dimensional points of a modification example of Embodiment 10.

[0129] Fig.107 It is a diagram showing a syntactic example of a modification example of Embodiment 10.

[0130] Fig.108 It is a flowchart of the encoding process of Embodiment 10.

[0131] Fig.109 It is a flowchart of the decoding process of Embodiment 10. Detailed implementation manners

[0132] A three-dimensional data encoding method according to an aspect of the present disclosure generates an N-ary tree structure (where N is an integer of 2 or more) of a plurality of three-dimensional points included in three-dimensional data; encodes a first branch having a first node included in a first layer as a root node through a first encoding process to generate first encoded data, where the first layer is one of a plurality of layers included in the N-ary tree structure; encodes a second branch having a second node different from the first node included in the first layer as a root node through a second encoding process different from the first encoding process to generate second encoded data; and generates a bitstream including the first encoded data and the second encoded data.

[0133] Thus, this three-dimensional data encoding method can apply an encoding process suitable for each branch included in the N-ary tree structure, so the encoding efficiency can be improved.

[0134] For example, it may be that the number of three-dimensional points included in the first branch is less than a preset threshold; and the number of three-dimensional points included in the second branch is more than the threshold.

[0135] For example, it may be that the first encoded data includes first information of a first N-ary tree structure representing a plurality of first three-dimensional points included in the first branch in a first manner; and the second encoded data includes second information of a second N-ary tree structure representing a plurality of second three-dimensional points included in the second branch in a second manner.

[0136] Thus, this three-dimensional data encoding method can apply an encoding method suitable for each branch included in the N-ary tree structure, so the encoding efficiency can be improved.

[0137] For example, it may also be that the first information includes three-dimensional point information corresponding to each of the multiple first three-dimensional points; each piece of the three-dimensional point information includes an index corresponding to each of the multiple layers in the first N-ary tree structure; each index indicates the sub-block to which the corresponding first three-dimensional point belongs among the N sub-blocks belonging to the corresponding layer; the second information includes a plurality of 1-bit information, and the plurality of 1-bit information corresponds to each of the multiple sub-blocks belonging to the multiple layers in the second N-ary tree structure, indicating whether there is a three-dimensional point in the corresponding sub-block.

[0138] For example, it may also be that the quantization parameter used in the second encoding process is different from the quantization parameter used in the first encoding process.

[0139] For example, it may also be that in the encoding of the first branch, the tree structure including the tree structure from the root node of the N-ary tree structure to the first node and the first branch is encoded by the first encoding process; in the encoding of the second branch, the tree structure including the tree structure from the root node of the N-ary tree structure to the second node and the second branch is encoded by the second encoding process.

[0140] For example, it may also be that the first encoded data includes the encoded data of the first branch and the third information indicating the position of the first node in the N-ary tree structure; the second encoded data includes the encoded data of the second branch and the fourth information indicating the position of the second node in the N-ary tree structure.

[0141] For example, it may also be that the third information includes information indicating the first layer and information indicating which node among the nodes included in the first layer the first node is; the fourth information includes information indicating the first layer and information indicating which node among the nodes included in the first layer the second node is.

[0142] For example, it may also be that the first encoded data includes information indicating the number of three-dimensional points included in the first branch; the second encoded data includes information indicating the number of three-dimensional points included in the second branch.

[0143] In addition, regarding a three-dimensional data decoding method according to an aspect of the present disclosure, a first encoded data and a second encoded data are obtained from a bitstream. The first encoded data is data obtained by encoding a first branch having a root node as a first node included in a first layer among a plurality of layers included in an N-ary tree structure (where N is an integer of 2 or more) of a plurality of three-dimensional points. The second encoded data is data obtained by encoding a second branch having a root node as a second node different from the first node included in the first layer. The first encoded data is decoded through a first decoding process to generate first decoded data of the first branch. The second encoded data is decoded through a second decoding process different from the first decoding process to generate second decoded data of the second branch. The plurality of three-dimensional points are restored using the first decoded data and the second decoded data.

[0144] Accordingly, this three-dimensional data decoding method can decode a bitstream with improved encoding efficiency.

[0145] For example, it may be that the number of three-dimensional points included in the first branch is less than a preset threshold; the number of three-dimensional points included in the second branch is more than the threshold.

[0146] For example, it may be that the first encoded data includes first information of a first N-ary tree structure representing a plurality of first three-dimensional points included in the first branch in a first manner; the second encoded data includes second information of a second N-ary tree structure representing a plurality of second three-dimensional points included in the second branch in a second manner.

[0147] For example, it may be that the first information includes three-dimensional point information corresponding to each of the plurality of first three-dimensional points; each of the three-dimensional point information includes an index corresponding to each of a plurality of layers in the first N-ary tree structure; each index indicates a sub-block to which the corresponding first three-dimensional point belongs among N sub-blocks belonging to the corresponding layer; the second information includes a plurality of 1-bit information, and the plurality of 1-bit information corresponds to each of a plurality of sub-blocks of a plurality of layers in the second N-ary tree structure, and indicates whether there is a three-dimensional point in the corresponding sub-block.

[0148] For example, it may be that a quantization parameter used in the second decoding process is different from a quantization parameter used in the first decoding process.

[0149] For example, it can also be that in the decoding of the first branch, the tree structure including the root node of the N-ary tree structure to the first node and the tree structure of the first branch are decoded through the first decoding process; in the decoding of the second branch, the tree structure including the root node of the N-ary tree structure to the second node and the tree structure of the second branch are decoded through the second decoding process.

[0150] For example, it can also be that the first encoded data includes the encoded data of the first branch and the third information indicating the position of the first node in the N-ary tree structure; the second encoded data includes the encoded data of the second branch and the fourth information indicating the position of the second node in the N-ary tree structure.

[0151] For example, it can also be that the third information includes the information indicating the first layer and the information indicating which node among the nodes included in the first layer the first node is; the fourth information includes the information indicating the first layer and the information indicating which node among the nodes included in the first layer the second node is.

[0152] For example, it can also be that the first encoded data includes the information indicating the number of three-dimensional points included in the first branch; the second encoded data includes the information indicating the number of three-dimensional points included in the second branch.

[0153] A three-dimensional data encoding device according to an aspect of the present disclosure includes a processor and a memory; the processor uses the memory to perform the following processes: generating an N-ary tree structure (N is an integer of 2 or more) of a plurality of three-dimensional points included in the three-dimensional data; encoding the first branch having the first node included in the first layer as the root node through the first encoding process to generate the first encoded data, where the first layer is one of the plurality of layers included in the N-ary tree structure; encoding the second branch having the second node different from the first node included in the first layer as the root node through a second encoding process different from the first encoding process to generate the second encoded data; generating a bitstream including the first encoded data and the second encoded data.

[0154] Thereby, since the three-dimensional data encoding device can apply an encoding process suitable for each branch included in the N-ary tree structure, the encoding efficiency can be improved.

[0155] A three-dimensional data decoding device according to an aspect of the present disclosure includes a processor and a memory; the processor performs the following processing using the memory: obtaining first encoded data and second encoded data from a bitstream, where the first encoded data is data obtained by encoding a first branch having a first node included in a first layer, which is one of a plurality of layers included in an N-ary tree structure (N is an integer of 2 or more) of a plurality of three-dimensional points, as a root node, and the second encoded data is data obtained by encoding a second branch having a second node different from the first node included in the first layer as a root node; decoding the first encoded data through a first decoding process to generate first decoded data of the first branch; decoding the second encoded data through a second decoding process different from the first decoding process to generate second decoded data of the second branch; and restoring the plurality of three-dimensional points using the first decoded data and the second decoded data.

[0156] Accordingly, the three-dimensional data decoding device can decode a bitstream with improved encoding efficiency.

[0157] In addition, these general or specific aspects can be implemented by a system, a method, an integrated circuit, a computer program, or a recording medium such as a computer-readable CD-ROM, and can be implemented by any combination of a system, a method, an integrated circuit, a computer program, and a recording medium.

[0158] Hereinafter, embodiments will be specifically described with reference to the drawings. In addition, all the embodiments to be described below are specific examples showing an aspect of the present disclosure. The numerical values, shapes, materials, constituent elements, arrangement positions and connection forms of the constituent elements, steps, order of steps, etc. shown in the following embodiments are all examples, and the gist thereof is not to limit the present disclosure. In addition, among the constituent elements of the following embodiments, those not described in the technical solution showing the most general concept are described as optional constituent elements.

[0159] (Embodiment 1)

[0160] First, a data structure of the encoded three-dimensional data (hereinafter also referred to as encoded data) according to the present embodiment will be described. Figure 1 The configuration of the encoded three-dimensional data according to the present embodiment is shown.

[0161] In this embodiment, a three-dimensional space is divided into spaces (SPC) corresponding to pictures in the encoding of moving images, and three-dimensional data is encoded in units of space. The space is further divided into volumes (VLM) corresponding to macroblocks and the like in moving image encoding, and prediction and transformation are performed in units of VLM. A volume includes a plurality of voxels (VXL) which are the smallest units corresponding to position coordinates. In addition, prediction means, similar to the prediction performed in a two-dimensional image, referring to other processing units, generating predicted three-dimensional data similar to the processing unit to be processed, and encoding the difference between the predicted three-dimensional data and the processing unit to be processed. And this prediction includes not only spatial prediction referring to other prediction units at the same time but also temporal prediction referring to prediction units at different times.

[0162] For example, when a three-dimensional data encoding device (hereinafter also referred to as an encoding device) encodes a three-dimensional space represented by point cloud data such as a point cloud, it encodes each point of the point cloud or a plurality of points included in a voxel together according to the size of the voxel. If the voxel is subdivided, the three-dimensional shape of the point cloud can be represented with high precision, and if the size of the voxel is increased, the three-dimensional shape of the point cloud can be represented roughly.

[0163] In addition, although the case where the three-dimensional data is a point cloud is described as an example below, the three-dimensional data is not limited to the point cloud and can be three-dimensional data in any form.

[0164] Also, hierarchical voxels can be used. In this case, in the nth level, it is possible to sequentially show whether there are sampling points in the levels below the (n - 1)th level (the lower layer of the nth level). For example, when only decoding the nth level, when there are sampling points in the levels below the (n - 1)th level, it is possible to perform decoding by regarding that there are sampling points at the center of the voxels in the nth level.

[0165] And the encoding device obtains point cloud data through a distance sensor, a stereo camera, a monocular camera, a gyroscope, or an inertial sensor, etc.

[0166] Regarding the space, similar to the encoding of moving images, it is at least classified into any one of the following three prediction structures: an intra-frame space (I-SPC) that can be decoded independently, a prediction space (P-SPC) that can only be referred to unidirectionally, and a bidirectional space (B-SPC) that can be referred to bidirectionally. And the space has two types of time information: a decoding time and a display time.

[0167] And, as Figure 1As shown, as a processing unit including multiple spaces, there is a GOS (Group Of Space) which is a random access unit. Moreover, as a processing unit including multiple GOSs, there is a World Space (WLD).

[0168] The space area occupied by the world space is corresponded to the absolute position on the earth through GPS or latitude and longitude information, etc. This position information is stored as meta information. In addition, the meta information can be included in the encoded data or transmitted separately from the encoded data.

[0169] Moreover, within a GOS, all SPCs can be three-dimensionally adjacent, or there can be SPCs that are not three-dimensionally adjacent to other SPCs.

[0170] In addition, hereinafter, the processes such as encoding, decoding, or referring to the three-dimensional data included in processing units such as GOS, SPC, or VLM are also simply referred to as encoding, decoding, or referring to the processing unit. And the three-dimensional data included in the processing unit includes at least one group of spatial positions such as three-dimensional coordinates and characteristic values such as color information, for example.

[0171] Next, the prediction structure of SPCs in a GOS will be described. Multiple SPCs within the same GOS, or multiple VLMs within the same SPC, although occupying different spaces from each other, hold the same time information (decoding time and display time).

[0172] Moreover, within a GOS, the SPC that is the first in the decoding order is an I-SPC. And there are two types of GOSs in a GOS: a closed GOS and an open GOS. A closed GOS is a GOS that can decode all SPCs within the GOS when starting to decode from the first I-SPC. In an open GOS, within the GOS, a part of the SPCs whose display time is earlier than that of the first I-SPC refer to different GOSs and can only be decoded in that GOS.

[0173] In addition, in the encoded data such as map information, there is a case of decoding the WLD in the direction opposite to the encoding order. If there is a dependency between GOSs, it is difficult to perform reverse regeneration. Therefore, in this case, a closed GOS is basically adopted.

[0174] Moreover, a GOS has a layer structure in the height direction, and encoding or decoding is sequentially performed starting from the SPCs in the bottom layer.

[0175] Figure 2 An example of the prediction structure between SPCs belonging to the bottommost layer of a GOS is shown. Figure 3 An example of the prediction structure between layers is shown.

[0176] There is more than one I-SPC in the GOS. Although there are objects such as people, animals, cars, bicycles, traffic lights, or buildings that serve as land marks in the three-dimensional space, it is effective especially when encoding small-sized objects as I-SPCs. For example, when a three-dimensional data decoding device (hereinafter also referred to as a decoding device) decodes the GOS with a low processing amount or at high speed, it only decodes the I-SPCs in the GOS.

[0177] Moreover, the encoding device can switch the encoding interval or the appearance frequency of the I-SPCs according to the density of the objects in the WLD.

[0178] Moreover, in Figure 3 In the configuration shown, the encoding device or the decoding device performs encoding or decoding for multiple layers sequentially starting from the lower layer (layer 1). Accordingly, for example, for an automatically moving vehicle or the like, it is possible to give higher priority to the data near the ground with a large amount of information.

[0179] In addition, in the encoded data used in a drone or the like, in the GOS, encoding or decoding can be performed sequentially starting from the SPC of the upper layer in the height direction.

[0180] Moreover, the encoding device or the decoding device can also encode or decode multiple layers in such a way that the decoding device generally grasps the GOS and can gradually increase the resolution. For example, the encoding device or the decoding device can perform encoding or decoding in the order of layer 3, 8, 1, 9...

[0181] Next, a method for corresponding to static objects and dynamic objects will be described.

[0182] In the three-dimensional space, there are static objects or scenes such as buildings or roads (hereinafter collectively referred to as static objects), and dynamic objects such as vehicles or people (hereinafter referred to as dynamic objects). Detection of the objects can be performed separately by extracting feature points from the data of the point cloud, or the images captured by a stereo camera or the like. Here, an example of an encoding method for dynamic objects will be described.

[0183] The first method is a method of encoding without distinguishing between static objects and dynamic objects. The second method is a method of distinguishing between static objects and dynamic objects by identification information.

[0184] For example, the GOS is used as an identification unit. In this case, the GOS including the SPCs constituting the static object and the GOS including the SPCs constituting the dynamic object are distinguished in the encoded data or by identification information stored separately from the encoded data.

[0185] Alternatively, an SPC is used as an identification unit. In this case, only the SPCs that constitute the VLMs of the static object and the SPCs that include the VLMs that constitute the dynamic object are distinguished by the above-mentioned identification information.

[0186] Alternatively, a VLM or a VXL can be used as an identification unit. In this case, the VLMs or VXLs that include static objects and the VLMs or VXLs that include dynamic objects are distinguished by the above-mentioned identification information.

[0187] Moreover, the encoding device can encode a dynamic object as one or more VLMs or SPCs, and encode the VLMs or SPCs that include static objects and the SPCs that include dynamic objects as different GOSs. Moreover, when the size of the GOS becomes variable according to the size of the dynamic object, the encoding device stores the size of the GOS separately as meta-information.

[0188] Moreover, the encoding device encodes the static object and the dynamic object independently of each other, and for the world space constituted by the static object, the dynamic object can be overlapped. At this time, the dynamic object is composed of one or more SPCs, and each SPC corresponds to one or more SPCs of the static object that overlaps the SPC. In addition, the dynamic object may not be represented by an SPC, and may be represented by one or more VLMs or VXLs.

[0189] Moreover, the encoding device can encode the static object and the dynamic object as different streams.

[0190] Moreover, the encoding device can also generate a GOS that includes one or more SPCs that constitute a dynamic object. Moreover, the encoding device can set the GOS (GOS_M) that includes the dynamic object and the GOS of the static object corresponding to the spatial region of GOS_M to have the same size (occupy the same spatial region). In this way, overlapping processing can be performed in units of GOS.

[0191] The P-SPC or B-SPC that constitutes the dynamic object can also refer to the SPCs included in different encoded GOSs. The position of the dynamic object changes over time. In the case where the same dynamic object is encoded as GOSs at different times, the cross-GOS reference is effective from the viewpoint of compression ratio.

[0192] Moreover, the above-mentioned first method and second method can also be switched according to the use of the encoded data. For example, when the encoded three-dimensional data is applied as a map, since it is desired to be separated from the dynamic object, the encoding device adopts the second method. In addition, when the encoding device encodes the three-dimensional data of an event such as a concert or a sports event, if there is no need to separate the dynamic object, the first method is adopted.

[0193] Moreover, the decoding time and display time of GOS or SPC can be stored in the encoded data or as meta-information. Also, the time information of static objects can all be the same. In this case, the actual decoding time and display time can be determined by the decoding device. Alternatively, as the decoding time, different values can be assigned for each GOS or SPC, and as the display time, the same value can be assigned to all. Moreover, as shown in the decoder mode in video coding such as HEVC's HRD (Hypothetical Reference Decoder), the decoder has a buffer of a specified size. As long as the bitstream is read at a specified bit rate according to the decoding time, a model that will not be damaged and can be guaranteed to be decoded can be imported.

[0194] Next, the configuration of GOS in the world space will be described. The coordinates of the three-dimensional space in the world space are represented by three mutually orthogonal coordinate axes (x-axis, y-axis, z-axis). By setting a specified rule in the encoding order of GOS, GOS that are adjacent in space can be encoded continuously in the encoded data. For example, in the Figure 4 example shown, the GOS in the xz plane are encoded continuously. After the encoding of all GOS in one xz plane is completed, the value of the y-axis is updated. That is, as the encoding progresses, the world space extends in the y-axis direction. Also, the index number of GOS is set as the encoding order.

[0195] Here, the three-dimensional space of the world space corresponds one-to-one with GPS or geographical absolute coordinates such as latitude and longitude. Alternatively, the three-dimensional space can be represented by the relative position with respect to a pre-set reference position. The directions of the x-axis, y-axis, and z-axis of the three-dimensional space are represented as direction vectors determined based on latitude and longitude, etc., and this direction vector is stored together with the encoded data as meta-information.

[0196] Also, the size of GOS is set to be fixed, and the encoding device stores this size as meta-information. Also, the size of GOS can be switched, for example, according to whether it is in the city, indoors, or outdoors, etc. That is, the size of GOS can be switched according to the amount or nature of the object having the value as information. Alternatively, the encoding device can appropriately switch the size of GOS or the interval of I-SPC within GOS in the same world space according to the density of the object, etc. For example, the encoding device sets the size of GOS to be smaller and the interval of I-SPC within GOS to be shorter when the density of the object is higher.

[0197] In Figure 5In the example, in the region from the 3rd to the 10th GOS, since the density of the objects is high, in order to achieve random access with fine granularity, the GOS is subdivided. And the 7th to 10th GOSs are respectively located on the back of the 3rd to 6th GOSs.

[0198] Next, the configuration and operation process of the three-dimensional data encoding device according to this embodiment will be described. Figure 6 FIG. is a block diagram of the three-dimensional data encoding device 100 according to this embodiment. Figure 7 FIG. is a flowchart showing an operation example of the three-dimensional data encoding device 100.

[0199] Figure 6 The three-dimensional data encoding device 100 shown generates encoded three-dimensional data 112 by encoding three-dimensional data 111. The three-dimensional data encoding device 100 includes: an acquisition unit 101, an encoding region determination unit 102, a division unit 103, and an encoding unit 104.

[0200] As Figure 7 shown, first, the acquisition unit 101 acquires three-dimensional data 111 as point cloud data (S101).

[0201] Next, the encoding region determination unit 102 determines the region to be encoded from the spatial region corresponding to the acquired point cloud data (S102). For example, the encoding region determination unit 102 determines the spatial region around the position as the region to be encoded according to the position of the user or the vehicle.

[0202] Next, the division unit 103 divides the point cloud data included in the region to be encoded into respective processing units. Here, the processing units are the above-mentioned GOS and SPC, etc. And the region to be encoded corresponds to the above-mentioned world space, for example. Specifically, the division unit 103 divides the point cloud data into processing units according to the preset size of the GOS, the presence or size of dynamic objects (S103). And the division unit 103 determines the start position of the SPC that becomes the beginning in the encoding order in each GOS.

[0203] Next, the encoding unit 104 generates encoded three-dimensional data 112 by sequentially encoding a plurality of SPCs in each GOS (S104).

[0204] In addition, here, after dividing the region to be encoded into GOS and SPC, although an example of encoding each GOS is shown, the order of processing is not limited to the above. For example, after determining the configuration of one GOS, it is possible to encode the GOS, and then determine the configuration of the GOS, etc. in this order.

[0205] In this way, the three-dimensional data encoding device 100 generates the encoded three-dimensional data 112 by encoding the three-dimensional data 111. Specifically, the three-dimensional data encoding device 100 divides the three-dimensional data into random access units, that is, divides it into first processing units (GOS) corresponding to three-dimensional coordinates respectively, divides the first processing units (GOS) into a plurality of second processing units (SPC), and divides the second processing units (SPC) into a plurality of third processing units (VLM). Moreover, the third processing unit (VLM) includes one or more voxels (VXL), and the voxel (VXL) is the smallest unit corresponding to the position information.

[0206] Next, the three-dimensional data encoding device 100 generates the encoded three-dimensional data 112 by encoding each of the plurality of first processing units (GOS). Specifically, the three-dimensional data encoding device 100 encodes each of the plurality of second processing units (SPC) in each first processing unit (GOS). Moreover, the three-dimensional data encoding device 100 encodes each of the plurality of third processing units (VLM) in each second processing unit (SPC).

[0207] For example, when the first processing unit (GOS) of the processing object is a closed GOS, the three-dimensional data encoding device 100 encodes the second processing unit (SPC) of the processing object included in the first processing unit (GOS) of the processing object with reference to other second processing units (SPC) included in the first processing unit (GOS) of the processing object. That is, the three-dimensional data encoding device 100 does not refer to the second processing units (SPC) included in the first processing units (GOS) different from the first processing unit (GOS) of the processing object.

[0208] Moreover, when the first processing unit (GOS) of the processing object is an open GOS, the three-dimensional data encoding device 100 encodes the second processing unit (SPC) of the processing object included in the first processing unit (GOS) of the processing object with reference to other second processing units (SPC) included in the first processing unit (GOS) of the processing object or the second processing units (SPC) included in the first processing units (GOS) different from the first processing unit (GOS) of the processing object.

[0209] Moreover, the three-dimensional data encoding device 100 selects one of the first type (I-SPC) that never refers to other second processing units (SPC), the second type (P-SPC) that refers to one other second processing unit (SPC), and the third type that refers to two other second processing units (SPC) as the type of the second processing unit (SPC) of the processing object, and encodes the second processing unit (SPC) of the processing object according to the selected type.

[0210] Next, the configuration and operation process of the three-dimensional data decoding device according to this embodiment will be described. Figure 8 It is a block diagram of the three-dimensional data decoding device 200 according to this embodiment. Fig. 9 It is a flowchart showing an operation example of the three-dimensional data decoding device 200.

[0211] Figure 8 The three-dimensional data decoding device 200 shown generates decoded three-dimensional data 212 by decoding the encoded three-dimensional data 211. Here, the encoded three-dimensional data 211 is, for example, the encoded three-dimensional data 112 generated by the three-dimensional data encoding device 100. The three-dimensional data decoding device 200 includes: an acquisition unit 201, a decoding start GOS determination unit 202, a decoding SPC determination unit 203, and a decoding unit 204.

[0212] First, the acquisition unit 201 acquires the encoded three-dimensional data 211 (S201). Next, the decoding start GOS determination unit 202 determines the GOS to be decoded (S202). Specifically, the decoding start GOS determination unit 202 refers to the meta information in the encoded three-dimensional data 211 or stored separately from the encoded three-dimensional data, and determines the GOS including the spatial position, object, or SPC corresponding to the time at which decoding starts as the GOS to be decoded.

[0213] Next, the decoding SPC determination unit 203 determines the type (I, P, B) of the SPC to be decoded within the GOS (S203). For example, the decoding SPC determination unit 203 determines (1) whether to decode only I-SPC, (2) whether to decode I-SPC and P-SPC, (3) whether to decode all types. In addition, when the type of SPC to be decoded, such as all SPCs, is specified in advance, this step may not be performed.

[0214] Next, the decoding unit 204 acquires the SPC that is the first in the decoding order (the same as the encoding order) within the GOS, the address position where it starts in the encoded three-dimensional data 211, acquires the encoded data of the first SPC from this address position, and sequentially decodes each SPC from this first SPC (S204). And the above address position is stored in meta information or the like.

[0215] In this way, the three-dimensional data decoding device 200 decodes the decoded three-dimensional data 212. Specifically, the three-dimensional data decoding device 200 generates the decoded three-dimensional data 212 of the first processing unit (GOS) as a random access unit by decoding each of the encoded three-dimensional data 211 of the first processing unit (GOS) corresponding to the three-dimensional coordinates. More specifically, the three-dimensional data decoding device 200 decodes each of the multiple second processing units (SPCs) in each of the first processing units (GOSs). Further, the three-dimensional data decoding device 200 decodes each of the multiple third processing units (VLMs) in each of the second processing units (SPCs).

[0216] The meta information for random access will be described below. This meta information is generated by the three-dimensional data encoding device 100 and is included in the encoded three-dimensional data 112 (211).

[0217] In the random access of conventional two-dimensional moving images, decoding starts from the first frame of the random access unit near the specified time. However, in the world space, random access is also envisioned for (coordinates, objects, etc.) in addition to time.

[0218] Therefore, in order to achieve random access to at least the three elements of coordinates, objects, and time, a table in which the index numbers of each element are associated with the GOS is prepared. Moreover, the index number of the GOS is associated with the address of the I-SPC that is the start of the GOS. Fig.10 An example of the table included in the meta information is shown. Additionally, it is not necessary to use Fig.10 all of the shown tables, and at least one table can be used.

[0219] Hereinafter, as an example, random access starting from coordinates will be described. When accessing the coordinates (x2, y2, z2), first referring to the coordinate-GOS table, it can be known that the location with the coordinates (x2, y2, z2) is included in the second GOS. Then, referring to the GOS address table, since it can be known that the address of the I-SPC at the start of the second GOS is addr(2), the decoding unit 204 obtains data from this address and starts decoding.

[0220] In addition, the address can be an address in the logical format or a physical address of an HDD or a memory. Also, information for identifying a file segment can be used instead of the address. For example, a file segment is a unit obtained by segmenting one or more GOSs, etc.

[0221] Also, in the case where the object spans multiple GOSs, the GOSs to which the multiple objects belong can also be shown in the object GOS table. If the multiple GOSs are closed GOSs, the encoding device and the decoding device can perform encoding or decoding in parallel. Additionally, if the multiple GOSs are open GOSs, by cross-referencing each other among the multiple GOSs, the compression efficiency can be further improved.

[0222] Examples of the object include a person, an animal, a car, a bicycle, a traffic signal, or a building serving as a land mark, etc. For example, when the three-dimensional data encoding device 100 encodes in the world space, it extracts the feature points unique to the object from a three-dimensional point cloud or the like, detects the object based on the feature points, and can set the detected object as a random access point.

[0223] In this way, the three-dimensional data encoding device 100 generates the first information, which shows multiple first processing units (GOSs) and the three-dimensional coordinates corresponding to each of the multiple first processing units (GOSs). And the encoded three-dimensional data 112(211) includes this first information. And the first information further shows at least one of the object, time, and data storage destination corresponding to each of the multiple first processing units (GOSs).

[0224] The three-dimensional data decoding device 200 obtains the first information from the encoded three-dimensional data 211, uses the first information to determine the encoded three-dimensional data 211 of the first processing unit corresponding to the specified three-dimensional coordinates, object, or time, and decodes the encoded three-dimensional data 211.

[0225] Examples of other meta-information will be described below. In addition to the meta-information for random access, the three-dimensional data encoding device 100 can also generate and store the following meta-information. And the three-dimensional data decoding device 200 can also use this meta-information during decoding.

[0226] In the case of using the three-dimensional data as map information, etc., a profile is specified according to the use, and the information showing the profile can be included in the meta-information. For example, a profile for urban areas or suburbs is specified, or a profile for flying objects is specified, and the maximum or minimum size of the world space, SPC, or VLM is defined respectively. For example, in the profile for urban areas, more detailed information is required, so the minimum size of the VLM is set smaller.

[0227] The meta information may also include a tag value indicating the type of the object. This tag value corresponds to the VLM, SPC, or GOS constituting the object. The tag value can be set according to the type of the object, etc. For example, the tag value "0" represents "person", the tag value "1" represents "car", and the tag value "2" represents "traffic signal". Alternatively, in a case where it is difficult to determine or unnecessary to determine the type of the object, a tag value indicating properties such as size, or whether it is a dynamic object or a static object can also be used.

[0228] Furthermore, the meta information may also include information indicating the range of the spatial region occupied by the world space.

[0229] Furthermore, the meta information may store the size of the SPC or VXL as the entire stream of encoded data or as header information shared by multiple SPCs such as SPCs within the GOS.

[0230] Furthermore, the meta information may also include identification information such as a distance sensor or a camera used in the generation of the point cloud, or may include information indicating the position accuracy of the point group within the point cloud.

[0231] Furthermore, the meta information may include information indicating whether the world space is composed only of static objects or contains dynamic objects.

[0232] A modification example of the present embodiment will be described below.

[0233] The encoding device or the decoding device may encode or decode two or more different SPCs or GOSs in parallel. The GOSs to be encoded or decoded in parallel can be determined based on the meta information indicating the spatial position of the GOSs, etc.

[0234] In a case where the three-dimensional data is used as a spatial map when a vehicle or a flying object moves, or in a case where such a spatial map is generated, etc., the encoding device or the decoding device may encode or decode the GOS or SPC included in the space determined based on GPS, path information, or zoom ratio, etc.

[0235] Furthermore, the decoding device may also start decoding sequentially from the space close to its own position or the walking path. The encoding device or the decoding device may also perform encoding or decoding by making the priority of the space far from its own position or the walking path lower than that of the close space. Here, reducing the priority means reducing the processing order, reducing the resolution (post-screening processing), or reducing the image quality (increasing the encoding efficiency. For example, increasing the quantization step size), etc.

[0236] Furthermore, when decoding the encoded data hierarchically encoded within the space, the decoding device may also decode only the lower hierarchy.

[0237] Also, the decoding device can also start decoding from the lower level according to the zoom ratio or usage of the map.

[0238] Also, in applications such as self-position estimation or object recognition performed during the automatic driving of a vehicle or a robot, the encoding device or the decoding device can also reduce the resolution of areas outside the area within a specified height from the road surface (the area to be recognized) for encoding or decoding.

[0239] Also, the encoding device can also encode the point clouds representing the spatial shapes of the indoor and outdoor spaces independently. For example, by separating the GOS representing the indoor (indoor GOS) from the GOS representing the outdoor (outdoor GOS), the decoding device can select the GOS to be decoded according to the viewpoint position when using the encoded data.

[0240] Also, the encoding device can make the indoor GOS and the outdoor GOS with close coordinates adjacent in the encoding stream for encoding. For example, the encoding device corresponds their identifiers and stores the information indicating that the corresponding identifiers are established in the encoding stream or in the meta-information stored separately. Accordingly, the decoding device can identify the indoor GOS and the outdoor GOS with close coordinates by referring to the information in the meta-information.

[0241] Also, the encoding device can also switch the size of the GOS or SPC between the indoor GOS and the outdoor GOS. For example, the encoding device sets the size of the GOS to be smaller indoors than outdoors. Also, the encoding device can also change the accuracy when extracting feature points from the point cloud or the accuracy of object detection, etc., between the indoor GOS and the outdoor GOS.

[0242] Also, the encoding device can attach information for the decoding device to distinguish and display dynamic objects from static objects to the encoded data. Accordingly, the decoding device can combine the dynamic objects with a red frame or explanatory text, etc., for display. In addition, the decoding device can also represent only with a red frame or explanatory text instead of the dynamic objects. And the decoding device can represent more detailed object categories. For example, a car can use a red frame and a person can use a yellow frame.

[0243] Also, the encoding device or the decoding device can determine whether to encode or decode the dynamic objects and the static objects as different SPCs or GOSs according to the appearance frequency of the dynamic objects, or the ratio of the static objects to the dynamic objects, etc. For example, when the appearance frequency or ratio of the dynamic objects exceeds the threshold, the SPC or GOS in which the dynamic objects and the static objects are mixed is allowed, and when the appearance frequency or ratio of the dynamic objects does not exceed the threshold, the SPC or GOS in which the dynamic objects and the static objects are mixed is not allowed.

[0244] When the dynamic object is detected not from the point cloud but from the two-dimensional image information of the camera, the encoding device can separately obtain the information (such as a frame or text) for identifying the detection result and the object position, and encode these information as part of the three-dimensional encoded data. In this case, the decoding device overlays and displays the auxiliary information (frame or text) representing the dynamic object on the decoding result of the static object.

[0245] Moreover, the encoding device can change the density of VXL or VLM according to the complexity of the shape of the static object, etc. For example, the encoding device sets VXL or VLM to be denser when the shape of the static object is more complex. Also, the encoding device can determine the quantization step size, etc. when quantifying the spatial position or color information according to the density of VXL or VLM. For example, the encoding device sets the quantization step size to be smaller when VXL or VLM is denser.

[0246] As described above, the encoding device or decoding device according to this embodiment performs spatial encoding or decoding in a spatial unit having coordinate information.

[0247] Moreover, the encoding device and the decoding device perform encoding or decoding in a volume unit within the space. The volume includes the voxel which is the smallest unit corresponding to the position information.

[0248] Moreover, the encoding device and the decoding device establish correspondence between any elements by using a table in which each element of the spatial information including coordinates, objects, and time, etc. is corresponded to the GOP, or a table corresponding between each element, and perform encoding or decoding. And the decoding device determines the coordinates by using the value of the selected element, determines the volume, voxel or space according to the coordinates, and decodes the space including the volume or voxel, or the determined space.

[0249] Moreover, the encoding device determines the volume, voxel or space that can be selected by the element through feature point extraction or object recognition, and encodes it as a volume, voxel or space that can be randomly accessed.

[0250] The space is divided into three types, namely: I-SPC that can be encoded or decoded by the space alone, P-SPC that encodes or decodes with reference to any one processed space, and B-SPC that encodes or decodes with reference to any two processed spaces.

[0251] One or more volumes correspond to static objects or dynamic objects. The space containing the static object and the space containing the dynamic object are encoded or decoded as different GOSs respectively. That is, the SPC containing the static object and the SPC containing the dynamic object are assigned to different GOSs.

[0252] Dynamic objects are encoded or decoded on a per-object basis, corresponding to more than one space that contains only static objects. That is, multiple dynamic objects are encoded separately, and the encoded data of the multiple dynamic objects corresponds to the SPC that contains only static objects.

[0253] The encoding device and the decoding device improve the priority of the I-SPC in the GOS to perform encoding or decoding. For example, the encoding device encodes in a manner that reduces the degradation of the I-SPC (after decoding, the original three-dimensional data can be reproduced more faithfully). And, the decoding device decodes only the I-SPC, for example.

[0254] The encoding device can change the frequency of using the I-SPC according to the density or value (quantity) of the objects in the world space to perform encoding. That is, the encoding device changes the frequency of selecting the I-SPC according to the quantity or density of the objects included in the three-dimensional data. For example, the encoding device increases the usage frequency of the I space when the density of the objects in the world space is greater.

[0255] Moreover, the encoding device sets random access points in units of GOS, and stores the information indicating the spatial region corresponding to the GOS in the header information.

[0256] The encoding device uses a default value as the spatial size of the GOS, for example. In addition, the encoding device can also change the size of the GOS according to the value (quantity) or density of the objects or dynamic objects. For example, the encoding device sets the spatial size of the GOS to be smaller when the objects or dynamic objects are denser or the quantity is larger.

[0257] Moreover, the space or volume includes a feature point group derived using information obtained by sensors such as a depth sensor, a gyroscope, or a camera. The coordinates of the feature points are set as the center positions of the voxels. And, through the subdivision of the voxels, high-precision position information can be achieved.

[0258] The feature point group is derived using multiple pictures. The multiple pictures have at least the following two types of time information, namely: actual time information, and the same time information in the multiple pictures corresponding to the space (for example, the encoding time for rate control, etc.).

[0259] Encoding or decoding is performed in units of GOS that includes more than one space.

[0260] The encoding device and the decoding device predict the P space or B space in the GOS to be processed with reference to the space in the processed GOS.

[0261] Alternatively, the encoding device and the decoding device do not refer to different GOSs, and use the processed space within the GOS of the object to be processed to predict the P space or B space within the GOS of the object to be processed.

[0262] Furthermore, the encoding device and the decoding device send or receive an encoded stream in units of a world space including one or more GOSs.

[0263] Moreover, the GOS has a layer structure at least in one direction within the world space, and the encoding device and the decoding device perform encoding or decoding starting from the lower layer. For example, a GOS that can be randomly accessed belongs to the lowest layer. A GOS belonging to an upper layer only refers to GOSs belonging to layers below the same layer. That is, the GOS is spatially divided in a predetermined direction and includes multiple layers each having one or more SPCs. The encoding device and the decoding device perform encoding or decoding for each SPC by referring to SPCs included in the same layer as or a lower layer than the SPC.

[0264] Also, the encoding device and the decoding device continuously perform encoding or decoding on GOSs within a world space unit including multiple GOSs. The encoding device and the decoding device write or read information indicating the order (direction) of encoding or decoding as metadata. That is, the encoded data includes information indicating the encoding order of multiple GOSs.

[0265] In addition, the encoding device and the decoding device perform encoding or decoding on two or more different spaces or GOSs in parallel.

[0266] Moreover, the encoding device and the decoding device encode or decode the spatial information (coordinates, size, etc.) of a space or GOS.

[0267] Furthermore, the encoding device and the decoding device encode or decode a space or GOS included in a specific space determined according to external information such as GPS, path information, or magnification related to its own position or / and region size.

[0268] The encoding device or the decoding device performs encoding or decoding by making the priority of a space far from its own position lower than that of a space close to its own position.

[0269] The encoding device sets one direction in the world space according to magnification or use, and encodes a GOS having a layer structure in that direction. And the decoding device preferentially decodes from the lower layer for a GOS having a layer structure in one direction of the world space set according to magnification or use.

[0270] The encoding device changes the extraction of feature points, the accuracy of object recognition, or the size of the spatial region, etc. included in the indoor and outdoor spaces. However, the encoding device and the decoding device encode or decode by making the indoor GOS and the outdoor GOS with close coordinates adjacent to each other in the world space, and also encode or decode while corresponding these identifiers to each other.

[0271] (Embodiment 2)

[0272] When using the encoded data of the point cloud for an actual device or service, in order to suppress the network bandwidth, it is desired to transmit and receive the required information according to the usage. However, such a function does not exist in the encoding structure of the three-dimensional data so far, and thus there is no encoding method corresponding thereto.

[0273] In the present embodiment, a three-dimensional data encoding method and a three-dimensional data encoding device for providing a function of transmitting and receiving the required information according to the usage in the encoded data of the three-dimensional point cloud, and a three-dimensional data decoding method and a three-dimensional data decoding device for decoding the encoded data will be described.

[0274] A voxel (VXL) having a feature amount equal to or more than a certain level is defined as a feature voxel (FVXL), and a world space (WLD) composed of FVXL is defined as a sparse world space (SWLD). Fig.11 A configuration example of the sparse world space and the world space is shown. In the SWLD, there are included: FGOS, which is a GOS composed of FVXL; FSPC, which is an SPC composed of FVXL; and FVLM, which is a VLM composed of FVXL. The data structure and the prediction structure of FGOS, FSPC, and FVLM may be the same as those of GOS, SPC, and VLM.

[0275] The feature amount refers to a feature amount representing the three-dimensional position information of the VXL or the visible light information at the VXL position, and in particular, a feature amount capable of detecting more features such as the corners and edges of a three-dimensional object. Specifically, although the feature amount is the three-dimensional feature amount or the visible light feature amount described below, in addition, as long as it is a feature amount representing the position, brightness, or color information of the VXL, it may be any feature amount.

[0276] As the three-dimensional feature amount, a SHOT feature amount (Signature of Histograms of OrienTations), a PFH feature amount (Point Feature Histograms), or a PPF feature amount (Point Pair Feature) is adopted.

[0277] The SHOT feature quantity is obtained by dividing the periphery of the VXL, calculating the inner product of the reference point and the normal vector of the divided region, and performing histogramming. This SHOT feature quantity has the characteristics of high dimensionality and high feature expressiveness.

[0278] The PFH feature quantity is obtained by selecting multiple two-point groups near the VXL, calculating the normal vector, etc. based on these two points, and performing histogramming. Since this PFH feature quantity is a histogram feature, it is robust against a small amount of interference and has the characteristic of high feature expressiveness.

[0279] The PPF feature quantity is a feature quantity calculated using the normal vector, etc. according to two VXLs. In this PPF feature quantity, since all VXLs are used, it is robust against occlusion.

[0280] Moreover, as feature quantities of visible light, SIFT (Scale-Invariant Feature Transform), SURF (Speeded Up Robust Features), or HOG (Histogram of Oriented Gradients), etc. that adopt information such as the luminance gradient information of an image can be used.

[0281] The SWLD is generated by calculating the above feature quantities from each VXL of the WLD and extracting the FVXL. Here, the SWLD can be updated each time the WLD is updated, or it can be updated periodically after a certain period of time regardless of the update timing of the WLD.

[0282] The SWLD can be generated for each feature quantity. For example, as shown by the SWLD1 based on the SHOT feature quantity and the SWLD2 based on the SIFT feature quantity, the SWLD can be generated separately for each feature quantity, and the SWLD can be distinguished and used according to the application. Also, the feature quantities of each calculated FVXL can be held as feature quantity information in each FVXL.

[0283] Next, the method of using the sparse world space (SWLD) will be described. Since the SWLD only contains feature voxels (FVXL), generally, the data size is smaller compared to the WLD that includes all VXLs.

[0284] In an application that uses feature quantities to achieve a certain purpose, by using the information of SWLD instead of WLD, the read time from the hard disk can be suppressed, and the bandwidth and transmission time during network transmission can be suppressed. For example, as map information, WLD and SWLD are stored in the server in advance, and by switching the transmitted map information to WLD or SWLD according to the demand from the client, the network bandwidth and transmission time can be suppressed. The following shows specific examples.

[0285] Fig.12 and Fig.13 shows examples of the use of SWLD and WLD. As Fig.12 shown, when the client 1 as a vehicle-mounted device needs map information for its own position judgment, the client 1 sends a request for obtaining map data for its own position estimation to the server (S301). The server sends the SWLD to the client 1 according to this acquisition requirement (S302). The client 1 uses the received SWLD to judge its own position (S303). At this time, the client 1 obtains VXL information around the client 1 by various methods such as a distance sensor such as a rangefinder, a stereo camera, or a combination of multiple monocular cameras, and estimates its own position information based on the obtained VXL information and SWLD. Here, the own position information includes the three-dimensional position information and the orientation of the client 1.

[0286] As Fig.13 shown, when the client 2 as a vehicle-mounted device needs map information for map drawing such as a three-dimensional map, the client 2 sends a request for obtaining map data for map drawing to the server (S311). The server sends the WLD to the client 2 according to this acquisition requirement (S312). The client 2 uses the received WLD to perform map drawing (S313). At this time, the client 2, for example, uses an image taken by its own visible light camera and the WLD obtained from the server to create a conceptual image, and depicts the created image on a screen such as a car navigation.

[0287] As shown above, the server sends the SWLD to the client in applications that mainly require the feature quantities of each VXL for its own position estimation, and sends the WLD to the client when detailed VXL information is required, such as map drawing. Accordingly, the map data can be efficiently transmitted and received.

[0288] In addition, the client can judge which of SWLD and WLD it needs and request the server to send SWLD or WLD. And the server can judge which of SWLD or WLD should be sent according to the condition of the client or the network.

[0289] Next, a method for switching the reception and transmission between the Sparse World Space (SWLD) and the World Space (WLD) will be described.

[0290] The reception of the WLD or the SWLD can be switched according to the network bandwidth. Fig.14 A working example in this case is shown. For example, when a low-speed network such as an LTE (Long Term Evolution) environment is used, when the client accesses the server via the low-speed network (S321), the client obtains the SWLD as map information from the server (S322). Additionally, when a high-speed network with sufficient network bandwidth such as a WiFi environment is used, the client accesses the server via the high-speed network (S323) and obtains the WLD from the server (S324). Accordingly, the client can obtain appropriate map information according to the network bandwidth of the client.

[0291] Specifically, the client receives the SWLD via LTE outdoors, and when entering indoors such as a facility, obtains the WLD via WiFi. Accordingly, the client can obtain more detailed map information of the interior.

[0292] In this way, the client can request the WLD or the SWLD from the server according to the frequency band of the network it uses. Alternatively, the client can send information indicating the frequency band of the network it uses to the server, and the server sends appropriate data (WLD or SWLD) to the client according to this information. Or, the server can determine the network bandwidth of the client and send appropriate data (WLD or SWLD) to the client.

[0293] Moreover, the reception of the WLD or the SWLD can be switched according to the moving speed. Fig.15 A working example in this case is shown. For example, when the client is moving at high speed (S331), the client receives the SWLD from the server (S332). Additionally, when the client is moving at low speed (S333), the client receives the WLD from the server (S334). Accordingly, the client can both suppress the network bandwidth and obtain map information according to the speed. Specifically, when the client is driving on a highway, by receiving the SWLD with less data volume, the map information can be updated at an appropriate speed approximately. Additionally, when the client is driving on an ordinary road, by receiving the WLD, more detailed map information can be obtained.

[0294] In this way, the client can request the WLD or SWLD from the server according to its own moving speed. Alternatively, the client can send the information indicating its own moving speed to the server, and the server sends appropriate data (WLD or SWLD) to the client according to this information. Alternatively, the server can determine the moving speed of the client and send appropriate data (WLD or SWLD) to the client.

[0295] Moreover, it can also be that the client first obtains the SWLD from the server and then obtains the WLD of the important areas therein. For example, when the client obtains map data, it first obtains the general map information with the SWLD, filters out the areas where there are many features such as buildings, signs, or people from it, and then obtains the WLD of the filtered areas. Accordingly, the client can both suppress the amount of received data from the server and obtain the detailed information of the required areas.

[0296] Moreover, it can also be that the server respectively creates the SWLD for each object according to the WLD, and the client respectively receives them according to the usage. Accordingly, the network bandwidth can be suppressed. For example, the server pre-identifies people or vehicles from the WLD and creates the SWLD for people and the SWLD for vehicles. When the client wants to obtain information about the people around it, it receives the SWLD for people, and when it wants to obtain information about vehicles, it receives the SWLD for vehicles. And the types of such SWLD can be distinguished according to the information (flags or types, etc.) attached to the header, etc.

[0297] Next, the configuration and the working process of the three-dimensional data encoding device (such as a server) according to the present embodiment will be described. Fig.16 FIG. is a block diagram of the three-dimensional data encoding device 400 according to the present embodiment. Fig.17 FIG. is a flowchart of the three-dimensional data encoding process performed by the three-dimensional data encoding device 400.

[0298] Fig.16 The three-dimensional data encoding device 400 shown generates encoded three-dimensional data 413 and 414 as an encoded stream by encoding the input three-dimensional data 411. Here, the encoded three-dimensional data 413 is the encoded three-dimensional data corresponding to the WLD, and the encoded three-dimensional data 414 is the encoded three-dimensional data corresponding to the SWLD. The three-dimensional data encoding device 400 includes: an acquisition unit 401, an encoding area determination unit 402, an SWLD extraction unit 403, a WLD encoding unit 404, and an SWLD encoding unit 405.

[0299] As Fig.17 shown, first, the acquisition unit 401 acquires the input three-dimensional data 411 as point cloud data in a three-dimensional space (S401).

[0300] Next, the coding area determination unit 402 determines the spatial area of the coding object based on the spatial area where the point cloud data exists (S402).

[0301] Next, the SWLD extraction unit 403 defines the spatial area of the coding object as the WLD, and calculates the feature amount based on each VXL included in the WLD. Further, the SWLD extraction unit 403 extracts the VXL whose feature amount is equal to or greater than a preset threshold value, defines the extracted VXL as the FVXL, and generates the extracted three-dimensional data 412 by adding the FVXL to the SWLD (S403). That is, the extracted three-dimensional data 412 whose feature amount is equal to or greater than the threshold value is extracted from the input three-dimensional data 411.

[0302] Next, the WLD coding unit 404 generates the coded three-dimensional data 413 corresponding to the WLD by coding the input three-dimensional data 411 corresponding to the WLD (S404). At this time, the WLD coding unit 404 attaches information for distinguishing that the coded three-dimensional data 413 is a stream including the WLD to the head of the coded three-dimensional data 413.

[0303] Further, the SWLD coding unit 405 generates the coded three-dimensional data 414 corresponding to the SWLD by coding the extracted three-dimensional data 412 corresponding to the SWLD (S405). At this time, the SWLD coding unit 405 attaches information for distinguishing that the coded three-dimensional data 414 is a stream including the SWLD to the head of the coded three-dimensional data 414.

[0304] Further, the processing order of the process of generating the coded three-dimensional data 413 and the process of generating the coded three-dimensional data 414 may be opposite to the above. Further, a part or all of the above processes may be executed in parallel.

[0305] The information given to the heads of the coded three-dimensional data 413 and 414 is defined as a parameter such as "world_type", for example. When world_type = 0, it indicates that the stream includes the WLD, and when world_type = 1, it indicates that the stream includes the SWLD. When defining more other categories, the assigned value can be increased as in world_type = 2. Further, a specific flag may be included in one of the coded three-dimensional data 413 and 414. For example, the coded three-dimensional data 414 may be given a flag indicating that the stream includes the SWLD. In this case, the decoding device can determine whether the stream includes the WLD or the SWLD based on the presence or absence of the flag.

[0306] Further, the coding method used by the WLD coding unit 404 when coding the WLD may be different from the coding method used by the SWLD coding unit 405 when coding the SWLD.

[0307] For example, since the SWLD data is selected, the correlation with the surrounding data may be lower compared to the WLD. Therefore, in the encoding method for SWLD, among intra prediction and inter prediction, inter prediction is prioritized compared to the encoding method for WLD.

[0308] Also, it may be that the representation methods of three-dimensional positions are different in the encoding method for SWLD and the encoding method for WLD. For example, it may be that in FWLD, the three-dimensional position of FVXL is represented by three-dimensional coordinates, and in WLD, the three-dimensional position is represented by an octree described later, and vice versa.

[0309] Also, the SWLD encoding unit 405 encodes in such a way that the data size of the encoded three-dimensional data 414 of SWLD is smaller than the data size of the encoded three-dimensional data 413 of WLD. As described above, for example, the correlation between data in SWLD may be lower than that in WLD. Accordingly, the encoding efficiency decreases, and the data size of the encoded three-dimensional data 414 may be larger than the data size of the encoded three-dimensional data 413 of WLD. Therefore, when the data size of the obtained encoded three-dimensional data 414 of SWLD is larger than the data size of the encoded three-dimensional data 413 of WLD, the SWLD encoding unit 405 performs re-encoding to regenerate the encoded three-dimensional data 414 with a reduced data size.

[0310] For example, the SWLD extraction unit 403 regenerates the extracted three-dimensional data 412 with a reduced number of extracted feature points, and the SWLD encoding unit 405 encodes the extracted three-dimensional data 412. Alternatively, the quantization level in the SWLD encoding unit 405 can be made coarser. For example, in the octree structure described later, by rounding the data in the bottom layer, the quantization level can be made coarser.

[0311] Also, when the SWLD encoding unit 405 cannot make the data size of the encoded three-dimensional data 414 of SWLD smaller than the data size of the encoded three-dimensional data 413 of WLD, it may not generate the encoded three-dimensional data 414 of SWLD. Alternatively, the encoded three-dimensional data 413 of WLD can be copied to the encoded three-dimensional data 414 of SWLD. That is, as the encoded three-dimensional data 414 of SWLD, the encoded three-dimensional data 413 of WLD can be directly used.

[0312] Next, the configuration and operation flow of the three-dimensional data decoding device (for example, a client) according to the present embodiment will be described. Fig.18 It is a block diagram of the three-dimensional data decoding device 500 according to the present embodiment. Fig.19 It is a flowchart of the three-dimensional data decoding process performed by the three-dimensional data decoding device 500.

[0313] Fig.18 The three-dimensional data decoding device 500 shown generates decoded three-dimensional data 512 or 513 by decoding the encoded three-dimensional data 511. Here, the encoded three-dimensional data 511 is, for example, the encoded three-dimensional data 413 or 414 generated by the three-dimensional data encoding device 400.

[0314] The three-dimensional data decoding device 500 includes: an acquisition unit 501, a header analysis unit 502, a WLD decoding unit 503, and a SWLD decoding unit 504.

[0315] As Fig.19 shown, first, the acquisition unit 501 acquires the encoded three-dimensional data 511 (S501). Next, the header analysis unit 502 analyzes the header of the encoded three-dimensional data 511 to determine whether the encoded three-dimensional data 511 is a stream including WLD or a stream including SWLD (S502). For example, the determination is made with reference to the above-described parameter of world_type.

[0316] In the case where the encoded three-dimensional data 511 is a stream including WLD (Yes in S503), the WLD decoding unit 503 decodes the encoded three-dimensional data 511 to generate decoded three-dimensional data 512 of WLD (S504). In addition, in the case where the encoded three-dimensional data 511 is a stream including SWLD (No in S503), the SWLD decoding unit 504 decodes the encoded three-dimensional data 511 to generate decoded three-dimensional data 513 of SWLD (S505).

[0317] And, similar to the encoding device, the decoding method used by the WLD decoding unit 503 when decoding WLD and the decoding method used by the SWLD decoding unit 504 when decoding SWLD can be different. For example, in the decoding method for SWLD, inter-frame prediction in intra-frame prediction and inter-frame prediction can be prioritized compared to the decoding method for WLD.

[0318] And, in the decoding method for SWLD and the decoding method for WLD, the representation method of the three-dimensional position can be different. For example, in SWLD, the three-dimensional position of FVXL can be represented by three-dimensional coordinates, and in WLD, the three-dimensional position can be represented by an octree described later, and vice versa.

[0319] Next, the octree representation as the representation method of the three-dimensional position will be described. The VXL data included in the three-dimensional data is converted into an octree structure and then encoded. Fig. 20 An example of VXL of WLD is shown. Fig.21 Shows Fig. 20 The octree structure of the WLD shown. In Fig. 20 In the example shown, there are three VXLs (hereinafter, effective VXLs) that include point groups, namely VXL1 to VXL3. As Fig.21 shown, the octree structure is composed of nodes and leaf nodes. Each node has a maximum of 8 nodes or leaf nodes. Each leaf node has VXL information. Here, Fig.21 among the leaf nodes shown, leaf nodes 1, 2, and 3 respectively represent Fig. 20 the VXL1, VXL2, and VXL3 shown.

[0320] Specifically, each node and leaf node correspond to a three-dimensional position. Node 1 corresponds to Fig. 20 all the blocks shown. The block corresponding to Node 1 is divided into 8 blocks. Among the 8 blocks, the blocks including the effective VXL are set as nodes, and the other blocks are set as leaf nodes. The block corresponding to the node is further divided into 8 nodes or leaf nodes, and the number of times this process is repeated is the same as the number of levels in the tree structure. And all the blocks in the bottom layer are set as leaf nodes.

[0321] And, Fig. 22 an example of the SWLD generated from the Fig. 20 shown WLD is shown. Fig. 20 The results of feature quantity extraction of the VXL1 and VXL2 shown are judged as FVXL1 and FVXL2 and added to the SWLD. In addition, since VXL3 is not judged as FVXL, it is not included in the SWLD. Fig.23 An example of the octree structure of the Fig. 22 shown SWLD is shown. In the Fig.23 octree structure shown, Fig.21 the leaf node 3 corresponding to VXL3 shown is deleted. Accordingly, Fig.21 node 3 shown has no effective VXL and is changed to a leaf node. In general, the number of leaf nodes in the SWLD is smaller than that in the WLD, and the encoded three-dimensional data of the SWLD is also smaller than that of the WLD.

[0322] The following describes a modification example of this embodiment.

[0323] For example, it can also be the case where, when a client such as an in-vehicle device estimates its own position, it receives the SWLD from the server, uses the SWLD to estimate its own position, and performs obstacle detection. In this case, various methods such as a distance sensor such as a rangefinder, a stereo camera, or a combination of multiple monocular cameras are used to perform obstacle detection based on the three-dimensional information of the surrounding area obtained by itself.

[0324] Also, generally speaking, it is difficult to include VXL data of flat areas in the SWLD. For this reason, the server maintains a downsampled world space (SubWLD) obtained by downsampling the WLD for detecting stationary obstacles, and can send the SWLD and the SubWLD to the client. Accordingly, both the network bandwidth can be suppressed and the self-position estimation and obstacle detection can be performed on the client side.

[0325] Also, when the client quickly depicts three-dimensional map data, it is convenient if the map information has a grid structure. Thus, the server can generate a grid based on the WLD and maintain it in advance as a grid world space (MWLD). For example, when the client needs to perform rough three-dimensional depiction, it receives the MWLD, and when it needs to perform detailed three-dimensional depiction, it receives the WLD. Accordingly, the network bandwidth can be suppressed.

[0326] Also, although the server sets the VXL whose feature amount is above the threshold as the FVXL from each VXL, the FVXL can also be calculated by different methods. For example, if the server determines that the VXL, VLM, SPC, or GOS constituting a signal or an intersection is required for self-position estimation, driving assistance, or autonomous driving, etc., it can be included in the SWLD as the FVXL, FVLM, FSPC, or FGOS. And the above determination can be made manually. In addition, the FVXL obtained by the above method can be added to the FVXL, etc. set based on the feature amount. That is, the SWLD extraction unit 403 can further extract, from the input three-dimensional data 411, the data corresponding to an object having a predetermined attribute as the extracted three-dimensional data 412.

[0327] Also, different labels can be assigned to the situations that are required for these uses, different from the feature amount. The server can separately maintain the FVXL required for self-position estimation, driving assistance, or autonomous driving, such as signals or intersections, as an upper layer of the SWLD (for example, a lane world space).

[0328] Also, the server can attach attributes to the VXLs in the WLD in units of random access or prescribed units. The attributes include, for example, information indicating whether it is required or not required for self-position estimation, or information indicating whether it is important as traffic information such as a signal or an intersection. And the attributes can also include the correspondence with Features (intersections or roads, etc.) in lane information (GDF: Geographic DataFiles, etc.).

[0329] Also, as a method for updating the WLD or the SWLD, the following method can be adopted.

[0330] Update information such as changes in people, construction, or street trees (facing the trajectory) is loaded into the server as a point cloud or metadata. Based on this load, the server updates the WLD, and after that, uses the updated WLD to update the SWLD.

[0331] Also, when the client detects a mismatch between the three-dimensional information generated by itself during self-position estimation and the three-dimensional information received from the server, the three-dimensional information generated by itself can be sent to the server together with an update notification. In this case, the server uses the WLD to update the SWLD. If the SWLD is not updated, the server determines that the WLD itself is old.

[0332] Also, as header information of the encoded stream, information for distinguishing between WLD and SWLD is attached. For example, in a case where there are multiple world spaces such as a grid world space or a lane world space, information for distinguishing them can be attached to the header information. Also, in a case where there are multiple SWLDs with different feature amounts, information for distinguishing them separately can also be attached to the header information.

[0333] Also, although the SWLD is composed of FVXLs, it can also include VXLs that are not determined to be FVXLs. For example, the SWLD can include adjacent VXLs used when calculating the feature amounts of FVXLs. Accordingly, even when no feature amount information is attached to each FVXL of the SWLD, the client can calculate the feature amounts of FVXLs when receiving the SWLD. Also, at this time, the SWLD can include information for distinguishing whether each VXL is an FVXL or a VXL.

[0334] As described above, the three-dimensional data encoding device 400 extracts the extracted three-dimensional data 412 (second three-dimensional data) whose feature amount is equal to or greater than the threshold from the input three-dimensional data 411 (first three-dimensional data), and generates the encoded three-dimensional data 414 (first encoded three-dimensional data) by encoding the extracted three-dimensional data 412.

[0335] Accordingly, the three-dimensional data encoding device 400 generates the encoded three-dimensional data 414 obtained by encoding data whose feature amount is equal to or greater than the threshold. In this way, compared with the case of directly encoding the input three-dimensional data 411, the data amount can be reduced. Therefore, the three-dimensional data encoding device 400 can reduce the data amount during transmission.

[0336] Also, the three-dimensional data encoding device 400 further generates the encoded three-dimensional data 413 (second encoded three-dimensional data) by encoding the input three-dimensional data 411.

[0337] Accordingly, the three-dimensional data encoding device 400 can selectively transmit the encoded three-dimensional data 413 and the encoded three-dimensional data 414 according to the usage purpose or the like.

[0338] Moreover, the extracted three-dimensional data 412 is encoded by the first encoding method, and the input three-dimensional data 411 is encoded by a second encoding method different from the first encoding method.

[0339] Accordingly, the three-dimensional data encoding device 400 can adopt appropriate encoding methods for the input three-dimensional data 411 and the extracted three-dimensional data 412 respectively.

[0340] Moreover, in the first encoding method, among intra prediction and inter prediction, inter prediction is prioritized compared with the second encoding method.

[0341] Accordingly, the three-dimensional data encoding device 400 can increase the priority of inter prediction for the extracted three-dimensional data 412 where the correlation between adjacent data is likely to become low.

[0342] Moreover, in the first encoding method and the second encoding method, the representation methods of three-dimensional positions are different. For example, in the second encoding method, the three-dimensional position is represented by an octree, and in the first encoding method, the three-dimensional position is represented by three-dimensional coordinates.

[0343] Accordingly, the three-dimensional data encoding device 400 can adopt a more appropriate representation method of three-dimensional positions for three-dimensional data with different numbers of data (the number of VXL or FVXL).

[0344] Moreover, at least one of the encoded three-dimensional data 413 and 414 includes an identifier indicating whether the encoded three-dimensional data is the encoded three-dimensional data obtained by encoding the input three-dimensional data 411 or the encoded three-dimensional data obtained by encoding a part of the input three-dimensional data 411. That is, this identifier indicates whether the encoded three-dimensional data is the encoded three-dimensional data 413 of WLD or the encoded three-dimensional data 414 of SWLD.

[0345] Accordingly, the decoding device can easily determine whether the acquired encoded three-dimensional data is the encoded three-dimensional data 413 or the encoded three-dimensional data 414.

[0346] Moreover, the three-dimensional data encoding device 400 encodes the extracted three-dimensional data 412 in such a way that the data amount of the encoded three-dimensional data 414 is less than the data amount of the encoded three-dimensional data 413.

[0347] Accordingly, the three-dimensional data encoding device 400 can make the data amount of the encoded three-dimensional data 414 less than the data amount of the encoded three-dimensional data 413.

[0348] Further, the three-dimensional data encoding device 400 extracts data corresponding to an object having a predetermined attribute from the input three-dimensional data 411 as the extracted three-dimensional data 412. For example, an object having a predetermined attribute refers to an object required for self-position estimation, driving assistance, or autonomous driving, such as a signal or an intersection.

[0349] Accordingly, the three-dimensional data encoding device 400 can generate the encoded three-dimensional data 414 including the data required by the decoding device.

[0350] Further, the three-dimensional data encoding device 400 (server) sends either the encoded three-dimensional data 413 or 414 to the client according to the state of the client.

[0351] Accordingly, the three-dimensional data encoding device 400 can send appropriate data according to the state of the client.

[0352] The state of the client includes the communication status of the client (e.g., network bandwidth) or the moving speed of the client.

[0353] Further, the three-dimensional data encoding device 400 sends either the encoded three-dimensional data 413 or 414 to the client according to the request of the client.

[0354] Accordingly, the three-dimensional data encoding device 400 can send appropriate data according to the request of the client.

[0355] The three-dimensional data decoding device 500 according to the present embodiment decodes the encoded three-dimensional data 413 or 414 generated by the above three-dimensional data encoding device 400.

[0356] That is, the three-dimensional data decoding device 500 decodes the encoded three-dimensional data 414 obtained by encoding the extracted three-dimensional data 412 whose feature amount extracted from the input three-dimensional data 411 is above the threshold value by the first decoding method. And the three-dimensional data decoding device 500 decodes the encoded three-dimensional data 413 obtained by encoding the input three-dimensional data 411 by using a second decoding method different from the first decoding method.

[0357] Accordingly, the three-dimensional data decoding device 500 can selectively receive, for example, according to the usage purpose, etc., the encoded three-dimensional data 414 and the encoded three-dimensional data 413 obtained by encoding the data whose feature amount is above the threshold value. Accordingly, the three-dimensional data decoding device 500 can reduce the amount of data during transmission. Moreover, the three-dimensional data decoding device 500 can adopt appropriate decoding methods for the input three-dimensional data 411 and the extracted three-dimensional data 412 respectively.

[0358] Further, in the first decoding method, among intra prediction and inter prediction, inter prediction is prioritized compared to the second decoding method.

[0359] Accordingly, the three-dimensional data decoding device 500 can increase the priority of inter prediction for the extracted three-dimensional data where the correlation between adjacent data is likely to decrease.

[0360] Also, in the first decoding method and the second decoding method, the representation methods of three-dimensional positions are different. For example, in the second decoding method, the three-dimensional position is represented by an octree, and in the first decoding method, the three-dimensional position is represented by three-dimensional coordinates.

[0361] Accordingly, the three-dimensional data decoding device 500 can adopt a more appropriate representation method of three-dimensional positions for three-dimensional data with different numbers of data (the number of VXL or FVXL).

[0362] Also, at least one of the encoded three-dimensional data 413 and 414 includes an identifier that indicates whether the encoded three-dimensional data is obtained by encoding the input three-dimensional data 411 or by encoding a part of the input three-dimensional data 411. The three-dimensional data decoding device 500 refers to this identifier to identify the encoded three-dimensional data 413 and 414.

[0363] Accordingly, the three-dimensional data decoding device 500 can easily determine whether the obtained encoded three-dimensional data is the encoded three-dimensional data 413 or the encoded three-dimensional data 414.

[0364] Also, the three-dimensional data decoding device 500 further notifies the server of the state of the client (the three-dimensional data decoding device 500). The three-dimensional data decoding device 500 receives one of the encoded three-dimensional data 413 and 414 sent from the server according to the state of the client.

[0365] Accordingly, the three-dimensional data decoding device 500 can receive appropriate data according to the state of the client.

[0366] Also, the state of the client includes the communication status of the client (e.g., network bandwidth) or the moving speed of the client.

[0367] Also, the three-dimensional data decoding device 500 further requests one of the encoded three-dimensional data 413 and 414 from the server and receives one of the encoded three-dimensional data 413 and 414 sent from the server according to this request.

[0368] Accordingly, the three-dimensional data decoding device 500 can receive appropriate data corresponding to the usage.

[0369] (Embodiment 3)

[0370] In this embodiment, a method for transmitting and receiving three-dimensional data between vehicles will be described. For example, three-dimensional data is transmitted and received between the host vehicle and surrounding vehicles.

[0371] Fig.24 FIG. 5 is a block diagram of a three-dimensional data creation device 620 according to this embodiment. The three-dimensional data creation device 620 is included in the host vehicle, for example, and creates a denser third three-dimensional data 636 by synthesizing the received second three-dimensional data 635 and the first three-dimensional data 632 created by the three-dimensional data creation device 620.

[0372] The three-dimensional data creation device 620 includes: a three-dimensional data creation unit 621, a request range determination unit 622, a search unit 623, a reception unit 624, a decoding unit 625, and a synthesis unit 626.

[0373] First, the three-dimensional data creation unit 621 creates the first three-dimensional data 632 using sensor information 631 detected by sensors provided in the host vehicle. Next, the request range determination unit 622 determines a request range, which is a three-dimensional space range where the data in the created first three-dimensional data 632 is insufficient.

[0374] Next, the search unit 623 searches for surrounding vehicles that hold three-dimensional data within the request range, and transmits request range information 633 indicating the request range to the surrounding vehicles identified by the search. Next, the reception unit 624 receives encoded three-dimensional data 634 as an encoded stream within the request range from the surrounding vehicles (S624). In addition, the search unit 623 can issue requests to all vehicles existing within the identified range without discrimination, and receive the encoded three-dimensional data 634 from the responding party. Further, the search unit 623 is not limited to vehicles, and can also issue requests to objects such as traffic lights or signs, and receive the encoded three-dimensional data 634 from the object.

[0375] Next, the received encoded three-dimensional data 634 is decoded by the decoding unit 625 to obtain the second three-dimensional data 635. Next, the first three-dimensional data 632 and the second three-dimensional data 635 are synthesized by the synthesis unit 626 to create a denser third three-dimensional data 636.

[0376] Next, the configuration and operation of a three-dimensional data transmission device 640 according to this embodiment will be described. Fig.25 FIG. 6 is a block diagram of the three-dimensional data transmission device 640.

[0377] The three-dimensional data transmission device 640 is included, for example, in the surrounding vehicles described above. It processes the fifth three-dimensional data 652 created by the surrounding vehicles into the sixth three-dimensional data 654 requested by its own vehicle, generates encoded three-dimensional data 634 by encoding the sixth three-dimensional data 654, and transmits the encoded three-dimensional data 634 to its own vehicle.

[0378] The three-dimensional data transmission device 640 includes: a three-dimensional data creation unit 641, a reception unit 642, an extraction unit 643, an encoding unit 644, and a transmission unit 645.

[0379] First, the three-dimensional data creation unit 641 creates the fifth three-dimensional data 652 using the sensor information 651 detected by the sensors equipped in the surrounding vehicles. Next, the reception unit 642 receives the requested range information 633 transmitted from its own vehicle.

[0380] Next, the extraction unit 643 extracts the three-dimensional data within the requested range indicated by the requested range information 633 from the fifth three-dimensional data 652, and processes the fifth three-dimensional data 652 into the sixth three-dimensional data 654. Then, the encoding unit 644 encodes the sixth three-dimensional data 654 to generate the encoded three-dimensional data 634 as an encoded stream. Thus, the transmission unit 645 transmits the encoded three-dimensional data 634 to its own vehicle.

[0381] In addition, here, although an example in which the own vehicle is equipped with the three-dimensional data creation device 620 and the surrounding vehicles are equipped with the three-dimensional data transmission device 640 has been described, each vehicle may also have the functions of the three-dimensional data creation device 620 and the three-dimensional data transmission device 640.

[0382] (Embodiment 4)

[0383] In this embodiment, the operations related to abnormal conditions in the self-position estimation based on the three-dimensional map will be described.

[0384] The use of autonomous movement of moving bodies such as the autonomous driving of motor vehicles, robots, or flying objects such as drones will expand in the future. As an example of a method for realizing such autonomous movement, there is a method in which the moving body estimates its own position within the three-dimensional map (self-position estimation) and travels according to the map.

[0385] Self-position estimation is achieved by matching the three-dimensional map with the three-dimensional information around the own vehicle (hereinafter referred to as the own vehicle detection three-dimensional data) obtained by sensors such as a range finder (LIDAR, etc.) or a stereo camera mounted on the own vehicle, and estimating the position of the own vehicle within the three-dimensional map.

[0386] A three-dimensional map, such as the HD map proposed by HERE Technologies, etc., is not only a three-dimensional point cloud, but may also include two-dimensional map data such as road and intersection shape information, or information that changes in real time such as traffic jams and accidents. The three-dimensional map is composed of multiple levels such as three-dimensional data, two-dimensional data, and metadata that changes in real time. The device can obtain only the required data, or can also refer to the required data.

[0387] The data of the point cloud can be the above-mentioned SWLD, or can also include point group data that is not feature points. And the transmission and reception of the data of the point cloud are basically executed in one or more random access units.

[0388] As a method for matching a three-dimensional map with the three-dimensional data detected by the own vehicle, the following method can be adopted. For example, the device compares the shapes of the point groups in the respective point clouds, and determines the part with a high similarity between the feature points as the same position. And when the three-dimensional map is composed of SWLD, the device compares the feature points constituting the SWLD with the three-dimensional feature points extracted from the three-dimensional data detected by the own vehicle and performs matching.

[0389] Here, in order to perform accurate self-position estimation, the following (A) and (B) need to be satisfied. (A) The three-dimensional map and the three-dimensional data detected by the own vehicle can already be obtained. (B) Their accuracies satisfy a predetermined standard. However, in the following abnormal situations, (A) or (B) cannot be satisfied.

[0390] (1) The three-dimensional map cannot be obtained through the communication path.

[0391] (2) There is no three-dimensional map, or the obtained three-dimensional map is damaged.

[0392] (3) The sensors of the own vehicle malfunction, or due to bad weather, the generation accuracy of the three-dimensional data detected by the own vehicle is insufficient.

[0393] The operations for coping with these abnormal situations will be described below. Although the operations are described below taking a vehicle as an example, the following methods can also be applied to all moving objects that perform autonomous movement such as robots and drones.

[0394] What will be described below is the configuration and operation of the three-dimensional information processing device according to the present embodiment for coping with abnormal situations in the three-dimensional map or the three-dimensional data detected by the own vehicle. Fig.26 It is a block diagram showing a configuration example of the three-dimensional information processing device 700 according to the present embodiment.

[0395] The three-dimensional information processing device 700 is mounted on a moving object such as a motor vehicle, for example. As Fig.26As shown in the figure, the three-dimensional information processing device 700 includes: a three-dimensional map acquisition unit 701, a self-vehicle detection data acquisition unit 702, an abnormal situation determination unit 703, a response operation determination unit 704, and an operation control unit 705.

[0396] In addition, the three-dimensional information processing device 700 may also include a camera for obtaining two-dimensional images, or may include two-dimensional or one-dimensional sensors (not shown) such as sensors for one-dimensional data using ultrasonic waves or lasers, which are used to detect structural objects or moving objects around the self-vehicle. And the three-dimensional information processing device 700 may also include a communication unit (not shown), which is used to obtain a three-dimensional map through a mobile communication network such as 4G or 5G, or vehicle-to-vehicle communication, or road-to-vehicle communication.

[0397] The three-dimensional map acquisition unit 701 acquires a three-dimensional map 711 near the driving route. For example, the three-dimensional map acquisition unit 701 acquires the three-dimensional map 711 through a mobile communication network, or vehicle-to-vehicle communication, or road-to-vehicle communication.

[0398] Next, the self-vehicle detection data acquisition unit 702 acquires self-vehicle detection three-dimensional data 712 based on the sensor information. For example, the self-vehicle detection data acquisition unit 702 generates the self-vehicle detection three-dimensional data 712 based on the sensor information obtained by the sensors equipped on the self-vehicle.

[0399] Next, the abnormal situation determination unit 703 detects an abnormal situation by performing a pre-determined check on at least one of the acquired three-dimensional map 711 and the self-vehicle detection three-dimensional data 712. That is, the abnormal situation determination unit 703 determines whether at least one of the acquired three-dimensional map 711 and the self-vehicle detection three-dimensional data 712 is abnormal.

[0400] When an abnormal situation is detected, the response operation determination unit 704 determines a response operation for the abnormal situation. Next, the operation control unit 705 controls the operations of each processing unit required in the implementation of the response operation, such as the three-dimensional map acquisition unit 701.

[0401] In addition, when no abnormal situation is detected, the three-dimensional information processing device 700 ends the processing.

[0402] And the three-dimensional information processing device 700 estimates the self-position of the vehicle equipped with the three-dimensional information processing device 700 by using the three-dimensional map 711 and the self-vehicle detection three-dimensional data 712. Next, the three-dimensional information processing device 700 uses the result of the self-position estimation to make the vehicle perform autonomous driving.

[0403] Accordingly, the three-dimensional information processing device 700 obtains map data (three-dimensional map 711) including first three-dimensional position information via a channel. For example, the first three-dimensional position information is encoded in units of partial spaces having three-dimensional coordinate information. The first three-dimensional position information includes a plurality of random access units. Each of the plurality of random access units is an aggregate of one or more partial spaces and can be independently decoded. For example, the first three-dimensional position information is data (SWLD) in which feature points where three-dimensional feature amounts exceed a specified threshold are encoded.

[0404] Furthermore, the three-dimensional information processing device 700 generates second three-dimensional position information (own vehicle detection three-dimensional data 712) based on information detected by a sensor. Next, the three-dimensional information processing device 700 determines whether the first three-dimensional position information or the second three-dimensional position information is abnormal by performing an abnormality determination process on the first three-dimensional position information or the second three-dimensional position information.

[0405] When the three-dimensional information processing device 700 determines that the first three-dimensional position information or the second three-dimensional position information is abnormal, it determines a response operation for the abnormality. Next, the three-dimensional information processing device 700 executes control required for the implementation of the response operation.

[0406] Accordingly, the three-dimensional information processing device 700 can detect an abnormality in the first three-dimensional position information or the second three-dimensional position information and can perform a response operation.

[0407] (Embodiment 5)

[0408] In the present embodiment, a method for transmitting three-dimensional data to a following vehicle and the like will be described.

[0409] Fig. 27 FIG. is a block diagram showing a configuration example of a three-dimensional data production device 810 according to the present embodiment. The three-dimensional data production device 810 is mounted on a vehicle, for example. The three-dimensional data production device 810 transmits and receives three-dimensional data to and from external traffic cloud monitoring, a preceding vehicle, or a following vehicle, and simultaneously produces and stores the three-dimensional data.

[0410] The three-dimensional data production device 810 includes: a data reception unit 811, a communication unit 812, a reception control unit 813, a format conversion unit 814, a plurality of sensors 815, a three-dimensional data production unit 816, a three-dimensional data synthesis unit 817, a three-dimensional data storage unit 818, a communication unit 819, a transmission control unit 820, a format conversion unit 821, and a data transmission unit 822.

[0411] The data receiving unit 811 receives three-dimensional data 831 from the traffic cloud monitoring or the vehicle ahead. The three-dimensional data 831 includes, for example, point clouds, visible light images, depth information, sensor position information, or speed information that contains information on areas that cannot be detected by the sensors 815 of the host vehicle itself.

[0412] The communication unit 812 communicates with the traffic cloud monitoring or the vehicle ahead, and sends data transmission requests and the like to the traffic cloud monitoring or the vehicle ahead.

[0413] The reception control unit 813 exchanges information such as corresponding formats with the communication partner via the communication unit 812 to establish communication with the communication partner.

[0414] The format conversion unit 814 generates three-dimensional data 832 by performing format conversion and the like on the three-dimensional data 831 received by the data receiving unit 811. Further, when the three-dimensional data 831 is compressed or encoded, the format conversion unit 814 performs decompression or decoding processing.

[0415] The plurality of sensors 815 are a group of sensors such as LiDAR, visible light cameras, or infrared cameras that obtain information on the outside of the vehicle, and generate sensor information 833. For example, when the sensor 815 is a laser sensor such as LiDAR, the sensor information 833 is three-dimensional data such as a point cloud (point group data). In addition, the number of sensors 815 may not be plural.

[0416] The three-dimensional data creation unit 816 generates three-dimensional data 834 based on the sensor information 833. The three-dimensional data 834 includes, for example, information such as point clouds, visible light images, depth information, sensor position information, or speed information.

[0417] The three-dimensional data synthesis unit 817 synthesizes the three-dimensional data 832 created by the traffic cloud monitoring or the vehicle ahead and the like into the three-dimensional data 834 created based on the sensor information 833 of the host vehicle itself, thereby enabling the construction of three-dimensional data 835 that also includes the space in front of the vehicle ahead that cannot be detected by the sensors 815 of the host vehicle.

[0418] The three-dimensional data storage unit 818 stores the generated three-dimensional data 835 and the like.

[0419] The communication unit 819 communicates with the traffic cloud monitoring or the vehicle behind, and sends data transmission requests and the like to the traffic cloud monitoring or the vehicle behind.

[0420] The transmission control unit 820 exchanges information such as the corresponding format with the communication partner via the communication unit 819 to establish communication with the communication partner. Further, the transmission control unit 820 determines the transmission area of the space of the three-dimensional data to be transmitted based on the three-dimensional data construction information of the three-dimensional data 832 generated by the three-dimensional data synthesis unit 817 and the data transmission request from the communication partner.

[0421] Specifically, the transmission control unit 820 determines the transmission area of the space in front of the host vehicle that cannot be detected by the sensors of the following vehicle in accordance with the data transmission request from the traffic cloud monitoring or the following vehicle. Further, the transmission control unit 820 determines the transmission area by judging, based on the three-dimensional data construction information, whether there is an update to the space that can be transmitted or the already transmitted space. For example, the transmission control unit 820 determines as the transmission area the area that is both specified by the data transmission request and where the corresponding three-dimensional data 835 exists. Then, the transmission control unit 820 notifies the format conversion unit 821 of the format corresponding to the communication partner and the transmission area.

[0422] The format conversion unit 821 generates the three-dimensional data 837 by converting the three-dimensional data 836 of the transmission area in the three-dimensional data 835 stored in the three-dimensional data storage unit 818 into a format corresponding to the receiving side. Additionally, the format conversion unit 821 may compress or encode the three-dimensional data 837 to reduce the data volume.

[0423] The data transmission unit 822 transmits the three-dimensional data 837 to the traffic cloud monitoring or the following vehicle. The three-dimensional data 837 includes, for example, the point cloud, visible light image, depth information, or sensor position information in front of the host vehicle that contains information on the area that is a blind spot for the following vehicle.

[0424] In addition, although the format conversion units 814 and 821 are taken as examples for format conversion and the like, format conversion may not be performed.

[0425] With this configuration, the three-dimensional data production device 810 obtains the three-dimensional data 831 of the area that cannot be detected by the sensors 815 of the host vehicle from the outside, and generates the three-dimensional data 835 by synthesizing the three-dimensional data 831 and the three-dimensional data 834 based on the sensor information 833 detected by the sensors 815 of the host vehicle. Accordingly, the three-dimensional data production device 810 can generate the three-dimensional data of the range that cannot be detected by the sensors 815 of the host vehicle.

[0426] Further, the three-dimensional data production device 810 can transmit the three-dimensional data of the space in front of the host vehicle that cannot be detected by the sensors of the following vehicle to the traffic cloud monitoring or the following vehicle, etc., in accordance with the data transmission request from the traffic cloud monitoring or the following vehicle.

[0427] (Embodiment 6)

[0428] In the example to be described in Embodiment 5, a client device such as a vehicle sends three-dimensional data to another vehicle or a server such as a traffic cloud monitor. In this embodiment, the client device sends sensor information obtained by sensors to the server or another client device.

[0429] First, the configuration of the system according to this embodiment will be described. Fig.28 The configuration of the three-dimensional map and the sensor information transceiver system according to this embodiment is shown. The system includes a server 901, and client devices 902A and 902B. Additionally, when not making a special distinction between client devices 902A and 902B, they are also denoted as client device 902.

[0430] The client device 902 is, for example, an in-vehicle device mounted on a moving body such as a vehicle. The server 901 is, for example, a traffic cloud monitor or the like and can communicate with multiple client devices 902.

[0431] The server 901 sends a three-dimensional map composed of point clouds to the client device 902. Additionally, the composition of the three-dimensional map is not limited to point clouds and can also be represented by other three-dimensional data such as a grid structure.

[0432] The client device 902 sends sensor information obtained by the client device 902 to the server 901. The sensor information, for example, at least includes one of the information obtained by LiDAR, visible light images, infrared images, depth images, sensor position information, and speed information.

[0433] Regarding the data transmitted and received between the server 901 and the client device 902, it can be compressed when wanting to reduce the data, and can not be compressed when wanting to maintain the accuracy of the data. When compressing the data, for example, a three-dimensional compression method based on an octree can be adopted in the point cloud. And in visible light images, infrared images, and depth images, a two-dimensional image compression method can be adopted. The two-dimensional image compression method is, for example, MPEG-4 AVC or HEVC standardized by MPEG.

[0434] Furthermore, the server 901 sends the 3D map managed by the server 901 to the client device 902 in accordance with the transmission request of the 3D map from the client device 902. In addition, the server 901 may also send the 3D map without waiting for the transmission request of the 3D map from the client device 902. For example, the server 901 may also broadcast the 3D map to one or more client devices 902 in a preset space. Also, the server 901 may send the 3D map adapted to the position of the client device 902 to the client device 902 that has received a transmission request once at regular intervals. Also, the server 901 may send the 3D map to the client device 902 whenever the 3D map managed by the server 901 is updated.

[0435] The client device 902 sends a transmission request for the 3D map to the server 901. For example, when the client device 902 wants to estimate its own position while driving, the client device 902 sends a transmission request for the 3D map to the server 901.

[0436] In addition, in the following cases, the client device 902 may also send a transmission request for the 3D map to the server 901. When the 3D map held by the client device 902 is relatively old, the client device 902 may also send a transmission request for the 3D map to the server 901. For example, when a certain period of time has passed since the client device 902 obtained the 3D map, the client device 902 may also send a transmission request for the 3D map to the server 901.

[0437] It may also be that the client device 902 sends a transmission request for the 3D map to the server 901 a certain moment before the client device 902 is about to leave the space shown in the 3D map held by the client device 902. For example, it may also be that when the client device 902 is within a preset distance from the boundary of the space shown in the 3D map held by the client device 902, the client device 902 sends a transmission request for the 3D map to the server 901. Also, when the movement path and movement speed of the client device 902 are grasped, the moment when the client device 902 leaves the space shown in the 3D map held by the client device 902 can be predicted based on the grasped movement path and movement speed.

[0438] When the error in the position comparison between the 3D data created by the client device 902 based on the sensor information and the 3D map is above a certain range, the client device 902 may send a transmission request for the 3D map to the server 901.

[0439] The client device 902 sends the sensor information to the server 901 in accordance with the transmission request of the sensor information sent from the server 901. Additionally, the client device 902 may also send the sensor information to the server 901 without waiting for the transmission request of the sensor information from the server 901. For example, in the case where the client device 902 has received a transmission request for sensor information from the server 901 once, it may periodically send the sensor information to the server 901 within a certain period. And it may also be that, when the error in the position comparison between the three-dimensional data created based on the sensor information by the client device 902 and the three-dimensional map obtained from the server 901 is above a certain range, the client device 902 determines that there is a possibility that the three-dimensional map around the client device 902 has changed, and sends this judgment result together with the sensor information to the server 901.

[0440] The server 901 issues a transmission request for sensor information to the client device 902. For example, the server 901 receives the location information of the client device 902 such as GPS from the client device 902. Based on the location information of the client device 902, when it is determined that the client device 902 is approaching a space with less information in the three-dimensional map managed by the server 901, in order to regenerate the three-dimensional map, a transmission request for sensor information is issued to the client device 902. And it may also be that the server 901 issues a transmission request for sensor information when it wants to update the three-dimensional map, when it wants to confirm the road conditions during snow accumulation or disasters, or when it wants to confirm the congestion conditions or accident conditions, etc.

[0441] And it may also be that the client device 902 sets the data volume of the sensor information sent to the server 901 according to the communication state or frequency band at the time of receiving the transmission request for the sensor information received from the server 901. Setting the data volume of the sensor information sent to the server 901, for example, means increasing or decreasing the data itself, or selecting an appropriate compression method.

[0442] Fig.29 It is a block diagram showing a configuration example of the client device 902. The client device 902 receives a three-dimensional map composed of point clouds, etc. from the server 901, and estimates its own position based on the three-dimensional data created based on the sensor information of the client device 902. And the client device 902 sends the obtained sensor information to the server 901.

[0443] The client device 902 includes: a data receiving unit 1011, a communication unit 1012, a reception control unit 1013, a format conversion unit 1014, a plurality of sensors 1015, a three-dimensional data creation unit 1016, a three-dimensional image processing unit 1017, a three-dimensional data storage unit 1018, a format conversion unit 1019, a communication unit 1020, a transmission control unit 1021, and a data transmission unit 1022.

[0444] The data receiving unit 1011 receives a three-dimensional map 1031 from the server 901. The three-dimensional map 1031 is data including point clouds such as WLD or SWLD. The three-dimensional map 1031 may include either compressed data or uncompressed data.

[0445] The communication unit 1012 communicates with the server 901 and sends a data transmission request (for example, a request for transmission of a three-dimensional map) to the server 901.

[0446] The reception control unit 1013 exchanges information such as corresponding formats with the communication partner via the communication unit 1012 to establish communication with the communication partner.

[0447] The format conversion unit 1014 generates a three-dimensional map 1032 by performing format conversion or the like on the three-dimensional map 1031 received by the data receiving unit 1011. Further, when the three-dimensional map 1031 is compressed or encoded, the format conversion unit 1014 performs decompression or decoding processing. In addition, when the three-dimensional map 1031 is uncompressed data, the format conversion unit 1014 does not perform decompression or decoding processing.

[0448] The plurality of sensors 1015 are a group of sensors mounted on the client device 902 such as LiDAR, a visible light camera, an infrared camera, or a depth sensor, which are used to obtain information about the outside of the vehicle, and generate sensor information 1033. For example, when the sensor 1015 is a laser sensor such as LiDAR, the sensor information 1033 is three-dimensional data such as point clouds (point group data). In addition, the sensor 1015 may not be plural.

[0449] The three-dimensional data creation unit 1016 creates three-dimensional data 1034 around its own vehicle based on the sensor information 1033. For example, the three-dimensional data creation unit 1016 uses the information obtained by LiDAR and the visible light image obtained by the visible light camera to create point cloud data with color information around its own vehicle.

[0450] The 3D image processing unit 1017 performs self-position estimation processing of the host vehicle and the like using the received 3D map 1032 such as point cloud, and the 3D data 1034 around the host vehicle generated based on the sensor information 1033. Additionally, it is also possible that the 3D image processing unit 1017 synthesizes the 3D map 1032 and the 3D data 1034 to create the 3D data 1035 around the host vehicle, and uses the created 3D data 1035 to perform self-position estimation processing.

[0451] The 3D data storage unit 1018 stores the 3D map 1032, the 3D data 1034, the 3D data 1035, and the like.

[0452] The format conversion unit 1019 generates the sensor information 1037 by converting the sensor information 1033 into the format corresponding to the receiving side. Additionally, the format conversion unit 1019 can reduce the data volume by compressing or encoding the sensor information 1037. And when format conversion is not required, the format conversion unit 1019 can omit the processing. Also, the format conversion unit 1019 can control the data volume to be sent according to the specified transmission range.

[0453] The communication unit 1020 communicates with the server 901 and receives a data transmission request (a transmission request for sensor information) and the like from the server 901.

[0454] The transmission control unit 1021 exchanges information such as the corresponding format with the communication partner via the communication unit 1020 to establish communication.

[0455] The data transmission unit 1022 sends the sensor information 1037 to the server 901. The sensor information 1037 includes, for example, information obtained by LiDAR, a luminance image (visible light image) obtained by a visible light camera, an infrared image obtained by an infrared camera, a depth image obtained by a depth sensor, sensor position information, and speed information, etc., which are obtained by multiple sensors 1015.

[0456] Next, the configuration of the server 901 will be described. Fig.30 It is a block diagram showing a configuration example of the server 901. The server 901 receives the sensor information sent from the client device 902, and creates 3D data based on the received sensor information. The server 901 updates the 3D map managed by the server 901 using the created 3D data. And the server 901 sends the updated 3D map to the client device 902 according to the transmission request of the 3D map from the client device 902.

[0457] The server 901 includes: a data receiving unit 1111, a communication unit 1112, a reception control unit 1113, a format conversion unit 1114, a three-dimensional data creation unit 1116, a three-dimensional data synthesis unit 1117, a three-dimensional data storage unit 1118, a format conversion unit 1119, a communication unit 1120, a transmission control unit 1121, and a data transmission unit 1122.

[0458] The data receiving unit 1111 receives sensor information 1037 from the client device 902. The sensor information 1037 includes, for example, information obtained by LiDAR, a luminance image (visible light image) obtained by a visible light camera, an infrared image obtained by an infrared camera, a depth image obtained by a depth sensor, sensor position information, speed information, and the like.

[0459] The communication unit 1112 communicates with the client device 902 and sends a data transmission request (for example, a transmission request for sensor information) to the client device 902.

[0460] The reception control unit 1113 exchanges information such as the corresponding format with the communication partner via the communication unit 1112 to establish communication.

[0461] When the received sensor information 1037 is compressed or encoded, the format conversion unit 1114 generates sensor information 1132 by performing decompression or decoding processing. Additionally, when the sensor information 1037 is uncompressed data, the format conversion unit 1114 does not perform decompression or decoding processing.

[0462] The three-dimensional data creation unit 1116 creates three-dimensional data 1134 of the surroundings of the client device 902 based on the sensor information 1132. For example, the three-dimensional data creation unit 1116 uses the information obtained by LiDAR and the visible light image obtained by the visible light camera to create point cloud data with color information of the surroundings of the client device 902.

[0463] The three-dimensional data synthesis unit 1117 synthesizes the three-dimensional data 1134 created based on the sensor information 1132 with the three-dimensional map 1135 managed by the server 901, and thereby updates the three-dimensional map 1135.

[0464] The three-dimensional data storage unit 1118 stores the three-dimensional map 1135 and the like.

[0465] The format conversion unit 1119 generates the three-dimensional map 1031 by converting the three-dimensional map 1135 into a format corresponding to the receiving side. Additionally, the format conversion unit 1119 can also reduce the data volume by compressing or encoding the three-dimensional map 1135. Moreover, when format conversion is not required, the format conversion unit 1119 can also omit the processing. And the format conversion unit 1119 can control the data volume to be sent according to the specified transmission range.

[0466] The communication unit 1120 communicates with the client device 902 and receives a data transmission request (a transmission request for a three-dimensional map), etc. from the client device 902.

[0467] The transmission control unit 1121 exchanges information such as the corresponding format with the communication partner via the communication unit 1120 to establish communication.

[0468] The data transmission unit 1122 sends the three-dimensional map 1031 to the client device 902. The three-dimensional map 1031 is data including point clouds such as WLD or SWLD. Either compressed data or uncompressed data can also be included in the three-dimensional map 1031.

[0469] Next, the operation process of the client device 902 will be described. Fig.31 It is a flowchart showing the operation when the client device 902 obtains a three-dimensional map.

[0470] First, the client device 902 requests the server 901 to send a three-dimensional map (point cloud, etc.) (S1001). At this time, the client device 902 also sends the position information of the client device 902 obtained through GPS, etc. Accordingly, it is possible to request the server 901 to send a three-dimensional map related to this position information.

[0471] Next, the client device 902 receives the three-dimensional map from the server 901 (S1002). If the received three-dimensional map is compressed data, the client device 902 decodes the received three-dimensional map to generate an uncompressed three-dimensional map (S1003).

[0472] Next, the client device 902 creates three-dimensional data 1034 of the periphery of the client device 902 based on the sensor information 1033 obtained from the plurality of sensors 1015 (S1004). Next, the client device 902 estimates its own position of the client device 902 by using the three-dimensional map 1032 received from the server 901 and the three-dimensional data 1034 created based on the sensor information 1033 (S1005).

[0473] Fig.32It is a flowchart showing the operation when the client device 902 transmits sensor information. First, the client device 902 receives a request to transmit sensor information from the server 901 (S1011). The client device 902 that has received the transmission request transmits the sensor information 1037 to the server 901 (S1012). Additionally, when the sensor information 1033 includes multiple pieces of information obtained through multiple sensors 1015, the client device 902 compresses each piece of information in a compression method suitable for each piece of information, thereby generating the sensor information 1037.

[0474] Next, the operation process of the server 901 will be described. Fig.33 It is a flowchart showing the operation when the server 901 obtains sensor information. First, the server 901 requests the client device 902 to transmit sensor information (S1021). Next, the server 901 receives the sensor information 1037 transmitted from the client device 902 in accordance with this request (S1022). Next, the server 901 uses the received sensor information 1037 to create three-dimensional data 1134 (S1023). Next, the server 901 reflects the created three-dimensional data 1134 onto the three-dimensional map 1135 (S1024).

[0475] Fig.34 It is a flowchart showing the operation when the server 901 transmits the three-dimensional map. First, the server 901 receives a request to transmit the three-dimensional map from the client device 902 (S1031). The server 901 that has received the request to transmit the three-dimensional map transmits the three-dimensional map 1031 to the client device 902 (S1032). At this time, the server 901 can extract the three-dimensional map in the vicinity corresponding to the location information of the client device 902 and transmit the extracted three-dimensional map. And it can be that the server 901 compresses the three-dimensional map composed of point clouds, for example, using a compression method such as an octree, and transmits the compressed three-dimensional map.

[0476] Hereinafter, a modified example of this embodiment will be described.

[0477] The server 901 uses the sensor information 1037 received from the client device 902 to create three-dimensional data 1134 near the location of the client device 902. Then, the server 901 matches the created three-dimensional data 1134 with the three-dimensional map 1135 of the same area managed by the server 901, and calculates the difference between the three-dimensional data 1134 and the three-dimensional map 1135. When the difference is equal to or greater than a predetermined threshold, the server 901 determines that some abnormality has occurred around the client device 902. For example, when the ground surface sinks due to a natural disaster such as an earthquake, a large difference may occur between the three-dimensional map 1135 managed by the server 901 and the three-dimensional data 1134 created based on the sensor information 1037.

[0478] The sensor information 1037 may also include at least one of the type of the sensor, the performance of the sensor, and the model of the sensor. It may also be that a category ID corresponding to the performance of the sensor is attached to the sensor information 1037. For example, when the sensor information 1037 is information obtained by LiDAR, it is considered to assign an identifier according to the performance of the sensor. For example, category 1 is assigned to a sensor that can obtain information with an accuracy of several millimeters, category 2 is assigned to a sensor that can obtain information with an accuracy of several centimeters, and category 3 is assigned to a sensor that can obtain information with an accuracy of several meters. Also, the server 901 can estimate the performance information of the sensor from the model of the client device 902. For example, when the client device 902 is mounted on a vehicle, the server 901 can determine the specification information of the sensor based on the model of the vehicle. In this case, the server 901 can obtain the information of the vehicle model in advance, or include this information in the sensor information. It may also be that the server 901 uses the obtained sensor information 1037 to switch the degree of correction for the three-dimensional data 1134 created using the sensor information 1037. For example, when the sensor performance is high accuracy (category 1), the server 901 does not perform correction on the three-dimensional data 1134. When the sensor performance is low accuracy (category 3), the server 901 applies correction suitable for the accuracy of the sensor to the three-dimensional data 1134. For example, the server 901 increases the degree (intensity) of correction as the accuracy of the sensor becomes lower.

[0479] The server 901 can also send a request to multiple client devices 902 existing in a certain space to send sensor information simultaneously. When the server 901 receives multiple sensor information from the multiple client devices 902, it is not necessary to utilize all the sensor information for the production of the three-dimensional data 1134. For example, the sensor information to be utilized can be selected according to the performance of the sensors. For example, when the server 901 updates the three-dimensional map 1135, it can select high-precision sensor information (category 1) from the received multiple sensor information and use the selected sensor information to produce the three-dimensional data 1134.

[0480] The server 901 is not limited to servers such as traffic cloud monitoring, and can also be other client devices (in-vehicle). Fig.35 The system configuration in this case is shown.

[0481] For example, the client device 902C sends a request to the client device 902A existing nearby to send sensor information and obtains the sensor information from the client device 902A. Then, the client device 902C uses the obtained sensor information of the client device 902A to produce three-dimensional data and updates the three-dimensional map of the client device 902C. In this way, the client device 902C can utilize the performance of the client device 902C to generate a three-dimensional map of the space that can be obtained from the client device 902A. For example, this situation can be considered when the performance of the client device 902C is high.

[0482] Moreover, in this case, the client device 902A that provides the sensor information is given the right to obtain the high-precision three-dimensional map generated by the client device 902C. The client device 902A receives the high-precision three-dimensional map from the client device 902C according to this right.

[0483] It can also be that the client device 902C sends a request to multiple client devices 902 (client device 902A and client device 902B) existing nearby to send sensor information. When the sensor of the client device 902A or the client device 902B is of high performance, the client device 902C can use the sensor information obtained through this high-performance sensor to produce three-dimensional data.

[0484] Fig.36 It is a block diagram showing the functional configurations of the server 901 and the client device 902. The server 901 includes, for example: a three-dimensional map compression / decoding processing unit 1201 for compressing and decoding the three-dimensional map, and a sensor information compression / decoding processing unit 1202 for compressing and decoding the sensor information.

[0485] The client device 902 includes: a three-dimensional map decoding processing unit 1211 and a sensor information compression processing unit 1212. The three-dimensional map decoding processing unit 1211 receives the encoded data of the compressed three-dimensional map, decodes the encoded data, and obtains the three-dimensional map. The sensor information compression processing unit 1212 does not compress the three-dimensional data created from the obtained sensor information, but compresses the sensor information itself, and sends the encoded data of the compressed sensor information to the server 901. With this configuration, the client device 902 can keep the processing unit (device or LSI) for decoding the three-dimensional map (point cloud, etc.) inside, without having to keep the processing unit for compressing the three-dimensional data of the three-dimensional map (point cloud, etc.) inside. In this way, the cost and power consumption of the client device 902 can be suppressed.

[0486] As described above, the client device 902 according to this embodiment is mounted on a moving body, and creates three-dimensional data 1034 of the periphery of the moving body based on the sensor information 1033 showing the peripheral conditions of the moving body obtained by the sensor 1015 mounted on the moving body. The client device 902 estimates its own position using the created three-dimensional data 1034. The client device 902 sends the obtained sensor information 1033 to the server 901 or another moving body 902.

[0487] Accordingly, the client device 902 sends the sensor information 1033 to the server 901 or the like. In this way, there is a possibility that the amount of data to be sent can be reduced compared to the case of sending three-dimensional data. Also, since there is no need to perform processing such as compression or encoding of three-dimensional data on the client device 902, the amount of processing on the client device 902 can be reduced. Therefore, the client device 902 can achieve a reduction in the amount of data transmitted or a simplification of the device configuration.

[0488] In addition, the client device 902 further sends a transmission request for the three-dimensional map to the server 901, and receives the three-dimensional map 1031 from the server 901. The client device 902 estimates its own position using the three-dimensional data 1034 and the three-dimensional map 1032 in the estimation of its own position.

[0489] Moreover, the sensor information 1033 includes at least one of the information obtained by a laser sensor, a luminance image (visible light image), an infrared image, a depth image, the position information of the sensor, and the speed information of the sensor.

[0490] Furthermore, the sensor information 1033 includes information showing the performance of the sensor.

[0491] Furthermore, the client device 902 encodes or compresses the sensor information 1033, and in the transmission of the sensor information, it sends the encoded or compressed sensor information 1037 to the server 901 or another moving body 902. Accordingly, the client device 902 can reduce the amount of data transmitted.

[0492] For example, the client device 902 includes a processor and a memory, and the processor uses the memory to perform the above processing.

[0493] Moreover, the server 901 according to this embodiment can communicate with the client device 902 mounted on the moving body, and receive the sensor information 1037 obtained by the sensor 1015 mounted on the moving body and showing the surrounding conditions of the moving body. The server 901 creates three-dimensional data 1134 of the surrounding of the moving body based on the received sensor information 1037.

[0494] Accordingly, the server 901 uses the sensor information 1037 sent from the client device 902 to create the three-dimensional data 1134. In this way, compared with the case where the client device 902 sends three-dimensional data, there is a possibility of reducing the amount of data to be sent. And since it is not necessary to perform processing such as compression or encoding of three-dimensional data on the client device 902, the processing amount of the client device 902 can be reduced. In this way, the server 901 can achieve a reduction in the amount of data transmitted or a simplification of the device configuration.

[0495] Furthermore, the server 901 further sends a transmission request for the sensor information to the client device 902.

[0496] Furthermore, the server 901 further uses the created three-dimensional data 1134 to update the three-dimensional map 1135, and according to the transmission request of the three-dimensional map 1135 from the client device 902, sends the three-dimensional map 1135 to the client device 902.

[0497] Moreover, the sensor information 1037 includes at least one of the information obtained by a laser sensor, a luminance image (visible light image), an infrared image, a depth image, the position information of the sensor, and the speed information of the sensor.

[0498] Moreover, the sensor information 1037 includes information showing the performance of the sensor.

[0499] Furthermore, the server 901 further corrects the three-dimensional data according to the performance of the sensor. Accordingly, this method for creating three-dimensional data can improve the quality of the three-dimensional data.

[0500] Further, in receiving the sensor information, the server 901 receives a plurality of pieces of sensor information 1037 from a plurality of client devices 902, and selects the sensor information 1037 to be used in the production of the three-dimensional data 1134 based on a plurality of pieces of information included in the plurality of sensor information 1037 that indicate the performance of the sensors. Accordingly, the server 901 can improve the quality of the three-dimensional data 1134.

[0501] Further, the server 901 decodes or decompresses the received sensor information 1037, and produces three-dimensional data 1134 based on the decoded or decompressed sensor information 1132. Accordingly, the server 901 can reduce the amount of data transmitted.

[0502] For example, the server 901 includes a processor and a memory, and the processor uses the memory to perform the above-described processing.

[0503] (Embodiment 7)

[0504] In the present embodiment, a method for encoding and decoding three-dimensional data using inter-frame prediction processing will be described.

[0505] Fig.37 is a block diagram of a three-dimensional data encoding apparatus 1300 according to the present embodiment. The three-dimensional data encoding apparatus 1300 generates an encoded bitstream (hereinafter also simply referred to as a bitstream) as an encoded signal by encoding three-dimensional data. As Fig.37 shown, the three-dimensional data encoding apparatus 1300 includes: a division unit 1301, a subtraction unit 1302, a transformation unit 1303, a quantization unit 1304, an inverse quantization unit 1305, an inverse transformation unit 1306, an addition unit 1307, a reference volume memory 1308, an intra-frame prediction unit 1309, a reference space memory 1310, an inter-frame prediction unit 1311, a prediction control unit 1312, and an entropy encoding unit 1313.

[0506] The division unit 1301 divides each space (SPC) included in the three-dimensional data into a plurality of volumes (VLM) as encoding units. Further, the division unit 1301 performs octree representation (Octree conversion) on the voxels within each volume. In addition, the division unit 1301 may make the space and the volume the same size and perform octree representation on the space. Further, the division unit 1301 may attach information (such as depth information) required for octree conversion to the head of the bitstream or the like.

[0507] The subtraction unit 1302 calculates the difference between the volume (encoding target volume) output from the division unit 1301 and the predicted volume generated by intra-frame prediction or inter-frame prediction described later, and outputs the calculated difference as a prediction residual to the transformation unit 1303. Fig.38An example of calculating the prediction residual is shown. In addition, the bit strings of the volume to be coded and the predicted volume shown here are, for example, position information indicating the positions of three-dimensional points (e.g., point cloud) included in the volume.

[0508] Hereinafter, the octree representation and the scanning order of voxels will be described. After the volume is transformed into an octree structure (octree conversion), it is coded. The octree structure is composed of nodes and leaf nodes. Each node has eight nodes or leaf nodes, and each leaf node has voxel (VXL) information. Fig.39 A configuration example of a volume including a plurality of voxels is shown. Fig.40 Shows Fig.39 An example of converting the volume shown into an octree structure. Here, Fig.40 Among the leaf nodes shown, leaf nodes 1, 2, and 3 respectively represent Fig.39 the voxels VXL1, VXL2, and VXL3 shown, and represent the VXL including the point group (hereinafter referred to as the effective VXL).

[0509] The octree is represented, for example, by a binary sequence of 0 and 1. For example, when a node or an effective VXL is set to a value of 1 and the rest are set to a value of 0, the binary sequence shown is assigned to each node and leaf node. Then, in accordance with the breadth-first or depth-first scanning order, this binary sequence is scanned. For example, when scanning is performed in a breadth-first manner, the binary sequence shown in A of Fig.40 is obtained. When scanning is performed in a depth-first manner, the binary sequence shown in B of Fig.41 is obtained. The binary sequence obtained by this scanning is coded by entropy coding, thereby reducing the amount of information. Fig.41 The binary sequence obtained by this scanning is coded by entropy coding, thereby reducing the amount of information.

[0510] Next, the depth information in the octree representation will be described. The depth in the octree representation is used for controlling up to which granularity the point cloud information included in the volume is maintained. If the depth is set large, the point cloud information can be reproduced at a finer level, but the amount of data for representing nodes and leaf nodes will increase. On the contrary, if the depth is set small, although the amount of data can be reduced, point cloud information at multiple different positions and with different colors will be regarded as being at the same position and having the same color, so the information originally possessed by the point cloud information will be lost.

[0511] For example, Fig.42 Shows Fig.40 An example of representing an octree with a depth of 2 shown as an octree with a depth of 1. Fig.42 The octree shown has less data volume than Fig.40 the octree shown. That is, Fig.42 the octree shown and Fig.42Compared with the octree shown, the number of bits after binary serialization is less. Fig.40 The leaf nodes 1 and 2 shown in the figure become Fig.41 The leaf node 1 shown is shown. That is, Fig.40 The leaf node 1 and the leaf node 2 shown are information of different locations.

[0512] Fig.43 Shown with Fig.42 The volume corresponding to the octree shown. Fig.39 The VXL1 and VXL2 shown are Fig.43 In this case, the three-dimensional data encoding device 1300 corresponds to VXL12 shown in FIG. Fig.39 The color information of VXL1 and VXL2 shown in the figure generates Fig.43 For example, the three-dimensional data encoding device 1300 calculates the color information of VXL1 and VXL2 as the color information of VXL12 using the average value, median value, or weighted average value. In this way, the three-dimensional data encoding device 1300 can control the reduction of the data amount by changing the depth of the octree.

[0513] The three-dimensional data encoding device 1300 may also use any one of the world space units, space units, and volume units to set the depth information of the octree. In addition, at this time, the three-dimensional data encoding device 1300 may also attach the depth information to the header information of the world space, the header information of the space, or the header information of the volume. In addition, the same value may be used as the depth information in all world spaces, spaces, and volumes at different times. In this case, the three-dimensional data encoding device 1300 may also attach the depth information to the header information that manages the world space at all times.

[0514] When the voxel contains color information, the transformation unit 1303 applies a frequency transformation such as an orthogonal transformation to the prediction residual of the color information of the voxels in the volume. For example, the transformation unit 1303 scans the prediction residual in a certain scanning order to produce a one-dimensional arrangement. Thereafter, the transformation unit 1303 transforms the one-dimensional arrangement into the frequency domain by applying a one-dimensional orthogonal transformation to the produced one-dimensional arrangement. Accordingly, when the value of the prediction residual in the volume is close, the value of the frequency component of the low frequency band becomes larger, and the value of the frequency component of the high frequency band becomes smaller. Therefore, the quantization unit 1304 can more effectively reduce the amount of coding.

[0515] Also, the transformation unit 1303 may use an orthogonal transformation of two or more dimensions instead of a one-dimensional orthogonal transformation. For example, the transformation unit 1303 maps the prediction residual to a two-dimensional arrangement in a certain scanning order, and applies a two-dimensional orthogonal transformation to the obtained two-dimensional arrangement. Also, the transformation unit 1303 may select the orthogonal transformation method to be used from among a plurality of orthogonal transformation methods. In this case, the three-dimensional data encoding device 1300 attaches information indicating which orthogonal transformation method is used to the bitstream. It may also be that the transformation unit 1303 selects the orthogonal transformation method to be used from among a plurality of orthogonal transformation methods with different dimensions. In this case, the three-dimensional data encoding device 1300 attaches information indicating which dimensional orthogonal transformation method is used to the bitstream.

[0516] For example, the transformation unit 1303 matches the scanning order of the prediction residual with the scanning order (such as breadth-first or depth-first) in the octree within the volume. Accordingly, since there is no need to attach information indicating the scanning order of the prediction residual to the bitstream, the overhead can be reduced. Also, the transformation unit 1303 may apply a scanning order different from the scanning order of the octree. In this case, the three-dimensional data encoding device 1300 attaches information indicating the scanning order of the prediction residual to the bitstream. Accordingly, the three-dimensional data encoding device 1300 can efficiently encode the prediction residual. It may also be that the three-dimensional data encoding device 1300 attaches information (such as a flag) indicating whether the scanning order of the octree is applied to the bitstream, and in the case where the scanning order of the octree is not applied, attaches information indicating the scanning order of the prediction residual to the bitstream.

[0517] The transformation unit 1303 can transform not only the prediction residual of the color information but also other attribute information possessed by the voxels. For example, it may be that the transformation unit 1303 transforms and encodes information such as reflectance obtained when acquiring point clouds through LiDAR or the like.

[0518] When the space does not have attribute information such as color information, the transformation unit 1303 may skip the processing. Also, the three-dimensional data encoding device 1300 may attach information (a flag) indicating whether to skip the processing of the transformation unit 1303 to the bitstream.

[0519] The quantization unit 1304 quantizes the frequency components of the prediction residuals generated by the transformation unit 1303 using quantization control parameters, thereby generating quantization coefficients. This reduces the amount of information. The generated quantization coefficients are output to the entropy encoding unit 1313. The quantization unit 1304 can control the quantization control parameters in world space units, spatial units, or volume units. At this time, the three-dimensional data encoding device 1300 attaches the quantization control parameters to their respective header information, etc. Also, the quantization unit 1304 can change the weights for quantization control according to the frequency components of each prediction residual. For example, the quantization unit 1304 can perform fine quantization on low-frequency components and rough quantization on high-frequency components. In this case, the three-dimensional data encoding device 1300 can attach parameters representing the weights of the respective frequency components to the header.

[0520] When the quantization unit 1304 does not have attribute information such as color information in space, the processing can be skipped. Also, the three-dimensional data encoding device 1300 can attach information (flag) indicating whether the processing of the quantization unit 1304 is skipped to the bitstream.

[0521] The inverse quantization unit 1305 performs inverse quantization on the quantization coefficients generated by the quantization unit 1304 using the quantization control parameters, thereby generating inverse quantization coefficients of the prediction residuals, and outputs the generated inverse quantization coefficients to the inverse transformation unit 1306.

[0522] The inverse transformation unit 1306 applies an inverse transformation to the inverse quantization coefficients generated by the inverse quantization unit 1305, thereby generating a prediction residual after the inverse transformation is applied. Since this prediction residual after the inverse transformation is the prediction residual generated after quantization, it may not be exactly the same as the prediction residual output by the transformation unit 1303.

[0523] The addition unit 1307 adds the prediction volume generated by the inverse transformation unit 1306 after the inverse transformation is applied and the prediction volume used in the generation of the prediction residual before quantization and generated by intra-frame prediction or inter-frame prediction described later to generate a reconstructed volume. This reconstructed volume is stored in the reference volume memory 1308 or the reference space memory 1310.

[0524] The intra-frame prediction unit 1309 generates a prediction volume of the volume to be encoded using the attribute information of adjacent volumes stored in the reference volume memory 1308. The attribute information includes voxel color information or reflectance. The intra-frame prediction unit 1309 generates a predicted value of the color information or reflectance of the volume to be encoded.

[0525] Fig.44 is a diagram for explaining the operation of the intra-frame prediction unit 1309. For example, Fig.44As shown, the intra prediction unit 1309 generates a predicted volume of the encoding target volume (volume idx = 3) based on an adjacent volume (volume idx = 0). Here, the volume idx is identifier information attached to volumes in space, and different values are assigned to each volume. The order of assignment of the volume idx may be the same as the encoding order or different from the encoding order. For example, as Fig.44 the predicted value of the color information of the encoding target volume shown, the intra prediction unit 1309 uses the average value of the color information of the voxels included in the volume with volume idx = 0 which is an adjacent volume. In this case, by subtracting the predicted value of the color information from the color information of each voxel included in the encoding target volume, a prediction residual is generated. The processes after the transform unit 1303 are performed on this prediction residual. And, in this case, the three-dimensional data encoding device 1300 attaches adjacent volume information and prediction mode information to the bitstream. Here, the adjacent volume information is information showing the adjacent volume used in the prediction, for example, showing the volume idx of the adjacent volume used in the prediction. And, the prediction mode information shows the mode used in the generation of the predicted volume. The mode is, for example, an average value mode that generates a predicted value based on the average value of the voxels in the adjacent volume, or a median value mode that generates a predicted value based on the median value of the voxels in the adjacent volume, etc.

[0526] The intra prediction unit 1309 may also generate a predicted volume based on multiple adjacent volumes. For example, in the Fig.44 configuration shown, the intra prediction unit 1309 generates a predicted volume 0 based on the volume with volume idx = 0, and generates a predicted volume 1 based on the volume with volume idx = 1. Then, the intra prediction unit 1309 generates the average of the predicted volume 0 and the predicted volume 1 as the final predicted volume. In this case, the three-dimensional data encoding device 1300 may also attach the multiple volume idxs of the multiple volumes used in the generation of the predicted volume to the bitstream.

[0527] Fig.45 The inter prediction process according to this embodiment is shown in terms of the mode. The inter prediction unit 1311 encodes (inter prediction) the space (SPC) at a certain time T_Cur using the encoded spaces at different times T_LX. In this case, the inter prediction unit 1311 applies rotation and translation processes to the encoded spaces at different times T_LX for encoding processing.

[0528] Further, the three-dimensional data encoding device 1300 attaches RT information related to the rotation and translation processing of the space applicable to different times T_LX to the bitstream. The different times T_LX are, for example, the time T_L0 before a certain time T_Cur. At this time, the three-dimensional data encoding device 1300 may also attach the RT information RT_L0 related to the rotation and translation processing of the space applicable to the time T_L0 to the bitstream.

[0529] Alternatively, the different times T_LX are, for example, the time T_L1 after a certain time T_Cur. At this time, the three-dimensional data encoding device 1300 may attach the RT information RT_L1 related to the rotation and translation processing of the space applicable to the time T_L1 to the bitstream.

[0530] Alternatively, the inter-frame prediction unit 1311 performs encoding (bi-prediction) by referring to the spaces at both different times T_L0 and time T_L1. In this case, the three-dimensional data encoding device 1300 may attach both the RT information RT_L0 and RT_L1 related to the rotation and translation applicable to the spaces respectively to the bitstream.

[0531] In addition, although T_L0 is set as the time before T_Cur and T_L1 is set as the time after T_Cur above, it is not limited thereto. For example, both T_L0 and T_L1 may be the times before T_Cur. Or, both T_L0 and T_L1 may be the times after T_Cur.

[0532] And it may also be that, when the three-dimensional data encoding device 1300 performs encoding by referring to the spaces at multiple different times, it attaches the RT information related to the rotation and translation applicable to each space to the bitstream. For example, the three-dimensional data encoding device 1300 manages the multiple encoded spaces it refers to through two reference lists (L0 list and L1 list). When the first reference space in the L0 list is set as L0R0, the second reference space in the L0 list is set as L0R1, the first reference space in the L1 list is set as L1R0, and the second reference space in the L1 list is set as L1R1, the three-dimensional data encoding device 1300 attaches the RT information RT_L0R0 of L0R0, the RT information RT_L0R1 of L0R1, the RT information RT_L1R0 of L1R0, and the RT information RT_L1R1 of L1R1 to the bitstream. For example, the three-dimensional data encoding device 1300 attaches these RT information to the head of the bitstream, etc.

[0533] Also, when the three-dimensional data encoding device 1300 performs encoding with reference to reference spaces at multiple different times, it determines whether rotation and translation are applied for each reference space. At this time, the three-dimensional data encoding device 1300 can attach information (such as an RT application flag) indicating whether rotation and translation are applied for each reference space to the header information of the bitstream. For example, the three-dimensional data encoding device 1300 calculates RT information and an ICP error value for each reference space to be referred to according to the encoding target space using the ICP (Interactive Closest Point) algorithm. When the ICP error value is equal to or less than a predetermined fixed value, the three-dimensional data encoding device 1300 determines that rotation and translation are not required and sets the RT application flag to OFF (invalid). In addition, when the ICP error value is greater than the above fixed value, the three-dimensional data encoding device 1300 sets the RT application flag to ON (valid) and attaches the RT information to the bitstream.

[0534] Fig.46 An example of the syntax for attaching RT information and an RT application flag to the header is shown. In addition, the number of bits allocated to each syntax can be determined according to the range that the syntax can take. For example, when the number of reference spaces included in the reference list L0 is 8, 3 bits can be allocated to MaxRefSpc_l0. The number of allocated bits can be changed according to the values that each syntax can take, or the number of allocated bits can be fixed regardless of the values that can be taken. When the number of allocated bits is fixed, the three-dimensional data encoding device 1300 can attach the fixed number of bits to other header information.

[0535] Here, Fig.46 The shown MaxRefSpc_l0 indicates the number of reference spaces included in the reference list L0. RT_flag_l0[i] is the RT application flag for the reference space i in the reference list L0. When RT_flag_l0[i] is 1, rotation and translation are applied to the reference space i. When RT_flag_l0[i] is 0, rotation and translation are not applied to the reference space i.

[0536] R_l0[i] and T_l0[i] are the RT information for the reference space i in the reference list L0. R_l0[i] is the rotation information for the reference space i in the reference list L0. The rotation information indicates the content of the applied rotation process, such as a rotation matrix or a quaternion. T_l0[i] is the translation information for the reference space i in the reference list L0. The translation information indicates the content of the applied translation process, such as a translation vector.

[0537] MaxRefSpc_l1 shows the number of reference spaces included in reference list L1. RT_flag_l1[i] is the RT applicability flag for reference space i in reference list L1. When RT_flag_l1[i] is 1, rotation and translation are applied to reference space i. When RT_flag_l1[i] is 0, rotation and translation are not applied to reference space i.

[0538] R_l1[i] and T_l1[i] are the RT information for reference space i in reference list L1. R_l1[i] is the rotation information for reference space i in reference list L1. The rotation information shows the content of the applied rotation process, such as a rotation matrix or quaternion, etc. T_l1[i] is the translation information for reference space i in reference list L1. The translation information shows the content of the applied translation process, such as a translation vector, etc.

[0539] The inter-frame prediction unit 1311 generates a predicted volume of the volume to be encoded by using the information of the encoded reference spaces stored in the reference space memory 1310. As described above, before generating the predicted volume of the volume to be encoded, the inter-frame prediction unit 1311 uses the ICP (Interactive Closest Point) algorithm in the volume to be encoded space and the reference space to find the RT information in order to make the positional relationship between the volume to be encoded space and the entire reference space closer. Then, the inter-frame prediction unit 1311 applies rotation and translation processing to the reference space by using the obtained RT information, thereby obtaining reference space B. After that, the inter-frame prediction unit 1311 generates a predicted volume of the volume to be encoded in the volume to be encoded space by using the information in reference space B. Here, the three-dimensional data encoding device 1300 attaches the RT information used to obtain reference space B to the header information, etc. of the volume to be encoded space.

[0540] In this way, the inter-frame prediction unit 1311 applies rotation and translation processing to the reference space, so that after making the positional relationship between the volume to be encoded space and the entire reference space closer, it uses the information of the reference space to generate a predicted volume. In this way, the accuracy of the predicted volume can be improved. And since the prediction residual can be suppressed, the amount of encoding can be reduced. In addition, although an example of using the volume to be encoded space and the reference space for ICP is shown here, it is not limited thereto. For example, in order to reduce the processing amount, the inter-frame prediction unit 1311 can also use at least one of the volume to be encoded space with the voxel or point cloud number extracted and the reference space with the voxel or point cloud number extracted to perform ICP, thereby finding the RT information.

[0541] Further, when the ICP error value obtained from the result of ICP is smaller than a pre-specified first threshold value, that is, for example, when the positional relationship between the encoding target space and the reference space is close, the inter-frame prediction unit 1311 may determine that rotation and translation processing are not required and may not perform rotation and translation. In such a case, the three-dimensional data encoding apparatus 1300 may not attach RT information to the bitstream, thereby being able to suppress the overhead.

[0542] Further, when the ICP error value is larger than a pre-specified second threshold value, the inter-frame prediction unit 1311 determines that the shape change in space is large, and may apply intra-frame prediction to all volumes of the encoding target space. Hereinafter, the space to which intra-frame prediction is applied is referred to as an intra-frame space. Also, the second threshold value is a value larger than the above-described first threshold value. Also, not limited to ICP, any method may be applicable as long as it is a method for obtaining RT information from two voxel sets or two point cloud sets.

[0543] Further, when the three-dimensional data includes attribute information such as shape or color, as the predicted volume of the encoding target volume within the encoding target space, the inter-frame prediction unit 1311 searches, for example, for the volume within the reference space that is closest to the shape or color attribute information of the encoding target volume. Also, the reference space is, for example, the reference space after the above-described rotation and translation processing. The inter-frame prediction unit 1311 generates a predicted volume based on the volume (reference volume) obtained through the search. Fig.47 is a diagram for explaining the generation operation of the predicted volume. When the inter-frame prediction unit 1311 encodes the shown encoding target volume (volume idx = 0) using inter-frame prediction, while sequentially scanning the reference volumes within the reference space, it searches for the volume for which the difference between the encoding target volume and the reference volume, that is, the prediction residual, is the smallest. The inter-frame prediction unit 1311 selects the volume with the smallest prediction residual as the predicted volume. The prediction residual between the encoding target volume and the predicted volume is encoded by the processing after the transform unit 1303. Here, the prediction residual refers to the difference between the attribute information of the encoding target volume and the attribute information of the predicted volume. Also, the three-dimensional data encoding apparatus 1300 attaches the volume idx of the reference volume within the reference space that is referred to as the predicted volume to the head of the bitstream or the like. Fig.47 In the case shown in

[0544] In Fig.47 the shown example, the reference volume with volume idx = 4 in the reference space L0R0 is selected as the predicted volume of the encoding target volume. Then, the prediction residual between the encoding target volume and the reference volume and the reference volume idx = 4 are encoded and attached to the bitstream.

[0545] In addition, although the prediction volume of the attribute information is taken as an example for description here, the same processing can also be performed on the prediction volume of the position information.

[0546] The prediction control unit 1312 controls which of intra prediction and inter prediction is used to encode the encoding target volume. Here, the mode including intra prediction and inter prediction is called the prediction mode. For example, the prediction control unit 1312 calculates, as evaluation values, the prediction residual when the encoding target volume is predicted by intra prediction and the prediction residual when it is predicted by inter prediction, and selects the prediction mode with the smaller evaluation value. Alternatively, the prediction control unit 1312 may perform orthogonal transformation, quantization, and entropy coding on the prediction residual of intra prediction and the prediction residual of inter prediction respectively to calculate the actual coding amount, and use the calculated coding amount as the evaluation value to select the prediction mode. Also, overhead information (such as reference volume idx information) other than the prediction residual may be added to the evaluation value. And, when the encoding target space is predetermined to be encoded in the intra space, the prediction control unit 1312 may usually select intra prediction.

[0547] The entropy coding unit 1313 generates an encoded signal (encoded bitstream) by performing variable-length coding on the quantization coefficients, which are the input from the quantization unit 1304. Specifically, the entropy coding unit 1313 binarizes the quantization coefficients, for example, and performs arithmetic coding on the obtained binary signal.

[0548] Next, a three-dimensional data decoding device that decodes the encoded signal generated by the three-dimensional data encoding device 1300 will be described. Fig.48 FIG. is a block diagram of the three-dimensional data decoding device 1400 according to the present embodiment. The three-dimensional data decoding device 1400 includes: an entropy decoding unit 1401, an inverse quantization unit 1402, an inverse transformation unit 1403, an addition unit 1404, a reference volume memory 1405, an intra prediction unit 1406, a reference space memory 1407, an inter prediction unit 1408, and a prediction control unit 1409.

[0549] The entropy decoding unit 1401 performs variable-length decoding on the encoded signal (encoded bitstream). For example, the entropy decoding unit 1401 performs arithmetic decoding on the encoded signal to generate a binary signal, and generates quantization coefficients based on the generated binary signal.

[0550] The inverse quantization unit 1402 performs inverse quantization on the quantization coefficients input from the entropy decoding unit 1401 using the quantization parameters attached to the bitstream or the like, thereby generating inverse quantization coefficients.

[0551] The inverse transformation unit 1403 performs an inverse transformation on the inverse quantization coefficients input from the inverse quantization unit 1402 to generate a prediction residual. For example, the inverse transformation unit 1403 performs an inverse orthogonal transformation on the inverse quantization coefficients according to the information appended to the bitstream to generate a prediction residual.

[0552] The addition unit 1404 adds the prediction residual generated by the inverse transformation unit 1403 and the prediction volume generated by intra prediction or inter prediction to generate a reconstructed volume. This reconstructed volume is output as decoded three-dimensional data and stored in the reference volume memory 1405 or the reference space memory 1407.

[0553] The intra prediction unit 1406 generates a prediction volume by intra prediction using the reference volume in the reference volume memory 1405 and the information appended to the bitstream. Specifically, the intra prediction unit 1406 obtains prediction mode information and adjacent volume information (such as volume idx) appended to the bitstream, and uses the adjacent volume indicated by the adjacent volume information to generate a prediction volume in the mode indicated by the prediction mode information. In addition, the details of these processes are the same as those of the above intra prediction unit 1309 except that the information appended to the bitstream is used.

[0554] The inter prediction unit 1408 generates a prediction volume by inter prediction using the reference space in the reference space memory 1407 and the information appended to the bitstream. Specifically, the inter prediction unit 1408 uses the RT information of each reference space appended to the bitstream, applies rotation and translation processing to the reference space, and uses the processed reference space to generate a prediction volume. In addition, when the RT application flag for each reference space exists in the bitstream, the inter prediction unit 1408 applies rotation and translation processing to the reference space according to the RT application flag. In addition, the details of the above processes are the same as those of the above inter prediction unit 1311 except that the information appended to the bitstream is used.

[0555] Whether to decode the volume to be decoded by intra prediction or inter prediction will be controlled by the prediction control unit 1409. For example, the prediction control unit 1409 selects intra prediction or inter prediction according to the information appended to the bitstream and indicating the prediction mode to be used. In addition, the prediction control unit 1409 may usually select intra prediction when it is predetermined that the object space to be decoded is decoded in the intra space.

[0556] The following describes a modification example of this embodiment. In this embodiment, although rotation and translation are applied in units of space as an example, rotation and translation can also be applied in smaller units. For example, the three-dimensional data encoding device 1300 can divide the space into sub-spaces and apply rotation and translation in units of sub-spaces. In this case, the three-dimensional data encoding device 1300 generates RT information for each sub-space and attaches the generated RT information to the head of the bitstream or the like. Further, the three-dimensional data encoding device 1300 can apply rotation and translation in units of volume as the encoding unit. In this case, the three-dimensional data encoding device 1300 generates RT information in units of encoding volume and attaches the generated RT information to the head of the bitstream or the like. Moreover, the above can be combined. That is, the three-dimensional data encoding device 1300 can apply rotation and translation in a large unit first and then apply rotation and translation in a smaller unit. For example, the three-dimensional data encoding device 1300 can apply rotation and translation in units of space, and apply different rotations and translations to each of a plurality of volumes included in the obtained space.

[0557] Moreover, in this embodiment, although rotation and translation are applied to the reference space as an example, it is not limited thereto. For example, the three-dimensional data encoding device 1300 can apply a scaling process to change the size of the three-dimensional data. Further, the three-dimensional data encoding device 1300 can also apply any one or two of rotation, translation, and scaling. Moreover, as described above, when processing is applied in different units in multiple stages, the types of processing applied in each unit can be different. For example, rotation and translation can be applied in units of space, and translation can be applied in units of volume.

[0558] In addition, regarding these modification examples, the same can be applied to the three-dimensional data decoding device 1400.

[0559] As described above, the three-dimensional data encoding device 1300 according to this embodiment performs the following processing. Fig.48 It is a flowchart of the inter-frame prediction process performed by the three-dimensional data encoding device 1300.

[0560] First, the three-dimensional data encoding device 1300 generates prediction position information (for example, a prediction volume) using the position information of three-dimensional points included in the object three-dimensional data (for example, the encoding object space) and the reference three-dimensional data (for example, the reference space) at different times (S1301). Specifically, the three-dimensional data encoding device 1300 generates prediction position information by applying rotation and translation processing to the position information of the three-dimensional points included in the reference three-dimensional data.

[0561] In addition, the three-dimensional data encoding device 1300 performs rotation and translation processing in units of a first unit (e.g., a space), and generates predicted position information in units of a second unit (e.g., a volume) that is finer than the first unit. For example, the three-dimensional data encoding device 1300 searches for the volume in the reference space after rotation and translation processing among a plurality of volumes included in the reference space, where the difference between the encoded object volume included in the encoding object space and the position information is minimized, and uses the obtained volume as the predicted volume. In addition, the three-dimensional data encoding device 1300 may perform rotation and translation processing and generation of predicted position information in the same unit.

[0562] Moreover, the three-dimensional data encoding device 1300 may apply first rotation and translation processing to the position information of the three-dimensional points included in the reference three-dimensional data in units of a first unit (e.g., a space), and apply second rotation and translation processing to the position information of the three-dimensional points obtained by the first rotation and translation processing in units of a second unit (e.g., a volume) that is finer than the first unit, thereby generating predicted position information.

[0563] Here, the position information of the three-dimensional points and the predicted position information are represented in an octree structure as Fig.41 shown. For example, the position information of the three-dimensional points and the predicted position information are represented in a width-first scan order of the depth and width in the octree structure. Alternatively, the position information of the three-dimensional points and the predicted position information are represented in a depth-first scan order of the depth and width in the octree structure.

[0564] Furthermore, as Fig.46 shown, the three-dimensional data encoding device 1300 encodes an RT application flag indicating whether rotation and translation processing is applied to the position information of the three-dimensional points included in the reference three-dimensional data. That is, the three-dimensional data encoding device 1300 generates an encoded signal (encoded bitstream) including the RT application flag. In addition, the three-dimensional data encoding device 1300 encodes RT information indicating the content of the rotation and translation processing. That is, the three-dimensional data encoding device 1300 generates an encoded signal (encoded bitstream) including the RT information. Alternatively, the three-dimensional data encoding device 1300 may encode the RT information when the RT application flag indicates that rotation and translation processing is applied, and may not encode the RT information when the RT application flag indicates that rotation and translation processing is not applied.

[0565] Moreover, the three-dimensional data includes, for example, the position information of the three-dimensional points and the attribute information (such as color information) of each three-dimensional point. The three-dimensional data encoding device 1300 generates predicted attribute information (S1302) using the attribute information of the three-dimensional points included in the reference three-dimensional data.

[0566] Next, the three-dimensional data encoding device 1300 uses the predicted position information to encode the position information of the three-dimensional points included in the target three-dimensional data. For example, as shown in Fig.38 , the three-dimensional data encoding device 1300 calculates the difference between the position information of the three-dimensional points included in the target three-dimensional data and the predicted position information, that is, the differential position information (S1303).

[0567] In addition, the three-dimensional data encoding device 1300 uses the predicted attribute information to encode the attribute information of the three-dimensional points included in the target three-dimensional data. For example, the three-dimensional data encoding device 1300 calculates the difference between the attribute information of the three-dimensional points included in the target three-dimensional data and the predicted attribute information, that is, the differential attribute information (S1304). Next, the three-dimensional data encoding device 1300 performs transformation and quantization on the calculated differential attribute information (S1305).

[0568] Finally, the three-dimensional data encoding device 1300 encodes the differential position information and the quantized differential attribute information (for example, entropy encoding) (S1306). That is, the three-dimensional data encoding device 1300 generates an encoded signal (encoded bitstream) including the differential position information and the differential attribute information.

[0569] In addition, when the attribute information is not included in the three-dimensional data, the three-dimensional data encoding device 1300 may not perform steps S1302, S1304, and S1305. Moreover, the three-dimensional data encoding device 1300 may only perform one of the encoding of the position information of the three-dimensional points and the encoding of the attribute information of the three-dimensional points.

[0570] Moreover, Fig.49 the order of the processes shown is only an example and is not limited thereto. For example, since the processing for the position information (S1301, S1303) and the processing for the attribute information (S1302, S1304, S1305) are independent of each other, they can be executed in any order, or a part of them can be processed in parallel.

[0571] As described above, in the present embodiment, the three-dimensional data encoding device 1300 uses the position information of the three-dimensional points included in the target three-dimensional data and the reference three-dimensional data at different times to generate the predicted position information, and encodes the difference between the position information of the three-dimensional points included in the target three-dimensional data and the predicted position information, that is, the differential position information. Accordingly, since the data amount of the encoded signal can be reduced, the encoding efficiency can be improved.

[0572] Further, in the present embodiment, the three-dimensional data encoding device 1300 generates predicted attribute information using the attribute information of the three-dimensional points included in the reference three-dimensional data, and encodes the difference, i.e., the differential attribute information, between the attribute information of the three-dimensional points included in the object three-dimensional data and the predicted attribute information. Accordingly, since the data amount of the encoded signal can be reduced, the encoding efficiency can be improved.

[0573] For example, the three-dimensional data encoding device 1300 includes a processor and a memory, and the processor uses the memory to perform the above-described processing.

[0574] Fig.48 It is a flowchart of the inter-frame prediction process performed by the three-dimensional data decoding device 1400.

[0575] First, the three-dimensional data decoding device 1400 decodes (e.g., entropy decodes) the differential position information and the differential attribute information from the encoded signal (encoded bitstream) (S1401).

[0576] Further, the three-dimensional data decoding device 1400 decodes the RT application flag indicating whether rotation and translation processing are applicable to the position information of the three-dimensional points included in the reference three-dimensional data from the encoded signal. Also, the three-dimensional data decoding device 1400 decodes the RT information indicating the details of the rotation and translation processing. Additionally, the three-dimensional data decoding device 1400 decodes the RT information when the RT application flag indicates that rotation and translation processing are applicable, and does not decode the RT information when the RT application flag indicates that rotation and translation processing are not applicable.

[0577] Next, the three-dimensional data decoding device 1400 performs inverse quantization and inverse transformation on the decoded differential attribute information (S1402).

[0578] Next, the three-dimensional data decoding device 1400 generates predicted position information (e.g., predicted volume) using the position information of the three-dimensional points included in the object three-dimensional data (e.g., decoding target space) and the reference three-dimensional data at a different time (e.g., reference space) (S1403). Specifically, the three-dimensional data decoding device 1400 generates the predicted position information by applying rotation and translation processing to the position information of the three-dimensional points included in the reference three-dimensional data.

[0579] More specifically, the three-dimensional data decoding device 1400 applies rotation and translation processing to the position information of the three-dimensional points included in the reference three-dimensional data indicated by the RT information when the RT application flag indicates that rotation and translation processing are applicable. Also, the three-dimensional data decoding device 1400 does not apply rotation and translation processing to the position information of the three-dimensional points included in the reference three-dimensional data when the RT application flag indicates that rotation and translation processing are not applicable.

[0580] In addition, the three-dimensional data decoding device 1400 can perform rotation and translation processing in a first unit (e.g., space), and can generate predicted position information in a second unit (e.g., volume) that is finer than the first unit. In addition, the three-dimensional data decoding device 1400 can also perform rotation and translation processing and generation of predicted position information in the same unit.

[0581] Specifically, the three-dimensional data decoding device 1400 applies first rotation and translation processing to the position information of the three-dimensional points included in the reference three-dimensional data in a first unit (e.g., space), and applies second rotation and translation processing to the position information of the three-dimensional points obtained by the first rotation and translation processing in a second unit (e.g., volume) that is finer than the first unit, thereby generating predicted position information.

[0582] Here, the position information of the three-dimensional points and the predicted position information are, for example, Fig.41 represented in an octree structure as shown. For example, the position information of the three-dimensional points and the predicted position information are represented in a scan order that gives priority to the width among the depth and width in the octree structure. Alternatively, the position information of the three-dimensional points and the predicted position information are represented in a scan order that gives priority to the depth among the depth and width in the octree structure.

[0583] The three-dimensional data decoding device 1400 generates predicted attribute information by using the attribute information of the three-dimensional points included in the reference three-dimensional data (S1404).

[0584] Next, the three-dimensional data decoding device 1400 decodes the encoded position information included in the encoded signal by using the predicted position information, thereby restoring the position information of the three-dimensional points included in the target three-dimensional data. Here, the encoded position information is, for example, differential position information, and the three-dimensional data decoding device 1400 restores the position information of the three-dimensional points included in the target three-dimensional data by adding the differential position information and the predicted position information (S1405).

[0585] In addition, the three-dimensional data decoding device 1400 decodes the encoded attribute information included in the encoded signal by using the predicted attribute information, thereby restoring the attribute information of the three-dimensional points included in the target three-dimensional data. Here, the encoded attribute information is, for example, differential attribute information, and the three-dimensional data decoding device 1400 restores the attribute information of the three-dimensional points included in the target three-dimensional data by adding the differential attribute information and the predicted attribute information (S1406).

[0586] Alternatively, when the attribute information is not included in the three-dimensional data, the three-dimensional data decoding apparatus 1400 may also not execute steps S1402, S1404, and S1406. Moreover, the three-dimensional data decoding apparatus 1400 may also perform only one of the decoding of the position information of the three-dimensional points and the decoding of the attribute information of the three-dimensional points.

[0587] Moreover, Fig.50 The order of the processes shown is an example and is not limited thereto. For example, since the processes for the position information (S1403, S1405) and the processes for the attribute information (S1402, S1404, S1406) are independent of each other, they can be performed in any order, and a part of them can also be processed in parallel.

[0588] (Embodiment 8)

[0589] In the present embodiment, a method for representing three-dimensional points (point cloud) in the encoding of three-dimensional data will be described.

[0590] Fig.51 FIG. is a block diagram showing the configuration of a three-dimensional data distribution system according to the present embodiment. Fig.51 The distribution system shown includes a server 1501 and a plurality of clients 1502.

[0591] The server 1501 includes a storage unit 1511 and a control unit 1512. The storage unit 1511 stores the encoded three-dimensional data, i.e., the encoded three-dimensional map 1513.

[0592] Fig.52 FIG. shows a configuration example of the bit stream of the encoded three-dimensional map 1513. The three-dimensional map is divided into a plurality of sub-maps, and each sub-map is encoded. A random access header (RA) including sub-coordinate information is attached to each sub-map. The sub-coordinate information is used to improve the encoding efficiency of the sub-map. The sub-coordinate information shows the sub-coordinates of the sub-map. The sub-coordinates are the coordinates of the sub-map based on a reference coordinate. In addition, a three-dimensional map including a plurality of sub-maps is called a global map. And, in the global map, the coordinate (e.g., the origin) serving as a reference is called the reference coordinate. That is, the sub-coordinates are the coordinates of the sub-map in the coordinate system of the global map. In other words, the sub-coordinates show the deviation between the coordinate system of the global map and the coordinate system of the sub-map. And, the coordinates in the coordinate system of the global map based on the reference coordinate are called global coordinates. The coordinates in the coordinate system of the sub-map based on the sub-coordinates are called differential coordinates.

[0593] Client 1502 sends a message to server 1501. The message includes the location information of client 1502. The control unit 1512 included in server 1501 obtains the bitstream of the sub-map at the location closest to the location of client 1502 based on the location information included in the received message. The bitstream of the sub-map includes sub-coordinate information and is sent to client 1502. The decoder 1521 included in client 1502 uses this sub-coordinate information to obtain the overall coordinates of the sub-map based on the reference coordinates. The application 1522 included in client 1502 executes an application related to its own location using the obtained overall coordinates of the sub-map.

[0594] Moreover, the sub-map shows a partial area of the overall map. The sub-coordinates are the coordinates of the location where the sub-map is located in the reference coordinate space of the overall map. For example, it is considered that there is a sub-map A of AA and a sub-map B of AB in the overall map of A. When the vehicle wants to refer to the map of AA, it starts decoding from sub-map A, and when it wants to refer to the map of AB, it starts decoding from sub-map B. Here, the sub-map is a random access point. Specifically, A is Osaka Prefecture, AA is Osaka City, AB is Takatsuki City, etc.

[0595] Each sub-map is sent to the client together with the sub-coordinate information. The sub-coordinate information is included in the header information of each sub-map, or in the transmitted data packet, etc.

[0596] The reference coordinates, which are the coordinates serving as the basis for the sub-coordinate information of each sub-map, can also be attached to the header information of a space higher than the sub-map, such as the header information of the overall map.

[0597] The sub-map can be composed of one space (SPC). Moreover, the sub-map can also be composed of multiple SPCs.

[0598] Moreover, the sub-map can also include a GOS (Group of Space). Moreover, the sub-map can also be composed of a world space. For example, in the case where there are multiple objects in the sub-map, if the multiple objects are assigned to different SPCs, the sub-map is composed of multiple SPCs. And if the multiple objects are assigned to one SPC, the sub-map is composed of one SPC.

[0599] Next, the improvement effect of the coding efficiency in the case of adopting the sub-coordinate information will be described. Fig.53 This is a figure for explaining this effect. For example, in order to Fig.53Encoding the three-dimensional point A at a position far from the reference coordinates as shown requires a larger number of bits. Here, the distance between the sub-coordinates and the three-dimensional point A is shorter than the distance between the reference coordinates and the three-dimensional point A. Therefore, compared with the case of encoding the coordinates of the three-dimensional point A based on the reference coordinates, the encoding efficiency can be improved when encoding the coordinates of the three-dimensional point A based on the sub-coordinates. Also, the bitstream of the sub-map includes sub-coordinate information. By sending the bitstream of the sub-map and the reference coordinates to the decoding side (client), the entire coordinates of the sub-map can be restored on the decoding side.

[0600] Fig.54 It is a flowchart of the process performed by the server 1501, which is the sending side of the sub-map.

[0601] First, the server 1501 receives a message (S1501) including the position information of the client 1502 from the client 1502. The control unit 1512 obtains the encoded bitstream of the sub-map based on the position information of the client from the storage unit 1511 (S1502). Then, the server 1501 sends the encoded bitstream of the sub-map and the reference coordinates to the client 1502 (S1503).

[0602] Fig.55 It is a flowchart of the process performed by the client 1502, which is the receiving side of the sub-map.

[0603] First, the client 1502 receives the encoded bitstream of the sub-map and the reference coordinates sent from the server 1501 (S1511). Next, the client 1502 obtains the sub-map and sub-coordinate information by decoding the encoded bitstream (S1512). Next, the client 1502 restores the differential coordinates in the sub-map to the entire coordinates using the reference coordinates and the sub-coordinates (S1513).

[0604] Next, a syntactic example of the information related to the sub-map will be described. In the encoding of the sub-map, the three-dimensional data encoding device calculates the differential coordinates by subtracting the sub-coordinates from the coordinates of each point cloud (three-dimensional point). Then, the three-dimensional data encoding device encodes the differential coordinates as a bitstream as the value of each point cloud. Also, the encoding device encodes the sub-coordinate information indicating the sub-coordinates as the header information of the bitstream. Accordingly, the three-dimensional data decoding device can obtain the entire coordinates of each point cloud. For example, the three-dimensional data encoding device is included in the server 1501, and the three-dimensional data decoding device is included in the client 1502.

[0605] Fig.56 A syntactic example of the sub-map is shown. Fig.56The NumOfPoint shown represents the number of point clouds included in the sub-map. sub_coordinate_x, sub_coordinate_y, and sub_coordinate_z are sub-coordinate information. sub_coordinate_x represents the x coordinate of the sub-coordinate. sub_coordinate_y represents the y coordinate of the sub-coordinate. sub_coordinate_z represents the z coordinate of the sub-coordinate.

[0606] Moreover, diff_x[i], diff_y[i], and diff_z[i] are the differential coordinates of the i-th point cloud within the sub-map. diff_x[i] represents the difference value between the x coordinate of the i-th point cloud within the sub-map and the x coordinate of the sub-coordinate. diff_y[i] represents the difference value between the y coordinate of the i-th point cloud within the sub-map and the y coordinate of the sub-coordinate. diff_z[i] represents the difference value between the z coordinate of the i-th point cloud within the sub-map and the z coordinate of the sub-coordinate.

[0607] The three-dimensional data decoding device decodes point_cloud[i]_x, point_cloud[i]_y, and point_cloud[i]_z, which are the overall coordinates of the i-th point cloud, using the following formula. point_cloud[i]_x is the x coordinate of the overall coordinates of the i-th point cloud. point_cloud[i]_y is the y coordinate of the overall coordinates of the i-th point cloud. point_cloud[i]_z is the z coordinate of the overall coordinates of the i-th point cloud.

[0608] point_cloud[i]_x = sub_coordinate_x + diff_x[i]

[0609] point_cloud[i]_y = sub_coordinate_y + diff_y[i]

[0610] point_cloud[i]_z = sub_coordinate_z + diff_z[i]

[0611] Next, the applicable switching process for octree encoding will be described. When encoding the sub-map, the three-dimensional data encoding device either selects octree representation to encode each point cloud (hereinafter referred to as octree encoding (octree encoding)), or selects to encode the difference value from the sub-coordinate (hereinafter referred to as non-octree encoding (non-octree encoding)). Fig.57The operation is shown in terms of a pattern. For example, when the number of point clouds within a sub-map is equal to or greater than a pre-specified threshold, the three-dimensional data encoding device applies octree encoding to the sub-map. When the number of point clouds within the sub-map is smaller than the above-mentioned threshold, the three-dimensional data encoding device applies non-octree encoding to the sub-map. Accordingly, the three-dimensional data encoding device appropriately selects whether to use octree encoding or non-octree encoding according to the shape and density of the object included in the sub-map, and thus the encoding efficiency can be improved.

[0612] In addition, the three-dimensional data encoding device attaches information indicating which of octree encoding and non-octree encoding is applied to the sub-map (hereinafter referred to as octree encoding application information) to the head of the sub-map or the like. Accordingly, the three-dimensional data decoding device can determine whether the bitstream is a bitstream obtained by octree encoding of the sub-map or a bitstream obtained by non-octree encoding of the sub-map.

[0613] Moreover, the three-dimensional data encoding device can calculate the encoding efficiency when octree encoding and non-octree encoding are respectively applied to the same point cloud, and apply the encoding method with higher encoding efficiency to the sub-map.

[0614] Fig.58 A syntax example of the sub-map in the case of performing such a switch is shown. Fig.58 The shown coding_type is information indicating the coding type and is the above-mentioned octree encoding application information. coding_type = 00 indicates that octree encoding is applied. coding_type = 01 indicates that non-octree encoding is applied. coding_type = 10 or 11 indicates that other coding methods than the above are applied, etc.

[0615] When the coding type is non-octree encoding (non_octree), the sub-map includes NumOfPoint and sub-coordinate information (sub_coordinate_x, sub_coordinate_y, and sub_coordinate_z).

[0616] When the coding type is octree encoding (octree), the sub-map includes octree_info. octree_info is information required in octree encoding and includes, for example, depth information, etc.

[0617] When the coding type is non-octree encoding (non_octree), the sub-map includes differential coordinates (diff_x[i], diff_y[i], and diff_z[i]).

[0618] In the case where the encoding type is octree encoding, the sub-map includes encoding data related to octree encoding, i.e., octree_data.

[0619] In addition, here, although an example of using the xyz coordinate system is shown as the coordinate system of the point cloud, a polar coordinate system can also be used.

[0620] Fig.59 It is a flowchart of the three-dimensional data encoding process performed by the three-dimensional data encoding device. First, the three-dimensional data encoding device calculates the number of point clouds in the object sub-map, which is the sub-map to be processed (S1521). Then, the three-dimensional data encoding device determines whether the calculated number of point clouds is equal to or greater than a pre-specified threshold (S1522).

[0621] In the case where the number of point clouds is equal to or greater than the threshold (Yes in S1522), the three-dimensional data encoding device applies octree encoding to the object sub-map (S1523). And the three-dimensional point data encoding device attaches octree encoding application information indicating that octree encoding has been applied to the object sub-map to the head of the bitstream (S1525).

[0622] In addition, in the case where the number of point clouds is lower than the threshold (No in S1522), the three-dimensional data encoding device applies non-octree encoding to the object sub-map (S1524). And the three-dimensional point data encoding device attaches octree encoding application information indicating that non-octree encoding has been applied to the object sub-map to the head of the bitstream (S1525).

[0623] Fig.60 It is a flowchart of the three-dimensional data decoding process performed by the three-dimensional data decoding device. First, the three-dimensional data decoding device decodes the octree encoding application information from the head of the bitstream (S1531). Then, the three-dimensional data decoding device determines whether the encoding type applied to the object sub-map is octree encoding based on the decoded octree encoding application information (S1532).

[0624] In the case where the encoding type indicated by the octree encoding application information is octree encoding (Yes in S1532), the three-dimensional data decoding device decodes the object sub-map using octree decoding (S1533). In addition, in the case where the encoding type indicated by the octree encoding application information is non-octree encoding (No in S1532), the three-dimensional data decoding device decodes the object sub-map using non-octree decoding (S1534).

[0625] A modification example of the present embodiment will be described below. Figure 61 to Figure 63 The operation of a modification example of the encoding type switching process is shown in terms of a mode.

[0626] As Fig.61As shown, the three-dimensional data encoding device can select whether to apply octree encoding or non-octree encoding for each space. In this case, the three-dimensional data encoding device attaches octree encoding applicability information to the head of the space. Accordingly, the three-dimensional data decoding device can determine whether octree encoding is applicable for each space. And, in this case, the three-dimensional data encoding device sets sub-coordinates for each space and encodes the difference value obtained by subtracting the sub-coordinates from the coordinates of each point cloud within the space.

[0627] Accordingly, since the three-dimensional data encoding device can appropriately switch whether to apply octree encoding according to the shape or the number of point clouds of the object within the space, the encoding efficiency can be improved.

[0628] And, as Fig.62 shown, the three-dimensional data encoding device can select whether to apply octree encoding or non-octree encoding for each volume. In this case, the three-dimensional data encoding device attaches octree encoding applicability information to the head of the volume. Accordingly, the three-dimensional data decoding device can determine whether octree encoding is applicable for each volume. And, in this case, the three-dimensional data encoding device sets sub-coordinates for each volume and encodes the difference value obtained by subtracting the sub-coordinates from the coordinates of each point cloud within the volume.

[0629] Accordingly, since the three-dimensional data encoding device can appropriately switch whether to apply octree encoding according to the shape or the number of point clouds of the object within the volume, the encoding efficiency can be improved.

[0630] And, in the above description, as an example of non-octree encoding, an example of encoding the difference obtained by subtracting the sub-coordinates from the coordinates of each point cloud is shown, but it is not limited thereto, and any encoding method other than octree encoding can be used for encoding. For example Fig.63 shown, the three-dimensional data encoding device may not use the difference with the sub-coordinates, but may use a method of encoding the values of the point clouds themselves within the sub-map, space, or volume (hereinafter referred to as original coordinate encoding) as non-octree encoding.

[0631] In this case, the three-dimensional data encoding device stores information indicating that original coordinate encoding is applied to the target space (sub-map, space, or volume) in the head. Accordingly, the three-dimensional data decoding device can determine whether original coordinate encoding is applied to the target space.

[0632] And, in the case of applying original coordinate encoding, the three-dimensional data encoding device can encode the original coordinates without applying quantization and arithmetic encoding. And, the three-dimensional data encoding device can encode the original coordinates with a predetermined fixed bit length. Accordingly, the three-dimensional data encoding device can generate a stream with a certain bit length at a certain timing.

[0633] Also, in the above description, although an example of encoding the difference obtained by subtracting the sub - coordinates from the coordinates of each point cloud is shown as non - octree encoding, it is not limited thereto.

[0634] For example, the three - dimensional data encoding device can successively encode the difference values between the coordinates of each point cloud. Fig.64 FIG. is a diagram for explaining the operation in this case. For example, in Fig.64 the example shown, when the three - dimensional data encoding device encodes the point cloud PA, it uses the sub - coordinates as the prediction coordinates and encodes the difference value between the coordinates of the point cloud PA and the prediction coordinates. Also, when the three - dimensional data encoding device encodes the point cloud PB, it uses the coordinates of the point cloud PA as the prediction coordinates and encodes the difference value between the point cloud PB and the prediction coordinates. And when the three - dimensional data encoding device encodes the point cloud PC, it uses the point cloud PB as the prediction coordinates and encodes the difference value between the point cloud PB and the prediction coordinates. In this way, the three - dimensional data encoding device can set a scanning order for multiple point clouds and encode the difference value between the coordinates of the target point cloud to be processed and the coordinates of the point cloud that is the previous one in the scanning order with respect to the target point cloud.

[0635] Also, in the above description, although the sub - coordinates are the coordinates of the lower - left front corner of the sub - map, the position of the sub - coordinates is not limited thereto. Figure 65 to Figure 67 Other examples of the position of the sub - coordinates are shown. Regarding the setting position of the sub - coordinates, they can be set to any coordinates within the object space (sub - map, space, or volume). That is, as described above, the sub - coordinates can be the coordinates of the lower - left front corner. As Fig.65 shown, the sub - coordinates can also be the coordinates of the center of the object space. As Fig.66 shown, the sub - coordinates can also be the coordinates of the upper - right rear corner of the object space. Also, the sub - coordinates are not limited to the coordinates of the lower - left front or upper - right rear corners of the object space and can be the coordinates of any corner in the object space.

[0636] Also, the setting position of the sub - coordinates can be the same as the coordinates of a certain point cloud within the object space (sub - map, space, or volume). For example, in Fig.67 the example shown, the coordinates of the sub - coordinates are the same as the coordinates of the point cloud PD.

[0637] Also, although an example of switching between octree coding and non-octree coding is shown in this embodiment, it is not limited thereto. For example, the three-dimensional data encoding device may also switch between using a tree structure other than the octree and a non-tree structure other than the tree structure. For example, other tree structures refer to kd-trees and the like that are divided using a plane perpendicular to one of the coordinate axes. Additionally, any method may be adopted as other tree structures.

[0638] Also, although an example of encoding the coordinate information included in the point cloud is shown in this embodiment, it is not limited thereto. The three-dimensional data encoding device may, for example, also encode color information, three-dimensional feature amounts, or feature amounts of visible light in the same method as the coordinate information. For example, the three-dimensional data encoding device may set the average value of the color information of each point cloud within the sub-map as sub-color information, and encode the difference between the color information of each point cloud and the sub-color information.

[0639] Also, although an example of selecting a highly efficient encoding method (octree coding or non-octree coding) according to the number of point clouds, etc. is shown in this embodiment, it is not limited thereto. For example, as the three-dimensional data encoding device on the server side, it may previously hold the bitstreams of the point clouds encoded by octree coding, the bitstreams of the point clouds encoded by non-octree coding, and the bitstreams of the point clouds encoded by both methods, and switch the bitstreams sent to the three-dimensional data decoding device according to the communication environment or the processing ability of the three-dimensional data decoding device.

[0640] Fig.68 A syntactic example of the volume in the case of switching the application of octree coding is shown. Fig.68 The shown syntax is the same as Fig.58 the shown syntax, except that each piece of information is information in volume units. Specifically, NumOfPoint shows the number of point clouds included in the volume. sub_coordinate_x, sub_coordinate_y, and sub_coordinate_z are the sub-coordinate information of the volume.

[0641] Also, diff_x[i], diff_y[i], and diff_z[i] are the differential coordinates of the i-th point cloud within the volume. diff_x[i] represents the difference value between the x coordinate of the i-th point cloud within the volume and the x coordinate of the sub-coordinate. diff_y[i] represents the difference value between the y coordinate of the i-th point cloud within the volume and the y coordinate of the sub-coordinate. diff_z[i] represents the difference value between the z coordinate of the i-th point cloud within the volume and the z coordinate of the sub-coordinate.

[0642] In addition, when the relative positions of volumes in space can be calculated, the three-dimensional data encoding device may not include sub-coordinate information in the headers of the volumes. That is, the three-dimensional data encoding device may calculate the relative positions of volumes in space without including sub-coordinate information in the headers, and use the calculated positions as the sub-coordinates of the respective volumes.

[0643] As described above, the three-dimensional data encoding device according to the present embodiment determines whether to encode an object space unit among a plurality of spatial units (e.g., sub-maps, spaces, or volumes) included in three-dimensional data in an octree structure (e.g., Fig.59 in S1522). For example, when the number of three-dimensional points included in the object space unit is greater than a preset threshold, the three-dimensional data encoding device determines to encode the object space unit in an octree structure. And when the number of three-dimensional points included in the object space unit is equal to or less than the above-mentioned threshold, the three-dimensional data encoding device determines not to encode the object space unit in an octree structure.

[0644] When it is determined to encode the object space unit in an octree structure (Yes in S1522), the three-dimensional data encoding device encodes the object space unit in an octree structure (S1523). And when it is determined not to encode the object space unit in an octree structure (No in S1522), the three-dimensional data encoding device encodes the object space unit in a manner different from the octree structure (S1524). For example, as a different manner, the three-dimensional data encoding device encodes the coordinates of the three-dimensional points included in the object space unit. Specifically, as a different manner, the three-dimensional data encoding device encodes the reference coordinates of the object space unit and the differences between the coordinates of the three-dimensional points included in the object space unit.

[0645] Next, the three-dimensional data encoding device attaches information indicating whether to encode the object space unit in an octree structure to the bitstream (S1525).

[0646] Accordingly, the three-dimensional data encoding device can reduce the data amount of the encoded signal, thereby improving the encoding efficiency.

[0647] For example, the three-dimensional data encoding device includes a processor and a memory, and the processor uses the memory to perform the above processing.

[0648] And the three-dimensional data decoding device according to the present embodiment decodes, from the bitstream, information indicating whether to decode an object space unit among a plurality of object space units (e.g., sub-maps, spaces, or volumes) included in the three-dimensional data (e.g., Fig.60(S1531). When it is shown by the above information that the object space unit is decoded in an octree structure (Yes in S1532), the three-dimensional data decoding device decodes the object space unit in an octree structure (S1533).

[0649] When it is shown by the above information that the object space unit is not decoded in an octree structure (No in S1532), the three-dimensional data decoding device decodes the object space unit in a manner different from the octree structure (S1534). For example, in a different manner, the three-dimensional data decoding device decodes the coordinates of the three-dimensional points included in the object space unit. Specifically, in a different manner, the three-dimensional data decoding device decodes the difference between the reference coordinates of the object space unit and the coordinates of the three-dimensional points included in the object space unit.

[0650] Thus, the three-dimensional data decoding device can reduce the data amount of the encoded signal, and thus can improve the encoding efficiency.

[0651] For example, the three-dimensional data decoding device includes a processor and a memory, and the processor uses the memory to perform the above processing.

[0652] (Embodiment 9)

[0653] In this embodiment, an encoding method of a tree structure such as an octree structure will be described.

[0654] By identifying an important area and preferentially decoding the three-dimensional data of the important area, the efficiency can be improved.

[0655] Fig.69 is a diagram showing an example of an important area in a three-dimensional map. The important area is, for example, an area including three-dimensional points with large feature quantity values among three-dimensional points in a three-dimensional map with a certain number or more. Or, the important area may be, for example, an area including three-dimensional points required for a client such as a vehicle to estimate its own position with a certain number or more. Or, the important area may also be the area of the face in a three-dimensional model of a person. In this way, the important area can be defined for each application program, and the important area can also be switched according to the application program.

[0656] In this embodiment, as a way to represent an octree structure or the like, occupancy coding and location coding are used. In addition, the bit string obtained by occupancy coding is called an occupancy code. The bit string obtained by location coding is called a location code.

[0657] Fig.70This is a diagram showing an example of an occupancy code. Fig.70 An example of an occupancy code representing a quadtree structure. In Fig.70 each node is assigned an occupancy code. Each occupancy code indicates whether the child nodes or leaf nodes of each node contain 3D points. For example, in the case of a quadtree, the information indicating whether each of the four child nodes or leaf nodes of each node contains 3D points is represented by a 4-bit occupancy code. Additionally, in the case of an octree, the information indicating whether each of the eight child nodes or leaf nodes of each node contains 3D points is represented by an 8-bit occupancy code. Here, for the sake of simplicity of explanation, the quadtree structure is used as an example, but it can also be applied equally to the octree structure. For example, as Fig.70 shown, the occupancy code is a bit pattern obtained by scanning the nodes and leaf nodes in breadth-first order as described in Fig.40 etc. In the occupancy code, since the information of multiple 3D points is decoded in a fixed order, it is not possible to preferentially decode the information of arbitrary 3D points. Additionally, the occupancy code can also be a bit string obtained by scanning the nodes and leaf nodes in depth-first order as described in Fig.40 etc.

[0658] Hereinafter, position encoding will be described. By using the position code, it is possible to directly decode important parts in the octree structure. Additionally, it is possible to efficiently encode important 3D points in the deep layer.

[0659] Fig.71 This is a diagram for explaining position encoding and is a diagram showing an example of a quadtree structure. In Fig.71 the example shown, 3D points A to I are represented by a quadtree structure. Additionally, 3D points A and C are important 3D points contained in the important area.

[0660] Fig.72 This is a diagram showing Fig.71 the occupancy code and position code representing important 3D points A and C in the quadtree structure shown.

[0661] In position encoding, in the tree structure, the indices of the nodes and the leaf node index existing in the path from the object node to the leaf node to which the object 3D point to be encoded belongs are encoded. Here, the index is a numerical value assigned to each node and leaf node. In other words, the index is an identifier used to identify the multiple child nodes of the object node. As Fig.71 shown, in the case of a quadtree, the index represents any one of 0 to 3.

[0662] For example, in Fig.71In the quadtree structure shown, when the leaf node A is the three-dimensional point of the object, the leaf node A is represented as 0→2→1→0→1→2→1. Here, when the maximum value of each index is as shown in the right figure, it is 4 (which can be represented by 2 bits), so the number of bits required for the position code of the leaf node A is 7×2 bits = 14 bits. When the leaf node C is the coded object, the same number of bits, 14 bits, is required. In addition, in the case of an octree, since the maximum value of each index is 8 (which can be represented by 3 bits), the number of bits required can be calculated as 3 bits × the depth of the leaf node. In addition, the three-dimensional data encoding device can also perform entropy encoding after binarizing each index to reduce the data volume.

[0663] In addition, as Fig.72 shown, in the occupancy code, in order to decode the leaf nodes A and C, it is necessary to decode all the nodes above them. On the other hand, in the position code, it is possible to decode only the data of the leaf nodes A and C. Thus, as Fig.72 shown, by using the position code, the number of bits can be reduced compared to the occupancy code.

[0664] In addition, as Fig.72 shown, by performing dictionary compression such as LZ77 on a part or all of the position code, the code volume can be further reduced.

[0665] Next, an example of applying position encoding to the three-dimensional points (point cloud) obtained by LiDAR will be described. Fig.73 is a diagram showing an example of three-dimensional points obtained by LiDAR. The three-dimensional points obtained by LiDAR are sparse. That is, when representing these three-dimensional points with an occupancy code, the number of zeros increases. In addition, high three-dimensional accuracy is required for these three-dimensional points. That is, the depth of the octree structure becomes deeper.

[0666] Fig.74 is a diagram showing an example of such a sparse deep octree structure. Fig.74 The occupancy code of the octree structure shown is 136 bits (= 8 bits × 17 nodes). In addition, since the depth is 6 and there are 6 three-dimensional points, the position code is 3 bits × 6 × 6 = 108 bits. That is, the position code can reduce the code volume by 20% compared to the occupancy code. In this way, by applying position encoding to the sparse deep octree structure, the code volume can be reduced.

[0667] Hereinafter, the code volumes of the occupancy code and the position code will be described. When the depth of the octree structure is 10, the maximum number of three-dimensional points is 8 10 = 1073741824. In addition, the number of bits L o of the occupancy code of the octree structure is represented as follows.

[0668] L o = 8 + 8 2 + … + 8 10 = 127133512 bits

[0669] Therefore, the number of bits per three - dimensional point is 1.143 bits. Additionally, in the occupancy code, this number of bits does not change even when the number of three - dimensional points included in the octree structure changes.

[0670] On the other hand, in the position code, the number of bits per three - dimensional point directly affects the depth of the octree structure. Specifically, the number of bits of the position code per three - dimensional point is 3 bits × depth 10 = 30 bits.

[0671] Therefore, the number of bits L of the position code of the octree structure l is represented as follows.

[0672] L l = 30 × N

[0673] Here, N is the number of three - dimensional points included in the octree structure.

[0674] Therefore, when N < L o / 30 = 40904450.4, that is, when the number of three - dimensional points is less than 40904450, the code amount of the position code becomes less than the code amount of the occupancy code (L l < L o ).

[0675] In this way, when the number of three - dimensional points is small, the code amount of the position code is less than the code amount of the occupancy code, and when the number of three - dimensional points is large, the code amount of the position code is more than the code amount of the occupancy code.

[0676] Therefore, the three - dimensional data encoding device can also switch which of the position encoding and the occupancy encoding to use according to the number of input three - dimensional points. In this case, the three - dimensional data encoding device can also attach information indicating which of the position encoding and the occupancy encoding has been used to the header information of the bitstream, etc.

[0677] Hereinafter, a hybrid encoding combining the position encoding and the occupancy encoding will be described. The hybrid encoding combining the position encoding and the occupancy encoding is effective when encoding a dense important area. Fig.75 is a diagram showing this example. In Fig.75In the example shown, important three-dimensional points are densely arranged. In this case, the three-dimensional data encoding device performs position encoding on the upper layer with a shallow depth and occupancy encoding on the lower layer. Specifically, position encoding is used until the deepest common node, and occupancy encoding is used at positions deeper than the deepest common node. Here, the deepest common node refers to the deepest node among the nodes that are common ancestors of multiple important three-dimensional points.

[0678] Next, a hybrid encoding that prioritizes compression efficiency will be described. The three-dimensional data encoding device can also switch between position encoding and occupancy encoding according to rules predefined in the octree encoding.

[0679] Fig.76 FIG. is an example showing this rule. First, the three-dimensional data encoding device confirms the ratio of nodes containing three-dimensional points at each level (depth). When this ratio is higher than a predefined threshold, the three-dimensional data encoding device performs occupancy encoding on several nodes in the upper layer of the target level. For example, the three-dimensional data encoding device applies occupancy encoding from the target level to the level of the deepest common node.

[0680] For example, in Fig.76 In the example shown, the ratio of nodes containing three-dimensional points in the third level is higher than the threshold. Therefore, the three-dimensional data encoding device applies occupancy encoding to the second and third levels from this third level to the deepest common node, and applies position encoding to the first and fourth levels other than these.

[0681] The calculation method of the above threshold will be described. In one layer of the octree structure, there is one root node and eight child nodes. Therefore, in occupancy encoding, 8 bits are required to encode one layer of the octree structure. On the other hand, in position encoding, 3 bits are required for each child node containing a three-dimensional point. Therefore, when the number of nodes containing three-dimensional points is greater than 2, occupancy encoding is more effective than position encoding. That is, in this case, the threshold is 2.

[0682] Hereinafter, a configuration example of the bitstream generated by the above position encoding, occupancy encoding, or hybrid encoding will be described.

[0683] Fig.77 FIG. is an example of the bitstream generated by position encoding. As Fig.77 shown, the bitstream generated by position encoding includes a header and multiple position codes. Each position encoding is for one three-dimensional point.

[0684] With this configuration, the three-dimensional data decoding device can decode multiple three-dimensional points with high precision respectively. In addition, Fig.77An example of a bitstream in the case of representing a quadtree structure. In the case of an octree structure, each index can take values from 0 to 7.

[0685] In addition, the three-dimensional data encoding device can also perform entropy encoding after binarizing the column (string) of indexes representing a three-dimensional point. For example, in the case where the column of indexes is 0121, the three-dimensional data encoding device can binarize 0121 into 00011001 and perform arithmetic encoding on this bit string.

[0686] Fig.78 is a diagram showing an example of a bitstream generated by hybrid encoding in the case of including important three-dimensional points. As Fig.78 shown, the position code of the upper layer, the occupancy rate code of the important three-dimensional points in the lower layer, and the occupancy rate code of the non-important three-dimensional points other than the important three-dimensional points in the lower layer are arranged in sequence. In addition, Fig.78 the position code length shown represents the code amount of the subsequent position code. In addition, the occupancy rate code amount represents the code amount of the subsequent occupancy rate code.

[0687] With this configuration, the three-dimensional data decoding device can select different decoding plans according to the application program.

[0688] In addition, the encoded data of the important three-dimensional points is stored near the beginning of the bitstream, and the encoded data of the non-important three-dimensional points that are not included in the important region is stored after the encoded data of the important three-dimensional points.

[0689] Fig.79 is a diagram showing the tree structure represented by Fig.78 the occupancy rate code of the important three-dimensional points shown. Fig.80 is a diagram showing the tree structure represented by Fig.78 the occupancy rate code of the non-important three-dimensional points shown. As Fig.79 shown, in the occupancy rate code of the important three-dimensional points, information related to the non-important three-dimensional points is excluded. Specifically, since there are no important three-dimensional points in node 0 and node 3 at depth 5, the values 0 indicating no three-dimensional points are assigned to node 0 and node 3.

[0690] On the other hand, as Fig.80 shown, in the occupancy rate code of the non-important three-dimensional points, information related to the important three-dimensional points is excluded. Specifically, since there are no non-important three-dimensional points in node 1 at depth 5, the value 0 indicating no three-dimensional points is assigned to node 1.

[0691] In this way, the three-dimensional data encoding device divides the original tree structure into a first tree structure containing important three-dimensional points and a second tree structure containing unimportant three-dimensional points, and performs occupancy encoding on the first tree structure and the second tree structure independently. Thus, the three-dimensional data decoding device can preferentially decode important three-dimensional points.

[0692] Next, a configuration example of a bitstream generated by efficiency-oriented hybrid encoding will be described. Fig.81 It is a diagram showing a configuration example of a bitstream generated by efficiency-oriented hybrid encoding. As Fig.81 shown, for each subtree, the position of the subtree root node, the occupancy code amount, and the occupancy code are sequentially arranged. Fig.81 The subtree position shown is the position code of the root node of the subtree.

[0693] In the above configuration, when only one of position encoding and occupancy encoding is applied to the octree structure, the following holds.

[0694] When the length of the position encoding of the root node of the subtree is equal to the depth of the octree structure, the subtree has no child nodes. That is, position encoding is applied to the entire tree structure.

[0695] When the root node of the subtree is equal to the root node of the octree structure, occupancy encoding is applied to the entire tree structure.

[0696] For example, based on the above rules, the three-dimensional data decoding device can determine whether the bitstream contains a position code or an occupancy encoding.

[0697] In addition, the bitstream can also include encoding mode information indicating which of position encoding, occupancy encoding, and hybrid encoding is used. Fig.82 It is a diagram showing an example of the bitstream in this case. For example, as Fig.82 shown, 2-bit encoding mode information indicating the encoding mode is appended to the bitstream.

[0698] In addition, (1) the "number of three-dimensional points" in position encoding represents the number of subsequent three-dimensional points. In addition, (2) the "occupancy code amount" in occupancy encoding represents the code amount of the subsequent occupancy code. In addition, (3) the "number of important subtrees" in hybrid encoding (important three-dimensional points) represents the number of subtrees containing important three-dimensional points. In addition, (4) the "number of occupancy subtrees" in hybrid encoding (efficiency-oriented) represents the number of subtrees after occupancy encoding.

[0699] Next, a syntax example used to switch the application of occupancy encoding and position encoding will be described. Fig.83 It is a diagram showing this syntax example.

[0700] Fig.83 The isleaf shown is a flag indicating whether the object node is a leaf node. isleaf = 1 indicates that the object node is a leaf node, and isleaf = 0 indicates that the object node is not a leaf node but a node.

[0701] When the object node is a leaf node, a point_flag is appended to the bitstream. The point_flag is a flag indicating whether the object node (leaf node) contains three-dimensional points. point_flag = 1 indicates that the object node contains three-dimensional points, and point_flag = 0 indicates that the object node does not contain three-dimensional points.

[0702] When the object node is not a leaf node, a coding_type is appended to the bitstream. The coding_type is coding type information indicating the applicable coding type. coding_type = 00 indicates that position coding is applicable, coding_type = 01 indicates that occupancy coding is applicable, and coding_type = 10 or 11 indicates that other coding methods are applicable, etc.

[0703] When the coding type is position coding, numPoint, num_idx[i], and idx[i][j] are appended to the bitstream.

[0704] numPoint represents the number of three-dimensional points for which position coding is performed. num_idx[i] represents the number (depth) of indices from the object node to the three-dimensional point i. When all the three-dimensional points for which position coding is performed are at the same depth, num_idx[i] are all the same value. Therefore, it can also be that, before the Fig.83 shown for statement (for(i = 0; i < numPoint; i++) {}), num_idx is defined as a common value.

[0705] idx[i][j] represents the value of the j-th index among the indices from the object node to the three-dimensional point i. In the case of an octree, the number of bits of idx[i][j] is 3 bits.

[0706] In addition, as described above, an index refers to an identifier used to identify multiple child nodes of an object node. In the case of an octree, idx[i][j] represents any one of 0 to 7. In addition, in the case of an octree, there are 8 child nodes, and each child node corresponds to each of the 8 sub-blocks obtained by spatially dividing the object block corresponding to the object node into 8 parts. Therefore, idx[i][j] can also be information representing the three-dimensional position of the sub-block corresponding to the child node. For example, idx[i][j] can also be 3-bit information in total containing 1 bit each representing the positions of x, y, and z of the sub-block.

[0707] When the coding type is occupancy coding, an occupancy_code is appended to the bitstream. The occupancy_code is the occupancy rate code of the object node. In the case of an octree, the occupancy_code is an 8-bit bitstring such as the bitstring "00101000".

[0708] When the value of the (i + 1)-th bit of the occupancy_code is 1, the process transfers to the child node. That is, the child node is set as the next object node, and the bitstring is generated recursively.

[0709] In the present embodiment, an example is shown in which the end of the octree is represented by appending leaf node information (isleaf, point_flag) to the bitstream, but it is not necessarily limited to this. For example, the three-dimensional data encoding device can append the maximum depth (depth) from the start node (root node) of the occupancy code to the end (leaf node) where the three-dimensional points exist to the head of the start node. Then, the three-dimensional data encoding device can also perform bitstring conversion on the information of the child nodes recursively while increasing the depth from the start node, and determine that it has reached the leaf node when the depth reaches the maximum depth. In addition, the three-dimensional data encoding device can append the information representing the maximum depth to the first node where the coding_type becomes occupancy coding, or can also append it to the start node (root node) of the octree.

[0710] As described above, the three-dimensional data encoding device can also append information for switching between occupancy coding and position coding to the bitstream as the header information of each node.

[0711] In addition, the three-dimensional data encoding device can also perform entropy coding on the coding_type, numPoint, num_idx, idx, and occupancy_code of each node generated by the above method. For example, the three-dimensional data encoding device performs arithmetic coding after binarizing each value.

[0712] Furthermore, in the above syntax, an example is shown in which a depth-first bitstring of the octree structure is used as the occupancy code, but it is not necessarily limited to this. The three-dimensional data encoding device can also use a breadth-first bitstring of the octree structure as the occupancy code. When the three-dimensional data encoding device uses a breadth-first bitstring, it can also append information for switching between occupancy coding and position coding to the bitstream as the header information of each node.

[0713] In the present embodiment, the octree structure is taken as an example for illustration, but it is not necessarily limited to this. The above method can also be applied to N-ary trees (N is an integer greater than or equal to 2) such as quadtrees and hexadecimal trees, or other tree structures.

[0714] Next, a flow example of the encoding process for switching the application of occupancy encoding and position encoding will be described. Fig.84 This is a flowchart of the encoding process of the present embodiment.

[0715] First, the three-dimensional data encoding device represents a plurality of three-dimensional points included in the three-dimensional data using an octree structure (S1601). Next, the three-dimensional data encoding device sets the root node in the octree structure as the target node (S1602). Next, the three-dimensional data encoding device generates a bit string of the octree structure by performing node encoding processing on the target node (S1603). Next, the three-dimensional data encoding device generates a bitstream by performing entropy encoding on the generated bit string (S1604).

[0716] Fig.85 This is a flowchart of the node encoding process (S1603). First, the three-dimensional data encoding device determines whether the target node is a leaf node (S1611). When the target node is not a leaf node (the "No" of S1611), the three-dimensional data encoding device sets the leaf node flag (isleaf) to 0 and attaches the leaf node flag to the bit string (S1612).

[0717] Next, the three-dimensional data encoding device determines whether the number of child nodes including three-dimensional points is larger than a preset threshold (S1613). Additionally, the three-dimensional data encoding device may also attach the threshold to the bit string.

[0718] When the number of child nodes including three-dimensional points is larger than the preset threshold (the "Yes" of S1613), the three-dimensional data encoding device sets the encoding type (coding_type) to occupancy encoding and attaches the encoding type to the bit string (S1614).

[0719] Next, the three-dimensional data encoding device sets occupancy encoding information and attaches the occupancy encoding information to the bit string. Specifically, the three-dimensional data encoding device generates an occupancy code of the target node and attaches the occupancy code to the bit string (S1615).

[0720] Next, the three-dimensional data encoding device sets the next target node according to the occupancy code (S1616). Specifically, the three-dimensional data encoding device sets an unprocessed child node with an occupancy code of "1" as the next target node.

[0721] Next, the three-dimensional data encoding device performs node encoding processing on the newly set target node (S1617). That is, it performs the processing shown in Fig.85 as follows.

[0722] When the processing of all child nodes has not been completed (No in S1618), the processing after step S1616 is performed again. On the other hand, when the processing of all child nodes is completed (Yes in S1618), the three-dimensional data encoding device ends the node encoding process.

[0723] In addition, in step S1613, when the number of child nodes including three-dimensional points is equal to or less than a preset threshold (No in S1613), the three-dimensional data encoding device sets the encoding type to position encoding and attaches this encoding type to the bit string (S1619).

[0724] Next, the three-dimensional data encoding device sets position encoding information and attaches this position encoding information to the bit string. Specifically, the three-dimensional data encoding device generates a position code and attaches this position encoding to the bit string (S1620). The position code includes numPoint, num_idx, and idx.

[0725] In addition, in step S1611, when the object node is a leaf node (Yes in S1611), the three-dimensional data encoding device sets the leaf node flag to 1 and attaches this leaf node flag to the bit string (S1621). In addition, the three-dimensional data encoding device sets information indicating whether the leaf node includes three-dimensional points, i.e., the point flag (point_flag), and attaches this point flag to the bit string (S1622).

[0726] Next, a flow example of the decoding process for switching the application of occupancy encoding and position encoding is described. Fig.85 It is a flowchart of the decoding process of this embodiment.

[0727] The three-dimensional data decoding device generates a bit string by performing entropy decoding on the bit stream (S1631). Next, the three-dimensional data decoding device restores the octree structure by performing node decoding processing on the obtained bit string (S1632). Next, the three-dimensional data decoding device generates three-dimensional points based on the restored octree structure (S1633).

[0728] Fig.87 It is a flowchart of the node decoding process (S1632). First, the three-dimensional data decoding device obtains (decodes) the leaf node flag (isleaf) from the bit string (S1641). Next, the three-dimensional data decoding device determines whether the object node is a leaf node based on the leaf node flag (S1642).

[0729] When the object node is not a leaf node (No in S1642), the three-dimensional data decoding device obtains the encoding type (coding_type) from the bit string (S1643). The three-dimensional data decoding device determines whether the encoding type is occupancy encoding (S1644).

[0730] When the encoding type is occupancy encoding (i.e., "yes" in S1644), the 3D data decoding device obtains occupancy encoding information from the bit string. Specifically, the 3D data decoding device obtains an occupancy code from the bit string (S1645).

[0731] Next, the 3D data decoding device sets the next object node according to the occupancy encoding (S1646). Specifically, the 3D data decoding device sets an unprocessed child node with the occupancy code being "1" as the next object node.

[0732] Next, the 3D data decoding device performs node decoding processing on the newly set object node (S1647). That is, the 3D data decoding device performs the Fig.87 processing shown.

[0733] If not all child nodes have been processed (i.e., "no" in S1648), the processing after step S1646 is performed again. On the other hand, if all child nodes have been processed (i.e., "yes" in S1648), the 3D data decoding device ends the node decoding processing.

[0734] In addition, when the encoding type is position encoding in step S1644 (i.e., "no" in S1644), the 3D data decoding device obtains position encoding information from the bit string. Specifically, the 3D data decoding device obtains a position code from the bit string (S1649). The position code includes numPoint, num_idx, and idx.

[0735] In addition, when the object node is a leaf node in step S1642 (i.e., "yes" in S1642), the 3D data decoding device obtains information indicating whether the leaf node contains 3D points, i.e., a point flag (point_flag), from the bit string (S1650).

[0736] In addition, in this embodiment, an example of switching the encoding type for each node is shown, but it is not necessarily limited to this. The encoding type can also be fixed in units of volume, space, or world space. In this case, the 3D data encoding device can also attach the encoding type information to the header information of the volume, space, or world space.

[0737] As described above, the three-dimensional data encoding device according to the present embodiment generates first information in the form of an N-ary tree structure (position encoding) representing a plurality of three-dimensional points included in the three-dimensional data, and generates a bitstream including the first information. The first information includes three-dimensional point information (position code) corresponding to each of the plurality of three-dimensional points. Each three-dimensional point information includes an index (idx) corresponding to each of the plurality of layers in the N-ary tree structure. Each index indicates the sub-block to which the corresponding three-dimensional point belongs among the N sub-blocks belonging to the corresponding layer.

[0738] In other words, each three-dimensional point information represents a path in the N-ary tree structure up to the corresponding three-dimensional point. Each index indicates the sub-node included in the path among the N sub-nodes belonging to the corresponding layer (node).

[0739] Accordingly, the three-dimensional data encoding method can generate a bitstream that can selectively decode three-dimensional points.

[0740] For example, the three-dimensional point information (position code) includes information (num_idx) indicating the number of indexes included in the three-dimensional point information. In other words, this information represents the depth (number of layers) in the N-ary tree structure up to the corresponding three-dimensional point.

[0741] For example, the first information includes information (numPoint) indicating the number of three-dimensional point information included in the first information. In other words, this information represents the number of three-dimensional points included in the N-ary tree structure.

[0742] For example, N is 8 and the index is 3 bits.

[0743] For example, the three-dimensional data encoding device has: a first encoding mode that generates the first information; and a second encoding mode that generates second information (occupancy code) representing the N-ary tree structure in a second manner (occupancy encoding), and generates a bitstream including the second information. The second information includes a 1-bit information corresponding to each of the plurality of sub-blocks belonging to the plurality of layers in the N-ary tree structure and indicating whether there is a three-dimensional point in the corresponding sub-block.

[0744] For example, when the number of the plurality of three-dimensional points is equal to or less than a predetermined threshold, the three-dimensional data encoding device uses the first encoding mode, and when the number of the plurality of three-dimensional points is more than the threshold, the three-dimensional data encoding device uses the second encoding mode. Accordingly, the three-dimensional data encoding device can reduce the code amount of the bitstream.

[0745] For example, the first information and the second information include information (encoding mode information) indicating whether the information represents the N-ary tree structure in the first manner or the second manner.

[0746] For example, as Fig.75As shown in etc., the three-dimensional data encoding device uses the first encoding mode in a part of the N-ary tree structure and the second encoding mode in another part of the N-ary tree structure.

[0747] For example, the three-dimensional data encoding device includes a processor and a memory, and the processor uses the memory to perform the above processing.

[0748] In addition, the three-dimensional data decoding device of the present embodiment obtains, from a bitstream, first information (position code) of an N-ary tree structure (where N is an integer greater than or equal to 2) representing a plurality of three-dimensional points included in the three-dimensional data in a first manner (position encoding). The first information includes three-dimensional point information (position code) corresponding to each of the plurality of three-dimensional points. Each three-dimensional point information includes an index (idx) corresponding to each of the plurality of layers in the N-ary tree structure. Each index indicates the sub-block to which the corresponding three-dimensional point belongs among the N sub-blocks belonging to the corresponding layer.

[0749] In other words, each three-dimensional point information represents the path in the N-ary tree structure up to the corresponding three-dimensional point. Each index indicates the sub-node included in the above path among the N sub-nodes belonging to the corresponding layer (node).

[0750] The three-dimensional data decoding device further uses the three-dimensional point information to restore the three-dimensional point corresponding to the three-dimensional point information.

[0751] Thereby, the three-dimensional data decoding device can selectively decode three-dimensional points from the bitstream.

[0752] For example, the three-dimensional point information (position code) includes information (num_idx) indicating the number of indices included in the three-dimensional point information. In other words, this information represents the depth (number of layers) in the N-ary tree structure up to the corresponding three-dimensional point.

[0753] For example, the first information includes information (numPoint) indicating the number of three-dimensional point information included in the first information. In other words, this information represents the number of three-dimensional points included in the N-ary tree structure.

[0754] For example, N is 8 and the index is 3 bits.

[0755] For example, the three-dimensional data decoding device further obtains, from the bitstream, second information (occupancy code) of the N-ary tree structure represented in a second manner (occupancy encoding). The three-dimensional data decoding device uses the second information to restore the plurality of three-dimensional points. The second information includes a 1-bit information corresponding to each of the plurality of sub-blocks belonging to the plurality of layers in the N-ary tree structure and indicating whether there is a three-dimensional point in the corresponding sub-block.

[0756] For example, the first information and the second information include information (encoding mode information) indicating whether the information represents an N-ary tree structure in the first manner or represents an N-ary tree structure in the second manner.

[0757] For example, as Fig.75 shown, etc., a part of the N-ary tree structure is represented in the first manner, and another part of the N-ary tree structure is represented in the second manner.

[0758] For example, the three-dimensional data decoding device includes a processor and a memory, and the processor uses the memory to perform the above processing.

[0759] (Embodiment 10)

[0760] In this embodiment, another example of an encoding method for a tree structure such as an octree structure will be described. Fig.88 is a diagram showing an example of the tree structure related to this embodiment. In addition, Fig.88 shows an example of a quadtree structure.

[0761] A leaf node containing three-dimensional points is called a valid leaf node, and a leaf node not containing three-dimensional points is called an invalid leaf node. A branch where the number of valid leaf nodes is above a threshold is called a dense branch. A branch where the number of valid leaf nodes is smaller than the threshold is called a sparse branch.

[0762] The three-dimensional data encoding device calculates the number of three-dimensional points (i.e., the number of valid leaf nodes) included in each branch in a certain layer of the tree structure. Fig.88 shows an example when the threshold is 5. In this example, there are two branches in layer 1. Since the left branch contains 7 three-dimensional points, the left branch is determined to be a dense branch. Since the right branch contains two three-dimensional points, the right branch is determined to be a sparse branch.

[0763] Fig.89 For example, it is a diagram showing an example of the number of valid leaf nodes (3D points) of each branch in layer 5. Fig.89 The horizontal axis represents the identification number (index) of the branches in layer 5. As Fig.89 shown, in a specific branch, it contains significantly more three-dimensional points than other branches. In such a dense branch, occupancy encoding is more effective than in a sparse branch.

[0764] Hereinafter, an application method of occupancy encoding and position encoding will be described. Fig.90 is a diagram showing the relationship between the number of three-dimensional points (the number of valid leaf nodes) included in each branch in layer 5 and the applied encoding method. As Fig.90As shown, the three-dimensional data encoding device performs occupancy encoding for dense branch applications and position encoding for sparse branch applications. Thereby, the encoding efficiency can be improved.

[0765] Fig.91 is a diagram showing an example of a dense branch region in LiDAR data. As Fig.91 shown, depending on the region, the density of the three-dimensional points calculated based on the number of three-dimensional points included in each branch is different.

[0766] In addition, by separating dense three-dimensional points (branches) from sparse three-dimensional points (branches), there are the following advantages. The closer to the LiDAR sensor, the higher the density of the three-dimensional points. Therefore, by separating the branches corresponding to density, zoning in the distance direction can be performed. Such zoning is effective in specific applications. In addition, for sparse branches, it is effective to use a method other than occupancy encoding.

[0767] In the present embodiment, the three-dimensional data encoding device separates the input three-dimensional point group into two or more sub three-dimensional point groups and applies different encoding methods to each sub three-dimensional point group.

[0768] For example, the three-dimensional data encoding device separates the input three-dimensional point group into a sub three-dimensional point group A (dense three-dimensional point group: dense cloud) including dense branches and a sub three-dimensional point group B (sparse three-dimensional point group: sparse cloud) including sparse branches. Fig.92 is a diagram showing an example of a sub three-dimensional point group A (dense three-dimensional point group) including dense branches separated from the Fig.88 tree structure shown. Fig.93 is a diagram showing an example of a sub three-dimensional point group B (sparse three-dimensional point group) including sparse branches separated from the Fig.88 tree structure shown.

[0769] Next, the three-dimensional data encoding device encodes the sub three-dimensional point group A by occupancy encoding and encodes the sub three-dimensional point group B by position encoding.

[0770] In addition, although an example in which different encoding methods (occupancy encoding and position encoding) are applied as different encoding methods is shown here, for example, the three-dimensional data encoding device may also use the same encoding method for the sub three-dimensional point group A and the sub three-dimensional point group B and make the parameters used in the encoding different between the sub three-dimensional point group A and the sub three-dimensional point group B.

[0771] Hereinafter, the process of the three-dimensional data encoding process performed by the three-dimensional data encoding device will be described. Fig.94 is a flowchart of the three-dimensional data encoding process performed by the three-dimensional data encoding device according to the present embodiment.

[0772] First, the three-dimensional data encoding device separates the input three-dimensional point cloud into sub-three-dimensional point clouds (S1701). The three-dimensional data encoding device can perform this separation either automatically or based on information input by the user. For example, the user can specify the range of the sub-three-dimensional point clouds, etc. In addition, as an example of automatic execution, for instance, when the input data is LiDAR data, the three-dimensional data encoding device separates the point clouds using the distance information to each point cloud. Specifically, the three-dimensional data encoding device separates the point clouds within a certain range from the measurement location and the point clouds outside the range. In addition, the three-dimensional data encoding device can also perform the separation using the information of important regions and unimportant regions.

[0773] Next, the three-dimensional data encoding device encodes the sub-three-dimensional point cloud A by method A to generate encoded data (encoded bitstream) (S1702). In addition, the three-dimensional data encoding device encodes the sub-three-dimensional point cloud B by method B to generate encoded data (S1703). Additionally, the three-dimensional data encoding device can also encode the sub-three-dimensional point cloud B by method A. In this case, the three-dimensional data encoding device encodes the sub-three-dimensional point cloud B using encoding parameters different from those used in the encoding of the sub-three-dimensional point cloud A. For example, this parameter can also be a quantization parameter. For example, the three-dimensional data encoding device encodes the sub-three-dimensional point cloud B using a quantization parameter larger than the quantization parameter used in the encoding of the sub-three-dimensional point cloud A. In this case, the three-dimensional data encoding device can also attach information indicating the quantization parameter used in the encoding of the sub-three-dimensional point cloud to the header of the encoded data of each sub-three-dimensional point cloud.

[0774] Next, the three-dimensional data encoding device generates a bitstream by combining the encoded data obtained in step S1702 and the encoded data obtained in step S1703 (S1704).

[0775] In addition, the three-dimensional data encoding device can also encode, as the header information of the bitstream, the information used to decode each sub-three-dimensional point cloud. For example, the three-dimensional data encoding device can encode the following information.

[0776] The header information can also include information indicating the number of sub-three-dimensional points encoded. In this example, this information indicates 2.

[0777] The header information can also include information indicating the number of three-dimensional points included in each sub-three-dimensional point cloud and the encoding method. In this example, this information indicates the number of three-dimensional points included in the sub-three-dimensional point cloud A, the encoding method (method A) applied to the sub-three-dimensional point cloud A, the number of three-dimensional points included in the sub-three-dimensional point cloud B, and the encoding method (method B) applied to the sub-three-dimensional point cloud B.

[0778] The header information may also include information indicating the start position or end position of the encoded data for identifying each sub three-dimensional point group.

[0779] In addition, the three-dimensional data encoding device may also encode the sub three-dimensional point group A and the sub three-dimensional point group B in parallel. Alternatively, the three-dimensional data encoding device may also encode the sub three-dimensional point group A and the sub three-dimensional point group B sequentially.

[0780] In addition, the method for separating into sub three-dimensional point groups is not limited to the above. For example, the three-dimensional data encoding device changes the separation method, encodes using each of multiple separation methods, and calculates the encoding efficiency of the encoded data obtained using each separation method. And the three-dimensional data encoding device selects the separation method with the highest encoding efficiency. For example, the three-dimensional data encoding device may also separate the three-dimensional point group in each of multiple layers, calculate the encoding efficiency in each case, select the separation method (i.e., the layer for separation) with the highest encoding efficiency, and generate and encode sub three-dimensional point groups using the selected separation method.

[0781] In addition, when combining the encoded data, the three-dimensional data encoding device may arrange the encoding information of the more important sub three-dimensional point groups closer to the start of the bitstream. Thus, the three-dimensional data decoding device can obtain important information only by decoding the start bitstream, so it can obtain important information earlier.

[0782] Next, the process of three-dimensional data decoding processing performed by the three-dimensional data decoding device will be described. Fig.95 is a flowchart of three-dimensional data decoding processing performed by the three-dimensional data decoding device according to the present embodiment.

[0783] First, the three-dimensional data decoding device obtains, for example, the bitstream generated by the above three-dimensional data encoding device. Next, the three-dimensional data decoding device separates the encoded data of the sub three-dimensional point group A and the encoded data of the sub three-dimensional point group B from the obtained bitstream (S1711). Specifically, the three-dimensional data decoding device decodes the information for decoding each sub three-dimensional point group from the header information of the bitstream, and uses this information to separate the encoded data of each sub three-dimensional point group.

[0784] Next, the three-dimensional data decoding device decodes the encoded data of the sub three-dimensional point group A using method A to obtain the sub three-dimensional point group A (S1712). In addition, the three-dimensional data decoding device decodes the encoded data of the sub three-dimensional point group B using method B to obtain the sub three-dimensional point group B (S1713). Next, the three-dimensional data decoding device combines the sub three-dimensional point group A and the sub three-dimensional point group B (S1714).

[0785] In addition, the three-dimensional data decoding device can also decode the sub-three-dimensional point group A and the sub-three-dimensional point group B in parallel. Alternatively, the three-dimensional data decoding device can also decode the sub-three-dimensional point group A and the sub-three-dimensional point group B sequentially.

[0786] In addition, the three-dimensional data decoding device can also decode the required sub-three-dimensional point group. For example, the three-dimensional data decoding device can decode the sub-three-dimensional point group A without decoding the sub-three-dimensional point group B. For example, in the case where the sub-three-dimensional point group A is a three-dimensional point group included in an important area of LiDAR data, the three-dimensional data decoding device decodes the three-dimensional point group of the important area. The three-dimensional point group of the important area is used for self-position estimation of a vehicle or the like.

[0787] Next, a specific example of the encoding process according to this embodiment will be described. Fig.96 It is a flowchart of the three-dimensional data encoding process performed by the three-dimensional data encoding device according to this embodiment.

[0788] First, the three-dimensional data encoding device separates the input three-dimensional points into a sparse three-dimensional point group and a dense three-dimensional point group (S1721). Specifically, the three-dimensional data encoding device counts the number of valid leaf nodes of the branches of a certain layer of the octree structure. The three-dimensional data encoding device sets each branch as a dense branch or a sparse branch according to the number of valid leaf nodes of each branch. And the three-dimensional data encoding device generates a sub-three-dimensional point group (dense three-dimensional point group) that aggregates the dense branches and a sub-three-dimensional point group (sparse three-dimensional point group) that aggregates the sparse branches.

[0789] Next, the three-dimensional data encoding device generates encoded data by encoding the sparse three-dimensional point group (S1722). For example, the three-dimensional data encoding device encodes the sparse three-dimensional point group using position encoding.

[0790] In addition, the three-dimensional data encoding device generates encoded data by encoding the dense three-dimensional point group (S1723). For example, the three-dimensional data encoding device encodes the dense three-dimensional point group using occupancy encoding.

[0791] Next, the three-dimensional data encoding device generates a bitstream by combining the encoded data of the sparse three-dimensional point group obtained in step S1722 and the encoded data of the dense three-dimensional point group obtained in step S1723 (S1724).

[0792] In addition, the three-dimensional data encoding device can also encode the information used to decode the sparse three-dimensional point group and the dense three-dimensional point group as the header information of the bitstream. For example, the three-dimensional data encoding device can encode the following information.

[0793] The header information may also include information indicating the number of sub-three-dimensional point groups to be encoded. In this example, this information indicates 2.

[0794] The header information may also include information indicating the number of three-dimensional points included in each sub-three-dimensional point group and the encoding method. In this example, this information indicates the number of three-dimensional points included in the sparse three-dimensional point group, the encoding method applied to the sparse three-dimensional point group (position encoding), the number of three-dimensional points included in the dense three-dimensional point group, and the encoding method applied to the dense three-dimensional point group (occupancy encoding).

[0795] The header information may also include information for identifying the start position or the end position of the encoded data of each sub-three-dimensional point group. In this example, this information indicates at least one of the start position and the end position of the encoded data of the sparse three-dimensional point group and the start position and the end position of the encoded data of the dense three-dimensional point group.

[0796] In addition, the three-dimensional data encoding device may also encode the sparse three-dimensional point group and the dense three-dimensional point group in parallel. Alternatively, the three-dimensional data encoding device may also encode the sparse three-dimensional point group and the dense three-dimensional point group sequentially.

[0797] Next, a specific example of the three-dimensional data decoding process will be described. Fig.97 is a flowchart of the three-dimensional data decoding process performed by the three-dimensional data decoding device according to the present embodiment.

[0798] First, the three-dimensional data decoding device, for example, obtains the bitstream generated by the above three-dimensional data encoding device. Next, the three-dimensional data decoding device separates the encoded data of the sparse three-dimensional point group and the encoded data of the dense three-dimensional point group from the obtained bitstream (S1731). Specifically, the three-dimensional data decoding device decodes the information for decoding each sub-three-dimensional point group from the header information of the bitstream, and uses this information to separate the encoded data of each sub-three-dimensional point group. In this example, the three-dimensional data decoding device separates the encoded data of the sparse three-dimensional point group and the dense three-dimensional point group from the bitstream using the header information.

[0799] Next, the three-dimensional data decoding device obtains the sparse three-dimensional point group by decoding the encoded data of the sparse three-dimensional point group (S1732). For example, the three-dimensional data decoding device decodes the sparse three-dimensional point group using the position decoding for decoding the encoded data encoded by the position encoding.

[0800] In addition, the three-dimensional data decoding device obtains the dense three-dimensional point group by decoding the encoded data of the dense three-dimensional point group (S1733). For example, the three-dimensional data decoding device decodes the dense three-dimensional point group using the occupancy decoding for decoding the encoded data encoded by the occupancy encoding.

[0801] Next, the three-dimensional data decoding device combines the sparse three-dimensional point group obtained in step S1732 with the dense three-dimensional point group obtained in step S1733 (S1734).

[0802] In addition, the three-dimensional data decoding device may also decode the sparse three-dimensional point group and the dense three-dimensional point group in parallel. Alternatively, the three-dimensional data decoding device may also decode the sparse three-dimensional point group and the dense three-dimensional point group sequentially.

[0803] Furthermore, the three-dimensional data decoding device may also decode a part of the required sub-three-dimensional point groups. For example, the three-dimensional data decoding device may decode the dense three-dimensional point group without decoding the sparse three-dimensional data. For example, in the case where the dense three-dimensional point group is the three-dimensional point group included in an important area of LiDAR data, the three-dimensional data decoding device decodes the three-dimensional point group of this important area. The three-dimensional point group of this important area is used for self-position estimation of a vehicle or the like.

[0804] Fig.98 It is a flowchart of the encoding process according to the present embodiment. First, the three-dimensional data encoding device generates a sparse three-dimensional point group and a dense three-dimensional point group by separating the input three-dimensional point group into a sparse three-dimensional point group and a dense three-dimensional point group (S1741).

[0805] Next, the three-dimensional data encoding device generates encoded data by encoding the dense three-dimensional point group (S1742). In addition, the three-dimensional data encoding device generates encoded data by encoding the sparse three-dimensional point group (S1743). Finally, the three-dimensional data encoding device generates a bit stream by combining the encoded data of the sparse three-dimensional point group obtained in step S1742 with the encoded data of the dense three-dimensional point group obtained in step S1743 (S1744).

[0806] Fig.99 It is a flowchart of the decoding process according to the present embodiment. First, the three-dimensional data decoding device extracts the encoded data of the dense three-dimensional point group and the encoded data of the sparse three-dimensional point group from the bit stream (S1751). Next, the three-dimensional data decoding device obtains the decoded data of the dense three-dimensional point group by decoding the encoded data of the dense three-dimensional point group (S1752). In addition, the three-dimensional data decoding device obtains the decoded data of the sparse three-dimensional point group by decoding the encoded data of the sparse three-dimensional point group (S1753). Next, the three-dimensional data decoding device generates a three-dimensional point group by combining the decoded data of the dense three-dimensional point group obtained in step S1752 with the decoded data of the sparse three-dimensional point group obtained in step S1753 (S1754).

[0807] In addition, the three-dimensional data encoding device and the three-dimensional data decoding device can encode or decode either the dense three-dimensional point group or the sparse three-dimensional point group first. Furthermore, the encoding process or the decoding process can also be performed in parallel by multiple processors or the like.

[0808] In addition, the three-dimensional data encoding device can also encode one of the dense three-dimensional point group and the sparse three-dimensional point group. For example, when important information is included in the dense three-dimensional point group, the three-dimensional data encoding device extracts the dense three-dimensional point group and the sparse three-dimensional point group from the input three-dimensional point group, encodes the dense three-dimensional point group, and does not encode the sparse three-dimensional point group. Thereby, the three-dimensional data encoding device can attach important information to the stream while suppressing the amount of bits. For...

Claims

1. A three-dimensional data encoding method, wherein, obtain first three-dimensional point group data including a plurality of three-dimensional points and second three-dimensional point group data including a plurality of three-dimensional points, encode the first three-dimensional point group data through a first encoding process, encode the second three-dimensional point group data through a second encoding process, the first encoding process is an encoding process suitable for dense three-dimensional point group data.

2. The three-dimensional data encoding method according to claim 1, wherein, the second encoding process is an encoding process suitable for non-dense three-dimensional point group data.

3. A three-dimensional data encoding method, wherein, obtain three-dimensional point group data including a plurality of three-dimensional points, separate the three-dimensional point group data into dense three-dimensional point group data and non-dense three-dimensional point group data, encode the dense three-dimensional point group data through a first encoding process, encode the non-dense three-dimensional point group data through a second encoding process.

4. A three-dimensional data encoding method, wherein, obtain first three-dimensional data including a plurality of three-dimensional points and second three-dimensional data including a plurality of three-dimensional points, encode the first three-dimensional data through a first encoding process, encode the second three-dimensional data through a second encoding process, the density of the plurality of three-dimensional points included in the first three-dimensional data is greater than the density of the plurality of three-dimensional points included in the second three-dimensional data.

5. A three-dimensional data encoding method, wherein, obtain first three-dimensional data including dense a plurality of three-dimensional points and second three-dimensional data including non-dense a plurality of three-dimensional points, encode the first three-dimensional data through a first encoding process, encode the second three-dimensional data through a second encoding process.

6. The three-dimensional data encoding method according to claim 4 or 5, wherein, in the first encoding process and the second encoding process, the representation method of the three-dimensional point positions is different.

7. The three-dimensional point data encoding method according to claim 4 or 5, wherein, in the first encoding process, encode the first three-dimensional data using a first parameter, in the second encoding process, encode the second three-dimensional data using a second parameter.

8. The three-dimensional point data encoding method according to claim 4 or 5, wherein, generate first information representing the first encoding process as processing for the first three-dimensional data, generate second information representing the second encoding process as processing for the second three-dimensional data, generate a bitstream including the first information and the second information.

9. A three-dimensional data decoding method, wherein, obtain from a bitstream first three-dimensional point group data including encoded a plurality of three-dimensional points and second three-dimensional point group data including encoded a plurality of three-dimensional points, decode the first three-dimensional point group data through a first decoding process, decode the second three-dimensional point group data through a second decoding process, the first decoding process is a decoding process suitable for dense three-dimensional point group data.

10. The three-dimensional data decoding method according to claim 9, wherein, the second decoding process is a decoding process suitable for non-dense three-dimensional point group data.

11. A three-dimensional data decoding method, wherein, obtain three-dimensional point group data including a plurality of encoded three-dimensional points from a bit stream, separate the three-dimensional point group data into dense three-dimensional point group data and non-dense three-dimensional point group data, decode the dense three-dimensional point group data through a first decoding process, decode the non-dense three-dimensional point group data through a second decoding process.

12. A three-dimensional data decoding method, wherein, obtain first three-dimensional data including a plurality of encoded three-dimensional points and second three-dimensional data including a plurality of encoded three-dimensional points, decode the first three-dimensional data through a first decoding process, decode the second three-dimensional data through a second decoding process, the density of the plurality of encoded three-dimensional points included in the first three-dimensional data is greater than the density of the plurality of encoded three-dimensional points included in the second three-dimensional data.

13. A three-dimensional data decoding method, wherein, obtain first three-dimensional data including a plurality of dense encoded three-dimensional points and second three-dimensional data including a plurality of non-dense encoded three-dimensional points, decode the first three-dimensional data through a first decoding process, decode the second three-dimensional data through a second decoding process.

14. The three-dimensional data decoding method according to claim 12 or 13, wherein, in the first decoding process and the second decoding process, the representation method of the three-dimensional point positions is different.

15. The three-dimensional data decoding method according to claim 12 or 13, wherein, in the first decoding process, use a first parameter to decode the first three-dimensional data, in the second decoding process, use a second parameter to decode the second three-dimensional data.

16. The three-dimensional data decoding method according to claim 12 or 13, wherein, obtain first information representing the first decoding process as a decoding process corresponding to the first three-dimensional data from a bit stream, obtain second information representing the second decoding process as a decoding process corresponding to the second three-dimensional data from the bit stream.

17. A three-dimensional data encoding device, wherein, Comprising: a processor; and a memory, the processor uses the memory, obtain first three-dimensional point group data including a plurality of three-dimensional points and second three-dimensional point group data including a plurality of three-dimensional points, encode the first three-dimensional point group data through a first encoding process, encode the second three-dimensional point group data through a second encoding process, the first encoding process is an encoding process suitable for dense three-dimensional point group data.

18. A three-dimensional data decoding device, wherein, Comprising: a processor; and a memory, the processor uses the memory, obtain first three-dimensional point group data including a plurality of encoded three-dimensional points and second three-dimensional point group data including a plurality of encoded three-dimensional points from a bit stream, decode the first three-dimensional point group data through a first decoding process, decode the second three-dimensional point group data through a second decoding process, the first decoding process is a decoding process suitable for dense three-dimensional point group data.

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