3D Data Encoding Method, 3D Data Decoding Method, 3D Data Encoding Apparatus, and 3D Data Decoding Apparatus

Abstract

A three-dimensional data encoding method is a three-dimensional data encoding method for encoding a plurality of three-dimensional points with attribute information. Each of the plurality of three-dimensional points is assigned to one of a plurality of hierarchies (S3331), and the plurality of attribute information of the plurality of three-dimensional points is encoded using the hierarchies (S3332). Information indicating the number of three-dimensional points belonging to each of the plurality of hierarchies is encoded (S3333). For example, in the assignment (S3331), the three-dimensional data encoding method may also assign each of the plurality of three-dimensional points to one of the plurality of hierarchies based on the distance between the plurality of three-dimensional points.

Description

Technical Field

[0001] The present disclosure relates to a three-dimensional data encoding method, a three-dimensional data decoding method, a three-dimensional data encoding apparatus, and a three-dimensional data decoding apparatus. Background Art

[0002] In large fields such as computer vision, map information, monitoring, infrastructure inspection, or video distribution for autonomous work of automobiles or robots, devices or services that make flexible use of three-dimensional data will become popular in the future. The three-dimensional data is obtained by various methods such as a distance sensor such as a rangefinder, a stereo camera, or a combination of multiple monocular cameras.

[0003] As a representation method of three-dimensional data, there is a representation method called point cloud, which represents the shape of a three-dimensional structure by a point group in a three-dimensional space. The position and color of the point group are stored in the point cloud. Although it is expected that the point cloud will become the mainstream as a representation method of three-dimensional data, the data volume of the point group is very large. Therefore, in the storage or transmission of three-dimensional data, like two-dimensional moving images (as an example, MPEG-4 AVC or HEVC standardized as MPEG), data volume compression needs to be performed by encoding.

[0004] In addition, for the compression of point clouds, some are supported by publicly available libraries (PointCloud Library) that perform point cloud correlation processing.

[0005] In addition, there is a well-known technique that uses three-dimensional map data to retrieve facilities around a vehicle and display them (for example, refer to Patent Document 1).

[0006] Prior Art Documents

[0007] Patent Documents

[0008] Patent Document 1 International Publication No. 2014 / 020663 Summary of the Invention

[0009] Problems to be Solved by the Invention

[0010] It is desired to shorten the processing time in a three-dimensional data decoding apparatus during the encoding and decoding of three-dimensional data.

[0011] An 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 that can shorten the processing time in a three-dimensional data decoding apparatus.

[0012] Means for Solving the Problems

[0013] One embodiment of the 3D data encoding method of the present disclosure is a 3D data encoding method for encoding a plurality of 3D points with attribute information. Each of the plurality of 3D points is assigned to one of a plurality of levels, and the levels are used to encode the plurality of attribute information of the plurality of 3D points, and information indicating the number of 3D points belonging to each of the plurality of levels is encoded.

[0014] One embodiment of the 3D data decoding method of the present disclosure is a 3D data decoding method for decoding a plurality of 3D points with attribute information. Information indicating the number of 3D points belonging to each of the plurality of levels to which the plurality of 3D points belong is decoded from a bitstream, and using this information, the plurality of attribute information of the plurality of 3D points is decoded from the bitstream.

[0015] Advantages of the Invention

[0016] The present disclosure can provide a 3D data encoding method, a 3D data decoding method, a 3D data encoding device, or a 3D data decoding device that can shorten the processing time in a 3D data decoding device. Brief Description of the Drawings

[0017] Figure 1 Shows the configuration of the encoded 3D data of Embodiment 1.

[0018] Figure 2 Shows an example of the prediction structure between SPCs belonging to the lowest layer of the GOS of Embodiment 1.

[0019] Figure 3 Shows an example of the inter-layer prediction structure of Embodiment 1.

[0020] Figure 4 Shows an example of the encoding order of the GOS of Embodiment 1.

[0021] Figure 5 Shows an example of the encoding order of the GOS of Embodiment 1.

[0022] Figure 6 Is a block diagram of the 3D data encoding device of Embodiment 1.

[0023] Figure 7 Is a flowchart of the encoding process of Embodiment 1.

[0024] Figure 8 Is a block diagram of the 3D data decoding device of Embodiment 1.

[0025] Fig. 9 Is a flowchart of the decoding process of Embodiment 1.

[0026] Fig.10Shows an example of the meta-information of Embodiment 1.

[0027] Fig.11 Shows a configuration example of the SWLD of Embodiment 2.

[0028] Fig.12 Shows an operation example of the server and the client of Embodiment 2.

[0029] Fig.13 Shows an operation example of the server and the client of Embodiment 2.

[0030] Fig.14 Shows an operation example of the server and the client of Embodiment 2.

[0031] Fig.15 Shows an operation example of the server and the client of Embodiment 2.

[0032] Fig.16 Is a block diagram of the three-dimensional data encoding device of Embodiment 2.

[0033] Fig.17 Is a flowchart of the encoding process of Embodiment 2.

[0034] Fig.18 Is a block diagram of the three-dimensional data decoding device of Embodiment 2.

[0035] Fig.19 Is a flowchart of the decoding process of Embodiment 2.

[0036] Fig. 20 Shows a configuration example of the WLD of Embodiment 2.

[0037] Fig.21 Shows an example of the octree structure of the WLD of Embodiment 2.

[0038] Fig. 22 Shows a configuration example of the SWLD of Embodiment 2.

[0039] Fig.23 Shows an example of the octree structure of the SWLD of Embodiment 2.

[0040] Fig.24 Is a block diagram of the three-dimensional data production device of Embodiment 3.

[0041] Fig.25 Is a block diagram of the three-dimensional data sending device of Embodiment 3.

[0042] Fig.26 Is a block diagram of the three-dimensional information processing device of Embodiment 4.

[0043] Fig. 27It is a block diagram of the three-dimensional data production device of Embodiment 5.

[0044] Fig.28 It shows the configuration of the system of Embodiment 6.

[0045] Fig.29 It is a block diagram of the client device of Embodiment 6.

[0046] Fig.30 It is a block diagram of the server of Embodiment 6.

[0047] Fig.31 It is a flowchart of the three-dimensional data production process performed by the client device of Embodiment 6.

[0048] Fig.32 It is a flowchart of the sensor information transmission process performed by the client device of Embodiment 6.

[0049] Fig.33 It is a flowchart of the three-dimensional data production process performed by the server of Embodiment 6.

[0050] Fig.34 It is a flowchart of the three-dimensional map transmission process performed by the server of Embodiment 6.

[0051] Fig.35 It shows the configuration of a modified example of the system of Embodiment 6.

[0052] Fig.36 It shows the configuration of the server and client device of Embodiment 6.

[0053] Fig.37 It is a block diagram of the three-dimensional data encoding device of Embodiment 7.

[0054] Fig.38 It shows an example of the prediction residual of Embodiment 7.

[0055] Fig.39 It shows an example of the volume of Embodiment 7.

[0056] Fig.40 It shows an example of the octree representation of the volume of Embodiment 7.

[0057] Fig.41 It shows an example of the bit string of the volume of Embodiment 7.

[0058] Fig.42 It shows an example of the octree representation of the volume of Embodiment 7.

[0059] Fig.43 It shows an example of the volume of Embodiment 7.

[0060] Fig.44 This is a diagram for explaining the intra prediction processing of Embodiment 7.

[0061] Fig.45 This is a diagram for explaining the rotation and translation processing of Embodiment 7.

[0062] Fig.46 An example of the syntax of the RT applicability flag and RT information of Embodiment 7 is shown.

[0063] Fig.47 This is a diagram for explaining the inter prediction processing of Embodiment 7.

[0064] Fig.48 This is a block diagram of the 3D data decoding device of Embodiment 7.

[0065] Fig.49 This is a flowchart of the 3D data encoding process performed by the 3D data encoding device of Embodiment 7.

[0066] Fig.50 This is a flowchart of the 3D data decoding process performed by the 3D data decoding device of Embodiment 7.

[0067] Fig.51 This is a diagram showing the reference relationship in the octree structure of Embodiment 8.

[0068] Fig.52 This is a diagram showing the reference relationship in the spatial region of Embodiment 8.

[0069] Fig.53 This is a diagram showing an example of adjacent reference nodes of Embodiment 8.

[0070] Fig.54 This is a diagram showing the relationship between the parent node and the node of Embodiment 8.

[0071] Fig.55 This is a diagram showing an example of occupancy rate coding of the parent node of Embodiment 8.

[0072] Fig.56 This is a block diagram of the 3D data encoding device of Embodiment 8.

[0073] Fig.57 This is a block diagram of the 3D data decoding device of Embodiment 8.

[0074] Fig.58 This is a flowchart of the 3D data encoding process of Embodiment 8.

[0075] Fig.59 This is a flowchart of the 3D data decoding process of Embodiment 8.

[0076] Fig.60 This is a diagram showing an example of switching the coding table according to Embodiment 8.

[0077] Fig.61 This is a diagram showing the reference relationship in the spatial region according to Variant Example 1 of Embodiment 8.

[0078] Fig.62 This is a diagram showing a syntactic example of the header information according to Variant Example 1 of Embodiment 8.

[0079] Fig.63 This is a diagram showing a syntactic example of the header information according to Variant Example 1 of Embodiment 8.

[0080] Fig.64 This is a diagram showing an example of adjacent reference nodes according to Variant Example 2 of Embodiment 8.

[0081] Fig.65 This is a diagram showing examples of object nodes and adjacent nodes according to Variant Example 2 of Embodiment 8.

[0082] Fig.66 This is a diagram showing the reference relationship in the octree structure according to Variant Example 3 of Embodiment 8.

[0083] Fig.67 This is a diagram showing the reference relationship in the spatial region according to Variant Example 3 of Embodiment 8.

[0084] Fig.68 This is a diagram showing an example of three-dimensional points according to Embodiment 9.

[0085] Fig.69 This is a diagram showing a setting example of LoD according to Embodiment 9.

[0086] Fig.70 This is a diagram showing an example of the threshold value used in the setting of LoD according to Embodiment 9.

[0087] Fig.71 This is a diagram showing an example of the attribute information used in the predicted value according to Embodiment 9.

[0088] Fig.72 This is a diagram showing an example of the exponential Golomb code according to Embodiment 9.

[0089] Fig.73 This is a diagram showing the processing for the exponential Golomb code according to Embodiment 9.

[0090] Fig.74 This is a diagram showing a syntactic example of the attribute header according to Embodiment 9.

[0091] Fig.75 This is a diagram showing a syntactic example of the attribute data according to Embodiment 9.

[0092] Fig.76 It is a flowchart of the three-dimensional data encoding process of Embodiment 9.

[0093] Fig.77 It is a flowchart of the attribute information encoding process of Embodiment 9.

[0094] Fig.78 It is a diagram showing the processing for the Golomb-Rice code in Embodiment 9.

[0095] Fig.79 It is a diagram showing an example of the inverse table representing the relationship between the residual code and its value in Embodiment 9.

[0096] Fig.80 It is a flowchart of the three-dimensional data decoding process of Embodiment 9.

[0097] Fig.81 It is a flowchart of the attribute information decoding process of Embodiment 9.

[0098] Fig.82 It is a block diagram of the three-dimensional data encoding device of Embodiment 9.

[0099] Fig.83 It is a block diagram of the three-dimensional data decoding device of Embodiment 9.

[0100] Fig.84 It is a flowchart of the three-dimensional data encoding process of Embodiment 9.

[0101] Fig.85 It is a flowchart of the three-dimensional data decoding process of Embodiment 9.

[0102] Fig.86 It is a diagram showing an example of the system of Embodiment 10.

[0103] Fig.87 It is a flowchart of the attribute information decoding process of Embodiment 10.

[0104] Fig.88 It is a flowchart of the attribute information decoding process of Embodiment 10.

[0105] Fig.89 It is a block diagram of the attribute information decoding unit of Embodiment 10.

[0106] Fig.90 It is a diagram showing a syntactic example of the attribute header of Embodiment 10.

[0107] Fig.91 It is a diagram showing a syntactic example of the attribute data of Embodiment 10.

[0108] Fig.92 It is a diagram showing a modified example of the system of Embodiment 10.

[0109] Fig.93 It is a flowchart of a modified example of the attribute information decoding process of Embodiment 10.

[0110] Fig.94 It is a flowchart of the three-dimensional data encoding process of Embodiment 10.

[0111] Fig.95 It is a flowchart of the three-dimensional data decoding process of Embodiment 10. Detailed implementation manners

[0112] A three-dimensional data encoding method according to one aspect of the present disclosure is a three-dimensional data encoding method for encoding a plurality of three-dimensional points having attribute information, assigning each of the plurality of three-dimensional points to one of a plurality of hierarchies, using the hierarchy to encode a plurality of attribute information of the plurality of three-dimensional points, and encoding information indicating the number of three-dimensional points belonging to each of the plurality of hierarchies.

[0113] Thereby, when a three-dimensional data decoding device decodes a bitstream generated by this three-dimensional data encoding method, it can use the information contained in the bitstream to grasp the number of three-dimensional points included in each hierarchy. Thereby, the three-dimensional data decoding device does not need to perform processing for discriminating three-dimensional points belonging to each hierarchy using, for example, the position information of the three-dimensional points, etc., and thus can shorten the processing time.

[0114] For example, in the assignment, each of the plurality of three-dimensional points may be assigned to one of a plurality of hierarchies based on the distance between the plurality of three-dimensional points.

[0115] For example, in the encoding of the plurality of attribute information, with reference to the hierarchy to which the target three-dimensional point belongs and the three-dimensional points in the upper layer above this hierarchy, without referring to the three-dimensional points in the lower layer below this hierarchy, the attribute information of the target three-dimensional point is encoded.

[0116] A three-dimensional data decoding method according to one aspect of the present disclosure is a three-dimensional data decoding method for decoding a plurality of three-dimensional points having attribute information, decoding information indicating the number of three-dimensional points belonging to each of the plurality of hierarchies to which the plurality of three-dimensional points belong from a bitstream, and using the information to decode the plurality of attribute information of the plurality of three-dimensional points from the bitstream.

[0117] Thereby, the three-dimensional data decoding device can use the information contained in the bitstream to grasp the number of three-dimensional points included in each hierarchy. Thereby, the three-dimensional data decoding device does not need to perform processing for discriminating three-dimensional points belonging to each hierarchy using, for example, the position information of the three-dimensional points, etc., and thus can shorten the processing time.

[0118] For example, it may also be that, in the decoding of the plurality of attribute information, the information is used to determine the hierarchy to which the three-dimensional points belong.

[0119] For example, it may also be that, in the decoding of the plurality of attribute information, the attribute information of the three-dimensional points belonging to the object hierarchy is decoded, and the number of three-dimensional points belonging to the object hierarchy for which the attribute information has been decoded is counted, and the counted number of the three-dimensional points is compared with the number of the three-dimensional points represented by the information, thereby determining the hierarchy to which the three-dimensional points belong.

[0120] For example, it may also be that the three-dimensional data decoding method further assigns each of the plurality of three-dimensional points to one of a plurality of hierarchies, and performs parallel processing on at least a part of the processing of the decoding of the plurality of attribute information and at least a part of the processing of the assignment.

[0121] Thus, the three-dimensional data decoding method can shorten the processing time by performing parallel processing on a part of the processing to be performed.

[0122] For example, it may also be that, in the assignment, based on the distance between the plurality of three-dimensional points, each of the plurality of three-dimensional points is assigned to one of a plurality of hierarchies.

[0123] For example, it may also be that, in the decoding of the plurality of attribute information, with reference to the hierarchy to which the object three-dimensional points belong and the three-dimensional points of the upper layer above this hierarchy, without referring to the three-dimensional points of the lower layer below this hierarchy, the attribute information of the above object three-dimensional points is decoded.

[0124] In addition, a three-dimensional data encoding device according to one aspect of the present disclosure is a three-dimensional data encoding device that encodes a plurality of three-dimensional points having attribute information, and includes a processor and a memory. The processor uses the memory to assign each of the plurality of three-dimensional points to one of a plurality of hierarchies, encodes the plurality of attribute information of the plurality of three-dimensional points using the hierarchy, and encodes information indicating the number of three-dimensional points belonging to each of the plurality of hierarchies.

[0125] Thus, when the three-dimensional data decoding device decodes the bitstream generated by the three-dimensional data encoding device, it can use the information included in the bitstream to grasp the number of three-dimensional points included in each hierarchy. Thus, the three-dimensional data decoding device does not need to perform processing for discriminating the three-dimensional points belonging to each hierarchy using, for example, the position information of the three-dimensional points, etc., and therefore can shorten the processing time.

[0126] In addition, a three-dimensional data decoding device according to one aspect of the present disclosure is a three-dimensional data decoding device that decodes a plurality of three-dimensional points having attribute information, and includes a processor and a memory. The processor uses the memory to decode information representing the number of three-dimensional points belonging to each of a plurality of hierarchies to which the plurality of three-dimensional points belong from a bitstream, and uses the information to decode a plurality of attribute information of the plurality of three-dimensional points from the bitstream.

[0127] Accordingly, the three-dimensional data decoding device can grasp the number of three-dimensional points included in each hierarchy using the information included in the bitstream. Accordingly, the three-dimensional data decoding device does not need to perform a process of discriminating three-dimensional points belonging to each hierarchy using, for example, the position information of the three-dimensional points, and thus can shorten the processing time.

[0128] In addition, these general or specific forms 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.

[0129] Hereinafter, embodiments will be specifically described with reference to the drawings. In addition, all of the embodiments to be described below are specific examples showing 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 uppermost concept are described as optional constituent elements.

[0130] (Embodiment 1)

[0131] First, the 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.

[0132] In this embodiment, the three-dimensional space is divided into spaces (SPC) corresponding to pictures in the encoding of moving pictures, and three-dimensional data is encoded in units of space. The space is further divided into volumes (VLM) corresponding to macroblocks etc. in the encoding of moving pictures, and prediction and transformation are performed in units of VLM. A volume includes a plurality of voxels (VXL) which are the minimum units corresponding to position coordinates. In addition, prediction means, similar to the prediction performed in two-dimensional images, 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.

[0133] 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.

[0134] 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.

[0135] Also, hierarchical voxels can be used. In this case, in the n-th hierarchy, it is possible to sequentially show whether there are sampling points in the hierarchies below the (n - 1)-th hierarchy (the lower layer of the n-th hierarchy). For example, when only decoding the n-th hierarchy, when there are sampling points in the hierarchies below the (n - 1)-th hierarchy, it is possible to perform decoding by regarding that there are sampling points at the center of the voxels in the n-th hierarchy.

[0136] 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.

[0137] Regarding the space, similar to the encoding of moving pictures, 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 predictive 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: the decoding time and the display time.

[0138] And, as Figure 1As shown in the figure, 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).

[0139] The space area occupied by the world space corresponds 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.

[0140] Also, within a GOS, all SPCs can be three-dimensionally adjacent, or there can be SPCs that are not three-dimensionally adjacent to other SPCs.

[0141] In addition, hereinafter, processes such as encoding, decoding, or referring to the three-dimensional data included in processing units such as GOS, SPC, or VLM will also be 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.

[0142] 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 they occupy different spaces from each other, hold the same time information (decoding time and display time).

[0143] Also, 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.

[0144] 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.

[0145] Also, a GOS has a layer structure in the height direction, and encoding or decoding is performed sequentially starting from the SPCs in the bottom layer.

[0146] Figure 2 An example of the prediction structure between SPCs belonging to the bottom layer of a GOS is shown. Figure 3 An example of the prediction structure between layers is shown.

[0147] 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 the decoding device) decodes the GOS with a low processing amount or at high speed, it only decodes the I-SPCs in the GOS.

[0148] Moreover, the encoding device can switch the encoding interval or the appearance frequency of the I-SPC according to the density of the objects in the WLD.

[0149] Moreover, in Figure 3 In the configuration shown, the encoding device or the decoding device encodes or decodes 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.

[0150] 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.

[0151] Moreover, the encoding device or the decoding device can also encode or decode multiple layers in such a way that the decoding device roughly grasps the GOS and can gradually increase the resolution. For example, the encoding device or the decoding device can encode or decode in the order of layer 3, 8, 1, 9...

[0152] Next, a method for corresponding to static objects and dynamic objects will be described.

[0153] 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 data of point clouds, or images captured by a stereo camera or the like. Here, an example of an encoding method for dynamic objects will be described.

[0154] 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.

[0155] 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.

[0156] Alternatively, the 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 identification information.

[0157] Alternatively, the VLM or 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 identification information.

[0158] Moreover, the encoding device can encode the dynamic object as one or more VLMs or SPCs, and encode the VLMs or SPCs that include the static object and the SPCs that include the dynamic object 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.

[0159] 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 SPCs, and may be represented by one or more VLMs or VXLs.

[0160] Moreover, the encoding device can encode the static object and the dynamic object as different streams.

[0161] Moreover, the encoding device can also generate a GOS that includes one or more SPCs that constitute the 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, the overlapping process can be performed in units of GOS.

[0162] 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 the compression ratio.

[0163] Moreover, the above first method and second method can also be switched according to the use of the encoded data. For example, when the three-dimensional data is encoded and 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 activities such as concerts or sports, if there is no need to separate the dynamic object, the first method is adopted.

[0164] Furthermore, 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 the HRD (Hypothetical Reference Decoder) of HEVC, 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.

[0165] 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 spatially adjacent can be encoded continuously in the encoded data. For example, in Figure 4 the 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.

[0166] 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.

[0167] 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 according to, for example, whether it is in the city or indoors or outdoors, etc. That is, the size of GOS can be switched according to the quantity or nature of the object having information value. Alternatively, the encoding device can appropriately switch the size of GOS or the interval of I-SPC within GOS according to the density of the object, etc. in the same world space. 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.

[0168] In Figure 5In the example, in the region from the 3rd to the 10th GOS, due to the high density of objects, in order to achieve random access with a fine granularity, the GOS is subdivided. Also, the 7th to 10th GOS are respectively located on the back side of the 3rd to 6th GOS.

[0169] 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.

[0170] 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.

[0171] As Figure 7 shown, first, the acquisition unit 101 acquires three-dimensional data 111 as point cloud data (S101).

[0172] 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 of the user or the vehicle as the region to be encoded according to the position.

[0173] 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 size of the GOS set in advance, the presence or absence or size of dynamic objects (S103). And the division unit 103 determines the start position of the SPC that becomes the start in the encoding order in each GOS.

[0174] Next, the encoding unit 104 generates encoded three-dimensional data 112 by sequentially encoding a plurality of SPCs in each GOS (S104).

[0175] 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, the GOS can be encoded, and then the configuration of the GOS can be determined, etc.

[0176] In this way, the three-dimensional data encoding device 100 generates 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.

[0177] Next, the three-dimensional data encoding device 100 generates 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).

[0178] For example, when the first processing unit (GOS) of the processing object is a closed GOS, for the second processing unit (SPC) of the processing object included in the first processing unit (GOS) of the processing object, encoding is performed 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 unit (GOS) different from the first processing unit (GOS) of the processing object.

[0179] Moreover, when the first processing unit (GOS) of the processing object is an open GOS, for the second processing unit (SPC) of the processing object included in the first processing unit (GOS) of the processing object, encoding is performed 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 unit (GOS) different from the first processing unit (GOS) of the processing object.

[0180] Moreover, the three-dimensional data encoding device 100 selects one of a first type (I-SPC) that never refers to other second processing units (SPC), a second type (P-SPC) that refers to one other second processing unit (SPC), and a 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.

[0181] Next, the configuration and operation process of the three-dimensional data decoding device according to this embodiment will be described. Figure 8 is a block diagram of the three-dimensional data decoding device 200 according to this embodiment. Fig. 9 is a flowchart showing an operation example of the three-dimensional data decoding device 200.

[0182] 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.

[0183] 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.

[0184] 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, and (3) whether to decode all types. In addition, in the case where the type of SPC to be decoded, such as all SPCs, is specified in advance, this step may not be performed.

[0185] 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 decodes each SPC in sequence from the first SPC (S204). And the above address position is stored in meta-information or the like.

[0186] 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 respectively. More specifically, the three-dimensional data decoding device 200 decodes each of the multiple second processing units (SPC) in each of the first processing units (GOS). Further, the three-dimensional data decoding device 200 decodes each of the multiple third processing units (VLM) in each of the second processing units (SPC).

[0187] 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).

[0188] 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 or objects, etc.) in addition to time.

[0189] 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.

[0190] 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.

[0191] In addition, the address can be an address in a logical format or a physical address of an HDD or a memory. Also, information for determining 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.

[0192] 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.

[0193] 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.

[0194] 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).

[0195] 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.

[0196] 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.

[0197] 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 than in the suburbs, so the minimum size of the VLM is set smaller.

[0198] The meta information may also include a tag value indicating the type of the object. The 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 a property such as size, or whether it is a dynamic object or a static object may be used.

[0199] Furthermore, the meta information may also include information indicating the range of the spatial region occupied by the world space.

[0200] 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.

[0201] 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.

[0202] Furthermore, the meta information may include information indicating whether the world space is composed only of static objects or contains dynamic objects.

[0203] A modification example of the present embodiment will be described below.

[0204] The encoding device or the decoding device may perform encoding or decoding on 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.

[0205] 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 perform encoding or decoding on the GOS or SPC included in the space determined based on GPS, path information, or zoom ratio, etc.

[0206] Furthermore, the decoding device may also start decoding from the space close to its own position or the walking path in sequence. 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, lowering the priority means lowering the processing order, lowering the resolution (post-screening processing), or lowering the image quality (improving the encoding efficiency. For example, increasing the quantization step size), etc.

[0207] Furthermore, when decoding the encoded data hierarchically encoded within the space, the decoding device may also decode only the lower hierarchy.

[0208] Further, the decoding device may also start decoding from the lower layer according to the zoom ratio or use of the map.

[0209] Further, 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 may 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.

[0210] Further, the encoding device may 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.

[0211] Further, the encoding device may make the indoor GOS and the outdoor GOS with adjacent coordinates adjacent in the encoding stream for encoding. For example, the encoding device corresponds their identifiers and stores the information indicating the corresponding identifiers 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 adjacent coordinates by referring to the information in the meta-information.

[0212] Further, the encoding device may 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. Further, the encoding device may 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.

[0213] Further, the encoding device may 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 may also use only a red frame or explanatory text instead of the dynamic objects for display. And the decoding device can represent more detailed object categories. For example, a car may use a red frame and a person may use a yellow frame.

[0214] Further, the encoding device or the decoding device may determine whether to perform encoding or decoding by treating dynamic objects and static objects as different SPCs or GOSs according to the appearance frequency of the dynamic objects, or the ratio of static objects to 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.

[0215] When the dynamic object is detected from the two-dimensional image information of the camera instead of the point cloud, the encoding device can separately obtain the information (such as a box 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 (box or text) representing the dynamic object on the decoding result of the static object.

[0216] 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 more complex the shape of the static object is, the denser the encoding device sets VXL or VLM. 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 denser VXL or VLM is, the smaller the encoding device sets the quantization step size.

[0217] As described above, the encoding device or decoding device according to the present embodiment performs spatial encoding or decoding in a spatial unit having coordinate information.

[0218] Moreover, the encoding device and the decoding device perform encoding or decoding in a volume unit within the space. The volume includes voxels, which are the smallest units corresponding to the position information.

[0219] Moreover, the encoding device and the decoding device establish correspondences between any elements by using a table in which each element of the spatial information including coordinates, objects, and time, etc. is associated with a GOP, or a table corresponding between the elements, 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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 performs encoding in a manner that reduces the degradation of the I-SPC (after decoding, the original three-dimensional data can be reproduced more faithfully). And, for example, the decoding device decodes only the I-SPC.

[0225] 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, when the density of the objects in the world space is greater, the encoding device increases the frequency of using the I space.

[0226] And, 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.

[0227] The encoding device, for example, uses a default value as the spatial size of the GOS. 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, when the objects or dynamic objects are denser or the quantity is larger, the encoding device sets the spatial size of the GOS to be smaller.

[0228] And, 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 of the position information can be achieved.

[0229] The feature point group is derived using multiple pictures. The multiple pictures have at least the following two kinds 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.).

[0230] And, encoding or decoding is performed in units of GOS that includes more than one space.

[0231] 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.

[0232] 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 the B space within the GOS of the object to be processed.

[0233] Moreover, the encoding device and the decoding device send or receive an encoded stream in units of a world space including one or more GOSs.

[0234] 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 a plurality of 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.

[0235] Moreover, the encoding device and the decoding device continuously perform encoding or decoding on GOSs within a world space unit including a plurality of 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 a plurality of GOSs.

[0236] Moreover, the encoding device and the decoding device perform encoding or decoding on two or more different spaces or GOSs in parallel.

[0237] Moreover, the encoding device and the decoding device perform encoding or decoding on the spatial information (coordinates, size, etc.) of a space or GOS.

[0238] Moreover, the encoding device and the decoding device perform encoding or decoding on 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 area size.

[0239] 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.

[0240] The encoding device sets one direction in the world space according to magnification or use, and performs encoding on a GOS having a layer structure in that direction. And the decoding device preferentially performs decoding starting from the lower layer on a GOS having a layer structure in one direction of the world space set according to magnification or use.

[0241] The encoding device changes the extraction of feature points, the accuracy of object recognition, or the size of the spatial region 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 adjacent coordinates adjacent to each other in the world space, and also encode or decode by corresponding these identifiers to each other.

[0242] (Embodiment 2)

[0243] 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 use. 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.

[0244] In this embodiment, a three-dimensional data encoding method, a three-dimensional data encoding device for providing a function of transmitting and receiving the required information according to the use in the encoded data of the three-dimensional point cloud, a three-dimensional data decoding method for decoding the encoded data, and a three-dimensional data decoding device will be described.

[0245] A voxel (VXL) having a feature amount equal to or more than a certain value is defined as a feature voxel (FVXL), and a world space (WLD) composed of FVXLs 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 FVXLs; FSPC, which is an SPC composed of FVXLs; and FVLM, which is a VLM composed of FVXLs. The data structure and the prediction structure of FGOS, FSPC, and FVLM may be the same as those of GOS, SPC, and VLM.

[0246] The feature amount refers to a feature amount that represents the three-dimensional position information of the VXL or the visible light information of the VXL position, and in particular, a feature amount that can detect more features such as 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, as long as it is a feature amount that represents the position, brightness, or color information of the VXL, it can be any feature amount.

[0247] 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.

[0248] The SHOT feature quantity is obtained by segmenting the periphery of the VXL and calculating the inner product of the normal vector of the reference point and the segmented region, and then performing histogramming. This SHOT feature quantity has the characteristics of high dimensionality and high feature expressiveness.

[0249] 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 then 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.

[0250] 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.

[0251] 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 the image can be used.

[0252] The SWLD is generated by calculating the above-mentioned 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.

[0253] The SWLD can be generated for each feature quantity. For example, as shown by SWLD1 based on the SHOT feature quantity and 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.

[0254] 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.

[0255] In an application that uses feature quantities to achieve a certain purpose, by using the information of SWLD instead of WLD, it is possible to suppress the read time from the hard disk and suppress the bandwidth and transmission time during network transmission. 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, it is possible to suppress the network bandwidth and transmission time. The following shows a specific example.

[0256] Fig.12 And Fig.13 shows an example 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 request (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.

[0257] 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 request (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.

[0258] 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, it is possible to efficiently transmit and receive map data.

[0259] 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 status of the client or the network.

[0260] Next, a method for switching the reception and transmission between the Sparse World Space (SWLD) and the World Space (WLD) will be described.

[0261] The reception of the WLD or 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 with a network bandwidth such as in 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 a surplus network bandwidth such as in 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.

[0262] Specifically, the client receives the SWLD via LTE outdoors, and when entering indoors such as in a facility, the client obtains the WLD via WiFi. Accordingly, the client can obtain more detailed map information of the interior.

[0263] In this way, the client can request the WLD or 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.

[0264] Moreover, the reception of the WLD or 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 a small data volume, the client can generally update the map information at an appropriate speed. Additionally, when the client is driving on an ordinary road, by receiving the WLD, the client can obtain more detailed map information.

[0265] 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.

[0266] Moreover, it can also be that the client first obtains the SWLD from the server and then obtains the WLD of the important regions therein. For example, when the client obtains map data, it first obtains the general map information in the form of SWLD, filters out the regions where there are many features such as buildings, signs, or people, and then obtains the WLD of the filtered regions. Accordingly, the client can both suppress the amount of received data from the server and obtain the detailed information of the required regions.

[0267] Furthermore, it can also be that the server separately creates the SWLD for each object according to the WLD, and the client receives them separately 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 (such as signs or types) attached to the head.

[0268] Next, the configuration and the working process of the three-dimensional data encoding device (such as the 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.

[0269] 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 region determination unit 402, an SWLD extraction unit 403, a WLD encoding unit 404, and an SWLD encoding unit 405.

[0270] 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).

[0271] Next, the encoding region determination unit 402 determines the spatial region of the encoding target according to the spatial region where the point cloud data exists (S402).

[0272] Next, the SWLD extraction unit 403 defines the spatial region of the encoding target as the WLD, and calculates the feature amount according to 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.

[0273] Next, the WLD encoding unit 404 generates the encoded three-dimensional data 413 corresponding to the WLD by encoding the input three-dimensional data 411 corresponding to the WLD (S404). At this time, the WLD encoding unit 404 attaches the information for distinguishing that the encoded three-dimensional data 413 is a stream including the WLD to the header of the encoded three-dimensional data 413.

[0274] Further, the SWLD encoding unit 405 generates the encoded three-dimensional data 414 corresponding to the SWLD by encoding the extracted three-dimensional data 412 corresponding to the SWLD (S405). At this time, the SWLD encoding unit 405 attaches the information for distinguishing that the encoded three-dimensional data 414 is a stream including the SWLD to the header of the encoded three-dimensional data 414.

[0275] Further, the processing order of the process of generating the encoded three-dimensional data 413 and the process of generating the encoded three-dimensional data 414 may be opposite to the above. Further, a part or all of the above processes may be executed in parallel.

[0276] The information given to the headers of the encoded 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 encoded three-dimensional data 413 and 414. For example, the encoded 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 according to the presence or absence of the flag.

[0277] Further, the encoding method used by the WLD encoding unit 404 when encoding the WLD may be different from the encoding method used by the SWLD encoding unit 405 when encoding the SWLD.

[0278] 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.

[0279] Also, the expression methods of three-dimensional positions may be different between 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.

[0280] Moreover, 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.

[0281] 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. Or, the quantization level in the SWLD encoding unit 405 can be made coarser. For example, in the octree structure described later, the quantization level can be made coarser by rounding the data in the bottom layer.

[0282] 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. Or, the encoded three-dimensional data 413 of WLD can be copied to the encoded three-dimensional data 414 of SWLD. That is, the encoded three-dimensional data 413 of WLD can be directly used as the encoded three-dimensional data 414 of SWLD.

[0283] Next, the configuration and operation process of the three-dimensional data decoding device (such as 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.

[0284] 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.

[0285] The three-dimensional data decoding device 500 includes: an acquisition unit 501, a header analysis unit 502, a WLD decoding unit 503, and an SWLD decoding unit 504.

[0286] 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 containing WLD or a stream containing SWLD (S502). For example, the determination is made with reference to the above-mentioned world_type parameter.

[0287] In the case where the encoded three-dimensional data 511 is a stream containing WLD (Yes in S503), the WLD decoding unit 503 decodes the encoded three-dimensional data 511 to generate the decoded three-dimensional data 512 of WLD (S504). In addition, in the case where the encoded three-dimensional data 511 is a stream containing SWLD (No in S503), the SWLD decoding unit 504 decodes the encoded three-dimensional data 511 to generate the decoded three-dimensional data 513 of SWLD (S505).

[0288] 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 given priority compared to the decoding method for WLD.

[0289] Also, 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.

[0290] 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 referred to as valid 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.

[0291] 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 valid VXLs 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.

[0292] 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 are 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 the node 3 shown has no valid 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.

[0293] The following describes a modification example of the present embodiment.

[0294] For example, it may also be the case that when a client such as a vehicle-mounted 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.

[0295] 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.

[0296] Also, when quickly rendering three-dimensional map data on the client side, 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 rendering, it receives the MWLD, and when it needs to perform detailed three-dimensional rendering, it receives the WLD. Accordingly, the network bandwidth can be suppressed.

[0297] Also, although the server sets the VXLs with feature amounts above the threshold as FVXLs from each VXL, the FVXLs can also be calculated by different methods. For example, if the server determines that VXLs, VLMs, SPCs, or GOSs that make up signals or intersections are required for self-position estimation, driving assistance, or autonomous driving, etc., they can be included in the SWLD as FVXLs, FVLMs, FSPCs, and FGOSs. And the above determination can be made manually. In addition, the FVXLs obtained by the above method can be added to the FVXLs set based on the feature amount. That is, the SWLD extraction unit 403 can further extract data corresponding to an object having a predetermined attribute from the input three-dimensional data 411 as the extracted three-dimensional data 412.

[0298] Also, different labels from the feature amounts can be assigned to the situations required for these uses. The server can separately maintain the FVXLs 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).

[0299] 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.).

[0300] Also, as a method for updating the WLD or SWLD, the following method can be adopted.

[0301] 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. The server updates the WLD based on this load, and then updates the SWLD using the updated WLD.

[0302] 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, it can send the three-dimensional information generated by itself to the server together with an update notification. In this case, the server updates the SWLD using the WLD. If the SWLD is not updated, the server determines that the WLD itself is old.

[0303] Also, as header information of the encoded stream, information for distinguishing between WLD and SWLD is attached. For example, in the 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, when there are multiple SWLDs with different feature amounts, information for distinguishing them separately can also be attached to the header information.

[0304] 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.

[0305] 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 above a 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.

[0306] Accordingly, the three-dimensional data encoding device 400 generates the encoded three-dimensional data 414 obtained by encoding data whose feature amount is above a 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.

[0307] 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.

[0308] 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 and the like.

[0309] Moreover, the extracted three-dimensional data 412 is encoded by the first encoding method, and the input three-dimensional data 411 is encoded by the second encoding method different from the first encoding method.

[0310] 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.

[0311] Moreover, in the first encoding method, among intra prediction and inter prediction, inter prediction is prioritized compared to the second encoding method.

[0312] 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.

[0313] 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.

[0314] 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).

[0315] 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.

[0316] 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.

[0317] 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.

[0318] 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.

[0319] 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 in self-position estimation, driving assistance, or autonomous driving, etc., such as a signal or an intersection.

[0320] Accordingly, the three-dimensional data encoding device 400 can generate the encoded three-dimensional data 414 including the data required by the decoding device.

[0321] Further, the three-dimensional data encoding device 400 (server) sends one of the encoded three-dimensional data 413 and 414 to the client according to the state of the client.

[0322] Accordingly, the three-dimensional data encoding device 400 can send appropriate data according to the state of the client.

[0323] Moreover, the state of the client includes the communication status of the client (e.g., network bandwidth) or the moving speed of the client.

[0324] Further, the three-dimensional data encoding device 400 sends one of the encoded three-dimensional data 413 and 414 to the client according to the request of the client.

[0325] Accordingly, the three-dimensional data encoding device 400 can send appropriate data according to the request of the client.

[0326] 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.

[0327] 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 a threshold 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.

[0328] 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. 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.

[0329] Also, in the first decoding method, among intra prediction and inter prediction, inter prediction is prioritized compared to the second decoding method.

[0330] 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.

[0331] Also, the representation methods of three-dimensional positions are different between the first decoding method and the second decoding method. 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.

[0332] 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 VXLs or FVXLs).

[0333] 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 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. The three-dimensional data decoding device 500 identifies the encoded three-dimensional data 413 and 414 with reference to this identifier.

[0334] 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.

[0335] 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.

[0336] Accordingly, the three-dimensional data decoding device 500 can receive appropriate data according to the state of the client.

[0337] Also, the state of the client includes the communication status of the client (e.g., network bandwidth) or the moving speed of the client.

[0338] 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.

[0339] Accordingly, the three-dimensional data decoding device 500 can receive appropriate data corresponding to the usage.

[0340] (Embodiment 3)

[0341] 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.

[0342] 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.

[0343] 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.

[0344] First, the three-dimensional data creation unit 621 creates the first three-dimensional data 632 using sensor information 631 detected by sensors included 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.

[0345] 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 through 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 indiscriminately to all vehicles present within the identified range, and receive the encoded three-dimensional data 634 from the responding parties. Also, 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.

[0346] 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.

[0347] 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.

[0348] The three-dimensional data transmission device 640 is included, for example, in the surrounding vehicles mentioned 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.

[0349] 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.

[0350] 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.

[0351] 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.

[0352] In addition, here, although an example in which its 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.

[0353] (Embodiment 4)

[0354] In this embodiment, the operations related to abnormal conditions in the estimation of the own position based on the three-dimensional map are described.

[0355] 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 (own position estimation) and travels according to the map.

[0356] The own 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 distance measuring instrument (LIDAR, etc.) or a stereo camera mounted on the own vehicle, and estimating the position of the own vehicle within the three-dimensional map.

[0357] A 3D map, such as the HD map proposed by HERE Technologies, etc., is not only a 3D point cloud, but may also include 2D map data such as road and intersection shape information, or information that changes in real time such as traffic jams and accidents. The 3D map is composed of multiple layers such as 3D data, 2D data, and metadata that changes in real time. The device can obtain only the required data, or can also refer to the required data.

[0358] The data of the point cloud can be the above-mentioned SWLD, or can also include point cloud data that is not feature points. And the transmission and reception of the data of the point cloud are basically performed in one or more random access units.

[0359] As a method for matching a 3D map with the 3D data detected by the host vehicle, the following method can be adopted. For example, the device compares the shapes of the point clouds of each other, and determines the part with a high similarity between feature points as the same position. And when the 3D map is composed of SWLD, the device compares the feature points that make up the SWLD with the 3D feature points extracted from the 3D data detected by the host vehicle and performs matching.

[0360] Here, in order to estimate the host vehicle position with high accuracy, the following (A) and (B) need to be satisfied. (A) The 3D map and the 3D data detected by the host vehicle can already be obtained. (B) Their accuracies satisfy a predetermined standard. However, in the following abnormal situations, (A) or (B) cannot be satisfied.

[0361] (1) The 3D map cannot be obtained through the communication path.

[0362] (2) There is no 3D map, or the obtained 3D map is damaged.

[0363] (3) The sensors of the host vehicle malfunction, or due to bad weather, the generation accuracy of the 3D data detected by the host vehicle is insufficient.

[0364] The following describes the operations for dealing with these abnormal situations. Although the following describes the operations 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.

[0365] What will be described below is the configuration and operation of the 3D information processing device according to the present embodiment for dealing with abnormal situations in the 3D map or the 3D data detected by the host vehicle. Fig.26 It is a block diagram showing a configuration example of the 3D information processing device 700 according to the present embodiment.

[0366] The 3D 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 host vehicle detection data acquisition unit 702, an abnormal situation determination unit 703, a response operation determination unit 704, and an operation control unit 705.

[0367] In addition, the three-dimensional information processing device 700 may also include a camera for acquiring a two-dimensional image, or may include a two-dimensional or one-dimensional sensor (not shown) such as a sensor for one-dimensional data using ultrasonic waves or lasers for detecting structural objects or moving objects around the host vehicle. Further, the three-dimensional information processing device 700 may also include a communication unit (not shown) for acquiring a three-dimensional map through a mobile communication network such as 4G or 5G, vehicle-to-vehicle communication, or road-to-vehicle communication.

[0368] 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, vehicle-to-vehicle communication, or road-to-vehicle communication.

[0369] Next, the host vehicle detection data acquisition unit 702 acquires host vehicle detection three-dimensional data 712 based on sensor information. For example, the host vehicle detection data acquisition unit 702 generates the host vehicle detection three-dimensional data 712 based on the sensor information acquired by the sensors equipped on the host vehicle.

[0370] 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 host 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 host vehicle detection three-dimensional data 712 is abnormal.

[0371] 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.

[0372] In addition, when no abnormal situation is detected, the three-dimensional information processing device 700 ends the processing.

[0373] Furthermore, the three-dimensional information processing device 700 estimates the own position of the vehicle equipped with the three-dimensional information processing device 700 by using the three-dimensional map 711 and the host vehicle detection three-dimensional data 712. Next, the three-dimensional information processing device 700 uses the result of the own position estimation to perform autonomous driving of the vehicle.

[0374] 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, and 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.

[0375] 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.

[0376] 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.

[0377] 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.

[0378] (Embodiment 5)

[0379] In this embodiment, a method for transmitting three-dimensional data to a following vehicle and the like will be described.

[0380] Fig. 27 FIG. is a block diagram showing a configuration example of a three-dimensional data production device 810 according to this 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 at the same time produces and stores the three-dimensional data.

[0381] 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.

[0382] 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 sensor 815 of its own vehicle.

[0383] The communication unit 812 communicates with the traffic cloud monitoring or the vehicle ahead, and sends data transmission requests, etc. to the traffic cloud monitoring or the vehicle ahead.

[0384] 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.

[0385] The format conversion unit 814 generates three-dimensional data 832 by performing format conversion, etc. on the three-dimensional data 831 received by the data receiving unit 811. Moreover, when the three-dimensional data 831 is compressed or encoded, the format conversion unit 814 performs decompression or decoding processing.

[0386] 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 sensor 815 may not be plural.

[0387] The three-dimensional data production 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.

[0388] The three-dimensional data synthesis unit 817 synthesizes the three-dimensional data 832 produced by the traffic cloud monitoring or the vehicle ahead, etc. into the three-dimensional data 834 produced based on the sensor information 833 of its own vehicle, 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 sensor 815 of its own vehicle.

[0389] The three-dimensional data storage unit 818 stores the generated three-dimensional data 835, etc.

[0390] The communication unit 819 communicates with the traffic cloud monitoring or the vehicle behind, and sends data transmission requests, etc. to the traffic cloud monitoring or the vehicle behind.

[0391] 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.

[0392] 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 of the space that can be transmitted or the 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. Further, the transmission control unit 820 notifies the format conversion unit 821 of the format corresponding to the communication partner and the transmission area.

[0393] 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.

[0394] 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 includes information on the area that is a blind spot for the following vehicle.

[0395] In addition, although the format conversion units 814 and 821 have been described as examples for performing format conversion and the like, format conversion may not be performed.

[0396] 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.

[0397] 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 or the like in accordance with the data transmission request from the traffic cloud monitoring or the following vehicle.

[0398] (Embodiment 6)

[0399] 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.

[0400] 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, 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.

[0401] 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.

[0402] 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.

[0403] The client device 902 sends the sensor information obtained by the client device 902 to the server 901. The sensor information includes, for example, at least one of the information obtained by LiDAR, visible light images, infrared images, depth images, sensor position information, and speed information.

[0404] 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.

[0405] Furthermore, in response to a request from the client device 902 to send a 3D map, the server 901 sends the 3D map managed by the server 901 to the client device 902. Additionally, the server 901 may send the 3D map without waiting for a request from the client device 902 to send the 3D map. For example, the server 901 may broadcast the 3D map to one or more client devices 902 in a pre-specified space. Also, the server 901 may send a 3D map suitable for the location of the client device 902 to the client device 902 that has received a send request once, at regular intervals. Moreover, the server 901 may send the 3D map to the client device 902 whenever the 3D map managed by the server 901 is updated.

[0406] The client device 902 sends a request to the server 901 to send a 3D map. For example, when the client device 902 wants to estimate its own position while in motion, the client device 902 sends a request to the server 901 to send a 3D map.

[0407] In addition, the client device 902 may send a request to the server 901 to send a 3D map in the following cases. When the 3D map held by the client device 902 is relatively old, the client device 902 may send a request to the server 901 to send a 3D map. For example, when a certain period has elapsed since the client device 902 obtained the 3D map, the client device 902 may send a request to the server 901 to send a 3D map.

[0408] It may also be that, a certain time before the client device 902 is about to leave the space shown in the 3D map held by the client device 902, the client device 902 sends a request to the server 901 to send a 3D map. For example, it may also be that when the client device 902 is within a pre-specified distance from the boundary of the space shown in the 3D map held by the client device 902, the client device 902 sends a request to the server 901 to send a 3D map. And, when the movement path and movement speed of the client device 902 are known, the time 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 known movement path and movement speed.

[0409] When the error in the position comparison between the 3D data created by the client device 902 based on sensor information and the 3D map is above a certain range, the client device 902 may send a request to the server 901 to send a 3D map.

[0410] The client device 902 sends sensor information to the server 901 in accordance with a transmission request for 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 a transmission request for the sensor information from the server 901. For example, when the client device 902 has received a transmission request for the sensor information from the server 901 once, it may periodically send the sensor information to the server 901 within a certain period. Also, when the error in the comparison of the three-dimensional data created based on the sensor information by the client device 902 and the position of 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 determination result together with the sensor information to the server 901.

[0411] The server 901 issues a transmission request for the sensor information to the client device 902. For example, the server 901 receives location information of the client device 902 such as GPS from the client device 902. When the server 901 determines, based on the location information of the client device 902, that the client device 902 is approaching a space with less information in the three-dimensional map managed by the server 901, it issues a transmission request for the sensor information to the client device 902 in order to regenerate the three-dimensional map. Also, when the server 901 wants to update the three-dimensional map, when it wants to confirm road conditions such as during snow accumulation or disasters, or when it wants to confirm congestion conditions or accident conditions, etc., it may also issue a transmission request for the sensor information.

[0412] Also, the client device 902 may set 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.

[0413] 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 acquired sensor information to the server 901.

[0414] 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.

[0415] 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.

[0416] The communication unit 1012 communicates with the server 901 and sends a data transmission request (for example, a request for transmitting a three-dimensional map) to the server 901.

[0417] The reception control unit 1013 exchanges information such as corresponding formats with the communication partner via the communication unit 1012 and establishes communication with the communication partner.

[0418] 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.

[0419] The plurality of sensors 1015 are a group of sensors mounted on the client device 902 such as LiDAR, visible light cameras, infrared cameras, or depth sensors for obtaining 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.

[0420] 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 images obtained by visible light cameras to create point cloud data with color information around its own vehicle.

[0421] The 3D image processing unit 1017 performs self-position estimation processing of the host vehicle using the received 3D map 1032 such as point cloud and the 3D data 1034 of the surroundings of the host vehicle generated based on the sensor information 1033. Alternatively, the 3D image processing unit 1017 may synthesize the 3D map 1032 and the 3D data 1034 to create the 3D data 1035 of the surroundings of the host vehicle, and perform self-position estimation processing using the created 3D data 1035.

[0422] The 3D data storage unit 1018 stores the 3D map 1032, the 3D data 1034, the 3D data 1035, etc.

[0423] 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 may reduce the data volume by compressing or encoding the sensor information 1037. Moreover, in the case where format conversion is not required, the format conversion unit 1019 may omit the processing. And the format conversion unit 1019 can control the data volume to be transmitted according to the specified transmission range.

[0424] The communication unit 1020 communicates with the server 901 and receives a data transmission request (a transmission request for sensor information), etc. from the server 901.

[0425] The transmission control unit 1021 exchanges information such as the corresponding format with the communication partner via the communication unit 1020 to establish communication.

[0426] The data transmission unit 1022 transmits 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. obtained by a plurality of sensors 1015.

[0427] Next, the configuration of the server 901 will be described. Fig.30 FIG. is a block diagram showing a configuration example of the server 901. The server 901 receives the sensor information transmitted 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 transmits the updated 3D map to the client device 902 in accordance with the transmission request for the 3D map from the client device 902.

[0428] 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.

[0429] 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, and speed information.

[0430] The communication unit 1112 communicates with the client device 902 and sends a data transmission request (e.g., a transmission request for sensor information) to the client device 902.

[0431] The reception control unit 1113 exchanges information such as the corresponding format with the communication partner via the communication unit 1112 to establish communication.

[0432] 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.

[0433] The three-dimensional data creation unit 1116 creates three-dimensional data 1134 around 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 around the client device 902.

[0434] 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.

[0435] The three-dimensional data storage unit 1118 stores the three-dimensional map 1135 and the like.

[0436] 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.

[0437] 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.

[0438] The transmission control unit 1121 exchanges information such as the corresponding format with the communication partner via the communication unit 1120 to establish communication.

[0439] 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 may be included in the three-dimensional map 1031.

[0440] Next, the operation flow of the client device 902 will be described. Fig.31 It is a flowchart showing the operations when the client device 902 obtains a three-dimensional map.

[0441] 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 by GPS, etc. Accordingly, it is possible to request the server 901 to send a three-dimensional map related to this position information.

[0442] 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).

[0443] Next, the client device 902 creates three-dimensional data 1034 around 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 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).

[0444] Fig.32This is a flowchart showing the operation when the client device 902 transmits sensor information. First, the client device 902 receives a sensor information transmission request 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 by multiple sensors 1015, the client device 902 compresses each piece of information in a compression method suitable for each piece of information to generate the sensor information 1037.

[0445] Next, the operation process of the server 901 will be described. Fig.33 This 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).

[0446] Fig.34 This is a flowchart showing the operation when the server 901 transmits the three-dimensional map. First, the server 901 receives a three-dimensional map transmission request from the client device 902 (S1031). The server 901 that has received the three-dimensional map transmission request transmits the three-dimensional map 1031 to the client device 902 (S1032). At this time, the server 901 can extract the three-dimensional map near it 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.

[0447] Hereinafter, a modified example of this embodiment will be described.

[0448] 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. Next, 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 natural disasters such as earthquakes, 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.

[0449] 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. Also, it may 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 can be 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 according to the model of the vehicle. In this case, the server 901 can obtain the information of the vehicle model in advance, or can include this information in the sensor information. Also, it may 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 for 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.

[0450] The server 901 can also send a request to send sensor information to multiple client devices 902 existing in a certain space 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 utilize the selected sensor information to produce the three-dimensional data 1134.

[0451] The server 901 is not limited by 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.

[0452] For example, the client device 902C sends a request to send sensor information to the client device 902A existing nearby, and obtains the sensor information from the client device 902A. Then, the client device 902C utilizes 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.

[0453] 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.

[0454] It can also be that the client device 902C sends a request to send sensor information to multiple client devices 902 (client device 902A and client device 902B) existing nearby. When the sensor of the client device 902A or the client device 902B is of high performance, the client device 902C can utilize the sensor information obtained through this high-performance sensor to produce three-dimensional data.

[0455] 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 that compresses and decodes the three-dimensional map, and a sensor information compression / decoding processing unit 1202 that compresses and decodes the sensor information.

[0456] 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 acquired 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.

[0457] As described above, the client device 902 according to the present embodiment is mounted on a moving body, and creates three-dimensional data 1034 of the surroundings of the moving body based on the sensor information 1033 showing the surroundings of the moving body obtained by the sensor 1015 mounted on the moving body. The client device 902 estimates its own position of the moving body by using the created three-dimensional data 1034. The client device 902 sends the acquired sensor information 1033 to the server 901 or another moving body 902.

[0458] 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 transmitted can be reduced compared with the case of transmitting three-dimensional data. And, since there is no need to perform processing such as compression or encoding of three-dimensional data in the client device 902, the amount of processing of the client device 902 can be reduced. Therefore, the client device 902 can achieve a reduction in the amount of transmitted data or a simplification of the device configuration.

[0459] Furthermore, 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 by using the three-dimensional data 1034 and the three-dimensional map 1032 in the estimation of its own position.

[0460] And, 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.

[0461] And, the sensor information 1033 includes the information showing the performance of the sensor.

[0462] Further, the client device 902 encodes or compresses the sensor information 1033, and in the transmission of the sensor information, transmits 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.

[0463] For example, the client device 902 includes a processor and a memory, and the processor uses the memory to perform the above processing.

[0464] 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 showing the surrounding conditions of the moving body obtained by the sensor 1015 mounted on the moving body from the client device 902. The server 901 creates three-dimensional data 1134 of the surroundings of the moving body based on the received sensor information 1037.

[0465] Accordingly, the server 901 uses the sensor information 1037 transmitted from the client device 902 to create the three-dimensional data 1134. In this way, compared with the case where the client device 902 transmits three-dimensional data, there is a possibility of reducing the amount of data to be transmitted. And since it is not necessary to perform processing such as compression or encoding of the 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.

[0466] Moreover, the server 901 further sends a transmission request for the sensor information to the client device 902.

[0467] Moreover, the server 901 further updates the three-dimensional map 1135 using the created three-dimensional data 1134, and transmits the three-dimensional map 1135 to the client device 902 in accordance with a transmission request for the three-dimensional map 1135 from the client device 902.

[0468] Moreover, the sensor information 1037 includes at least one of 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.

[0469] Moreover, the sensor information 1037 includes information showing the performance of the sensor.

[0470] Moreover, the server 901 further corrects the three-dimensional data according to the performance of the sensor. Accordingly, this three-dimensional data creation method can improve the quality of the three-dimensional data.

[0471] Also, 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.

[0472] Also, the server 901 decodes or decompresses the received sensor information 1037, and produces the three-dimensional data 1134 based on the decoded or decompressed sensor information 1132. Accordingly, the server 901 can reduce the amount of data transmitted.

[0473] For example, the server 901 includes a processor and a memory, and the processor uses the memory to perform the above processing.

[0474] (Embodiment 7)

[0475] In the present embodiment, an encoding method and a decoding method for three-dimensional data using inter-frame prediction processing will be described.

[0476] Fig.37 is a block diagram of a three-dimensional data encoding device 1300 according to the present embodiment. The three-dimensional data encoding device 1300 encodes three-dimensional data to generate an encoded bitstream (hereinafter also simply referred to as a bitstream) as an encoded signal. As Fig.37 shown, the three-dimensional data encoding device 1300 includes: a segmentation 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.

[0477] The segmentation unit 1301 divides each space (SPC) included in the three-dimensional data into a plurality of volumes (VLM) as encoding units. Also, the segmentation unit 1301 performs octree representation (octree conversion) on the voxels within each volume. Additionally, the segmentation unit 1301 may make the space and the volume the same size and perform octree representation on the space. Also, the segmentation unit 1301 may attach information (such as depth information) required for octree conversion to the head of the bitstream.

[0478] The subtraction unit 1302 calculates the difference between the volume (encoding target volume) output from the segmentation 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 encoded 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.

[0479] 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 encoded. The octree structure consists 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 the Fig.39 example of transforming the shown volume 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 a VXL including a point group (hereinafter referred to as an effective VXL).

[0480] 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 value 1 and the rest are set to value 0, the binary sequences shown in Fig.40 are 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 scanned in breadth-first, the binary sequence shown in Fig.41 A is obtained. When scanned in depth-first, the binary sequence shown in Fig.41 B is obtained. The binary sequence obtained by this scanning is encoded by entropy coding, thereby reducing the amount of information.

[0481] 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 retained. 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 the same position and the same color, so the information originally possessed by the point cloud information will be lost.

[0482] For example, Fig.42 shows an example of representing the octree with depth = 2 shown in Fig.40 as an octree with depth = 1. Fig.42 The octree shown has less data volume than the octree shown in Fig.40 . 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.

[0483] 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.

[0484] 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.

[0485] 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.

[0486] Further, 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 into a two-dimensional arrangement in a certain scanning order, and applies a two-dimensional orthogonal transformation to the obtained two-dimensional arrangement. Further, 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 apparatus 1300 attaches information indicating which orthogonal transformation method is used to the bitstream. Further, it may 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 apparatus 1300 attaches information indicating which dimensional orthogonal transformation method is used to the bitstream.

[0487] 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 it is not necessary to attach information indicating the scanning order of the prediction residual to the bitstream, the overhead can be reduced. Further, 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 apparatus 1300 attaches information indicating the scanning order of the prediction residual to the bitstream. Accordingly, the three-dimensional data encoding apparatus 1300 can efficiently encode the prediction residual. Further, it may be that the three-dimensional data encoding apparatus 1300 attaches information (such as a flag) indicating whether the scanning order of the octree is applied to the bitstream, and when the scanning order of the octree is not applied, attaches information indicating the scanning order of the prediction residual to the bitstream.

[0488] The transformation unit 1303 may transform not only the prediction residual of the color information but also other attribute information possessed by the voxel. For example, it may be that the transformation unit 1303 transforms and encodes information such as reflectance obtained when obtaining a point cloud by LiDAR or the like.

[0489] When the space does not have attribute information such as color information, the transformation unit 1303 may skip the processing. Further, the three-dimensional data encoding apparatus 1300 may attach information (such as a flag) indicating whether to skip the processing of the transformation unit 1303 to the bitstream.

[0490] The quantization unit 1304 quantizes the frequency components of the prediction residuals generated by the transform 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 coding 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 respective header information and the like. Also, the quantization unit 1304 can change 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 a parameter indicating the weights of the respective frequency components to the header.

[0491] When the quantization unit 1304 does not have attribute information such as color information in the space, the process can be skipped. Also, the three-dimensional data encoding device 1300 can attach information (flag) indicating whether the process of the quantization unit 1304 is skipped to the bitstream.

[0492] 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 transform unit 1306.

[0493] The inverse transform unit 1306 applies an inverse transform to the inverse quantization coefficients generated by the inverse quantization unit 1305, thereby generating a prediction residual after the inverse transform is applied. Since this prediction residual after the inverse transform is applied is the prediction residual generated after quantization, it may not be exactly the same as the prediction residual output by the transform unit 1303.

[0494] The addition unit 1307 adds the prediction volume generated by the inverse transform unit 1306 after the inverse transform is applied and the prediction volume generated by intra-frame prediction or inter-frame prediction described later and used in the generation of the prediction residuals before quantization, to generate a reconstructed volume. This reconstructed volume is stored in the reference volume memory 1308 or the reference space memory 1310.

[0495] 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 predicted values of the color information or reflectance of the volume to be encoded.

[0496] 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 refers to, 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.

[0497] The intra prediction unit 1309 may also generate a predicted volume based on a plurality of 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 plurality of volume idxs of the plurality of volumes used in the generation of the predicted volume to the bitstream.

[0498] 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.

[0499] Furthermore, 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 time T_LX is, for example, a time T_L0 before a certain time T_Cur. At this time, the three-dimensional data encoding device 1300 may also attach RT information RT_L0 related to the rotation and translation processing of the space applicable to the time T_L0 to the bitstream.

[0500] Alternatively, the different time T_LX is, for example, a time T_L1 after the certain time T_Cur. At this time, the three-dimensional data encoding device 1300 may attach RT information RT_L1 related to the rotation and translation processing of the space applicable to the time T_L1 to the bitstream.

[0501] Alternatively, the inter-frame prediction unit 1311 performs encoding (bi-prediction) by referring to the spaces of both different times T_L0 and time T_L1. In this case, the three-dimensional data encoding device 1300 may attach both RT information RT_L0 and RT_L1 related to the rotation and translation applicable to the spaces respectively to the bitstream.

[0502] In addition, although T_L0 is set as a time before T_Cur and T_L1 is set as a time after T_Cur above, it is not limited thereto. For example, both T_L0 and T_L1 may be times before T_Cur. Or, both T_L0 and T_L1 may be times after T_Cur.

[0503] And it may also be that, when the three-dimensional data encoding device 1300 performs encoding by referring to the spaces of multiple different times, it attaches 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 to be referred to by 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.

[0504] Alternatively, 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 may 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 by 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.

[0505] Fig.46 An example of the syntax for attaching RT information and the 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 available values. When the number of allocated bits is fixed, the three-dimensional data encoding device 1300 may attach the fixed number of bits to other header information.

[0506] 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.

[0507] 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, etc. 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, etc.

[0508] MaxRefSpc_l1 shows the number of reference spaces included in the reference list L1. RT_flag_l1[i] is the RT application flag for the reference space i in the reference list L1. When RT_flag_l1[i] is 1, rotation and translation are applied to the reference space i. When RT_flag_l1[i] is 0, rotation and translation are not applied to the reference space i.

[0509] R_l1[i] and T_l1[i] are the RT information for the reference space i in the reference list L1. R_l1[i] is the rotation information for the reference space i in the 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 the reference space i in the reference list L1. The translation information shows the content of the applied translation process, such as a translation vector, etc.

[0510] 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 obtain 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 the 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 the reference space B. Here, the three-dimensional data encoding device 1300 attaches the RT information used to obtain the reference space B to the header information of the volume to be encoded space, etc.

[0511] In this way, the inter-frame prediction unit 1311 applies rotation and translation processing to the reference space, and after making the positional relationship between the volume to be encoded space and the entire reference space closer, generates a predicted volume by using the information of the reference space. In this way, the accuracy of the predicted volume can be improved. And since the prediction residual can be suppressed, the encoding amount can be reduced. In addition, although an example of using the volume to be encoded space and the reference space to perform 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 perform ICP by using 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, so as to obtain the RT information.

[0512] Furthermore, when the ICP error value obtained from the ICP result is smaller than a predetermined first threshold value, that is, when the positional relationship between the encoding object space and the reference space is close, the inter-frame prediction unit 1311 may determine that rotation and translation processing are not required, and does not perform rotation and translation. In this case, the three-dimensional data encoding device 1300 may not add RT information to the bitstream, thereby suppressing additional overhead.

[0513] Furthermore, when the ICP error value is greater than a predetermined second threshold, the inter-frame prediction unit 1311 determines that the shape change in space is large, and intra-frame prediction can be applied to all volumes of the encoding object space. Hereinafter, the space to which intra-frame prediction is applied is referred to as intra-frame space. Furthermore, the second threshold is a value greater than the above-mentioned first threshold. Furthermore, it is not limited to ICP, and any method can be applied as long as the method of obtaining RT information from two voxel sets or two point cloud sets.

[0514] Furthermore, when the three-dimensional data contains attribute information such as shape or color, the inter-frame prediction unit 1311 searches, for example, a volume in the reference space that is closest to the shape or color attribute information of the encoding target volume as a prediction volume of the encoding target volume in the encoding target space. Furthermore, the reference space is, for example, a reference space after the above-mentioned rotation and translation processing. The inter-frame prediction unit 1311 generates a prediction volume based on the volume (reference volume) obtained by the search. Fig.47 is a diagram for explaining the generation of the prediction volume. Fig.47 When the encoding target volume (volume idx=0) shown in the figure is encoded by using inter-frame prediction, the reference volumes in the reference space are scanned in sequence while searching for the volume with the smallest prediction residual, that is, the difference between the encoding target volume and the reference volume. The inter-frame prediction unit 1311 selects the volume with the smallest prediction residual as the prediction volume. The prediction residual between the encoding target volume and the prediction volume is encoded by the processing after the transformation 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 prediction volume. In addition, the three-dimensional data encoding device 1300 adds the volume idx of the reference volume in the reference space referred to as the prediction volume to the header of the bit stream.

[0515] exist Fig.47 In the example shown, the reference volume idx=4 of the reference space L0R0 is selected as the prediction 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 added to the bit stream.

[0516] In addition, although the prediction volume of the attribute information has been described as an example here, the same processing can be performed for the prediction volume of the position information.

[0517] 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 the prediction residuals in the case where the encoding target volume is predicted by intra prediction and in the case where it is predicted by inter prediction as evaluation values, and selects the prediction mode with the smaller evaluation value. Alternatively, the prediction control unit 1312 may apply orthogonal transformation, quantization, and entropy coding to the prediction residuals of intra prediction and inter prediction, respectively, to calculate the actual encoding amount, and use the calculated encoding amount as the evaluation value to select the prediction mode. Also, overhead information other than the prediction residual (such as the reference volume idx information) 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.

[0518] 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.

[0519] 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.

[0520] 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.

[0521] 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.

[0522] The inverse transform unit 1403 performs an inverse transform on the inverse quantized coefficients input from the inverse quantization unit 1402 to generate a prediction residual. For example, the inverse transform unit 1403 performs an inverse orthogonal transform on the inverse quantized coefficients according to the information attached to the bitstream to generate a prediction residual.

[0523] The addition unit 1404 adds the prediction residual generated by the inverse transform 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.

[0524] The intra prediction unit 1406 uses the reference volume in the reference volume memory 1405 and the information attached to the bitstream to generate a prediction volume by intra prediction. Specifically, the intra prediction unit 1406 obtains prediction mode information and adjacent volume information (such as volume idx) attached 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 intra prediction unit 1309 described above, except that the information attached to the bitstream is used.

[0525] The inter prediction unit 1408 uses the reference space in the reference space memory 1407 and the information attached to the bitstream to generate a prediction volume by inter prediction. Specifically, the inter prediction unit 1408 uses the RT information of each reference space attached 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 inter prediction unit 1311 described above, except that the information attached to the bitstream is used.

[0526] Regarding whether to decode the volume to be decoded by intra prediction or inter prediction, it 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 attached to the bitstream and indicating the prediction mode to be used. In addition, the prediction control unit 1409 may also usually select intra prediction when it is predetermined that the decoding object space is decoded by the intra space.

[0527] A modification example of this embodiment will be described below. In this embodiment, although rotation and translation are applied in terms of spatial units, 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 terms of sub-space units. 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. Further, the three-dimensional data encoding device 1300 can apply rotation and translation in terms of volume units as the encoding unit. In this case, the three-dimensional data encoding device 1300 generates RT information in terms of encoding volume units and attaches the generated RT information to the head of the bitstream. 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 terms of spatial units, and apply different rotations and translations to each of the multiple volumes included in the obtained space.

[0528] Further, 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 terms of spatial units, and translation can be applied in terms of volume units.

[0529] In addition, regarding these modification examples, the same can be applied to the three-dimensional data decoding device 1400.

[0530] 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.

[0531] First, the three-dimensional data encoding device 1300 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., the encoding object space) and the reference three-dimensional data (e.g., the reference space) at different times (S1301). Specifically, the three-dimensional data encoding device 1300 generates 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.

[0532] In addition, the three-dimensional data encoding device 1300 performs rotation and translation processing in a first unit (e.g., a space), and generates predicted position information in 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 a volume in the reference space after rotation and translation processing, among a plurality of volumes included in the reference space, such that the difference between the encoded object volume and the position information included in the encoded object space 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.

[0533] Alternatively, 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 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 a second unit (e.g., a volume) that is finer than the first unit, thereby generating predicted position information.

[0534] Here, the position information of the three-dimensional points and the predicted position information are Fig.41 represented in an octree structure. 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.

[0535] In addition, 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.

[0536] Furthermore, 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.

[0537] 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).

[0538] Moreover, 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).

[0539] 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.

[0540] 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 also perform only 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.

[0541] 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.

[0542] 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.

[0543] Further, in the present embodiment, the three-dimensional data encoding device 1300 generates predicted attribute information by using the attribute information of the three-dimensional points included in the reference three-dimensional data, and encodes 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. Accordingly, since the data amount of the encoded signal can be reduced, the encoding efficiency can be improved.

[0544] 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 processing.

[0545] Fig.48 It is a flowchart of the inter-frame prediction process performed by the three-dimensional data decoding device 1400.

[0546] First, the three-dimensional data decoding device 1400 decodes (for example, entropy decodes) the differential position information and the differential attribute information according to the encoded signal (encoded bitstream) (S1401).

[0547] 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 according to the encoded signal. Also, the three-dimensional data decoding device 1400 decodes the RT information indicating the content of the rotation and translation processing. In addition, 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.

[0548] Next, the three-dimensional data decoding device 1400 performs inverse quantization and inverse transformation on the decoded differential attribute information (S1402).

[0549] Next, the three-dimensional data decoding device 1400 generates predicted position information (for example, a predicted volume) by using the position information of the three-dimensional points included in the target three-dimensional data (for example, the decoding target space) and the reference three-dimensional data (for example, the reference space) at different times (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.

[0550] 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.

[0551] In addition, the three-dimensional data decoding device 1400 can perform rotation and translation processing in a first unit (e.g., a space), and can generate predicted position information in a second unit (e.g., a 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.

[0552] Specifically, the three-dimensional data decoding device 1400 can apply 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., a space), and can apply 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., a volume) that is finer than the first unit, thereby generating predicted position information.

[0553] Here, the position information of the three-dimensional points and the predicted position information are, for example Fig.41 As shown, they are represented in an octree structure. For example, the position information of the three-dimensional points and the predicted position information are represented in a width-first scan order 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 depth-first scan order among the depth and width in the octree structure.

[0554] 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).

[0555] 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).

[0556] 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).

[0557] Alternatively, when the three-dimensional data does not include attribute information, the three-dimensional data decoding device 1400 may also not execute steps S1402, S1404, and S1406. Moreover, the three-dimensional data decoding device 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.

[0558] 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.

[0559] (Embodiment 8)

[0560] A control method for references during encoding of occupancy encoding in the present embodiment will be described. In addition, hereinafter, the operations of the three-dimensional data encoding device will be mainly described, but the same processing can also be performed in the three-dimensional data decoding device.

[0561] Fig.51 And Fig.52 is a diagram showing the reference relationship according to the present embodiment, Fig.51 is a diagram showing the reference relationship on an octree structure, Fig.52 is a diagram showing the reference relationship in a spatial region.

[0562] In the present embodiment, when the three-dimensional data encoding device encodes the encoding information of a node to be encoded (hereinafter referred to as an object node), it refers to the encoding information of each node in the parent node to which the object node belongs. However, it does not refer to the encoding information of each node in other nodes (hereinafter referred to as parent adjacent nodes) at the same level as the parent node. That is, the three-dimensional data encoding device sets the parent adjacent nodes as non-referable or prohibits reference.

[0563] In addition, the three-dimensional data encoding device may also permit reference to the encoding information in the parent node to which the parent node belongs (hereinafter referred to as the grandparent node). That is, the three-dimensional data encoding device may also refer to the encoding information of the parent node and the grandparent node to which the object node belongs to encode the encoding information of the object node.

[0564] Here, the encoded information is, for example, occupancy encoding. When encoding the occupancy encoding of an object node, the three-dimensional data encoding device refers to information indicating whether each node in the parent node to which the object node belongs contains a point cloud (hereinafter referred to as occupancy information). In other words, when encoding the occupancy encoding of an object node, the three-dimensional data encoding device refers to the occupancy encoding of the parent node. On the other hand, the three-dimensional data encoding device does not refer to the occupancy information of each node in the parent adjacent node. That is, the three-dimensional data encoding device does not refer to the occupancy encoding of the parent adjacent node. In addition, the three-dimensional data encoding device may also refer to the occupancy information of each node in the grandparent node. That is, the three-dimensional data encoding device may also refer to the occupancy information of the parent node and the parent adjacent node.

[0565] For example, when encoding the occupancy encoding of an object node, the three-dimensional data encoding device uses the occupancy encoding of the parent node or the grandparent node to which the object node belongs, and switches the encoding table used when performing entropy encoding on the occupancy encoding of the object node. The details will be described later. At this time, the three-dimensional data encoding device may also not refer to the occupancy encoding of the parent adjacent node. Thus, the three-dimensional data encoding device can appropriately switch the encoding table according to the information of the occupancy encoding of the parent node or the grandparent node when encoding the occupancy encoding of the object node, and therefore can improve the encoding efficiency. In addition, since the three-dimensional data encoding device does not refer to the parent adjacent node, it is possible to suppress the confirmation process of the information of the parent adjacent node and the memory capacity for storing the process. In addition, it becomes easy to scan and encode the occupancy encoding of each node of the octree in depth-first order.

[0566] Hereinafter, an example of encoding table switching using the occupancy encoding of the parent node will be described. Fig.53 It is a diagram showing an example of an object node and an adjacent reference node. Fig.54 It is a diagram showing the relationship between the parent node and the node. Fig.55 It is a diagram showing an example of the occupancy encoding of the parent node. Here, the adjacent reference node refers to a node that is spatially adjacent to the object node and is referred to during the encoding of the object node. In Fig.53 In the example shown, the adjacent nodes are nodes belonging to the same layer as the object node. In addition, as the reference adjacent nodes, nodes X adjacent in the x direction of the object block, nodes Y adjacent in the y direction, and nodes Z adjacent in the z direction are used. That is, one adjacent block is set as the reference adjacent block in each of the x, y, and z directions.

[0567] In addition, Fig.54 The node numbers shown are an example, and the relationship between the node numbers and the positions of the nodes is not limited to this. In addition, in Fig.55Among them, node 0 is assigned to the lower-order bit, and node 7 is assigned to the higher-order bit. However, the assignment can also be made in the reverse order. In addition, each node can be assigned to any bit.

[0568] The three-dimensional data encoding device determines the encoding table for entropy encoding the occupancy rate encoding of the target node by, for example, the following formula.

[0569] CodingTable = (FlagX << 2) + (FlagY << 1) + (FlagZ)

[0570] Here, CodingTable represents the encoding table for the occupancy rate encoding of the target node, and represents any one of the values 0 to 7. FlagX is the occupancy information of the adjacent node X, and represents 1 if the adjacent node X contains (occupies) a point group, and represents 0 if not. FlagY is the occupancy information of the adjacent node Y, and represents 1 if the adjacent node Y contains (occupies) a point group, and represents 0 if not. FlagZ is the occupancy information of the adjacent node Z, and represents 1 if the adjacent node Z contains (occupies) a point group, and represents 0 if not.

[0571] In addition, the information indicating whether the adjacent node is occupied is included in the occupancy rate encoding of the parent node. Therefore, the three-dimensional data encoding device can also use the value shown in the occupancy rate encoding of the parent node to select the encoding table.

[0572] From the above, it can be seen that the three-dimensional data encoding device uses the information indicating whether the adjacent nodes of the target node contain a point group to switch the encoding table, thereby improving the encoding efficiency.

[0573] In addition, as Fig.53 shown, the three-dimensional data encoding device can also switch the adjacent reference nodes according to the spatial position of the target node in the parent node. That is, the three-dimensional data encoding device can also switch the adjacent node to be referenced among the multiple adjacent nodes according to the spatial position in the parent node of the target node.

[0574] Next, a structural example of the three-dimensional data encoding device and the three-dimensional data decoding device will be described. Fig.56 is a block diagram of the three-dimensional data encoding device 2100 according to the present embodiment. Fig.56 The three-dimensional data encoding device 2100 shown includes an octree generation unit 2101, a geometric information calculation unit 2102, an encoding table selection unit 2103, and an entropy encoding unit 2104.

[0575] The octree generation unit 2101 generates, for example, an octree from the input three-dimensional points (point cloud), and generates occupancy encoding for each node included in the octree. The geometric information calculation unit 2102 obtains occupancy information indicating whether an adjacent reference node of the target node is occupied. For example, the geometric information calculation unit 2102 obtains the occupancy information of the adjacent reference node from the occupancy encoding of the parent node to which the target node belongs. In addition, as Fig.53 shown, the geometric information calculation unit 2102 can also switch the adjacent reference node according to the position within the parent node of the target node. Additionally, the geometric information calculation unit 2102 does not refer to the occupancy information of each node within the parent adjacent node.

[0576] The encoding table selection unit 2103 uses the occupancy information of the adjacent reference node calculated by the geometric information calculation unit 2102 to select an encoding table used in the entropy encoding of the occupancy encoding of the target node. The entropy encoding unit 2104 generates a bitstream by performing entropy encoding on the occupancy encoding using the selected encoding table. Additionally, the entropy encoding unit 2104 can also attach information indicating the selected encoding table to the bitstream.

[0577] Fig.57 is a block diagram of the three-dimensional data decoding device 2110 according to the present embodiment. Fig.57 The three-dimensional data decoding device 2110 shown in

[0578] includes an octree generation unit 2111, a geometric information calculation unit 2112, an encoding table selection unit 2113, and an entropy decoding unit 2114.

[0579] The octree generation unit 2111 generates an octree of a certain space (node) using the header information of the bitstream and the like. The octree generation unit 2111 generates a large space (root node) using, for example, the sizes in the x-axis, y-axis, and z-axis directions of a certain space attached to the header information, and generates an octree by dividing this space into 8 small spaces A (nodes A0 to A7) by dividing it in the x-axis, y-axis, and z-axis directions respectively. Additionally, nodes A0 to A7 are sequentially set as the target nodes.

[0579] The geometric information calculation unit 2112 obtains occupancy information indicating whether an adjacent reference node of the target node is occupied. For example, the geometric information calculation unit 2112 obtains the occupancy information of the adjacent reference node from the occupancy encoding of the parent node to which the target node belongs. In addition, as Fig.53 shown, the geometric information calculation unit 2112 can also switch the adjacent reference node according to the position within the parent node of the target node. Additionally, the geometric information calculation unit 2112 does not refer to the occupancy information of each node within the parent adjacent node.

[0580] The encoding table selection unit 2113 selects an encoding table (decoding table) used in the entropy decoding of the occupancy rate encoding of the object node, using the occupancy information of adjacent reference nodes calculated by the geometric information calculation unit 2112. The entropy decoding unit 2114 generates three-dimensional points by performing entropy decoding on the occupancy rate encoding using the selected encoding table. In addition, the encoding table selection unit 2113 decodes and obtains the information of the selected encoding table attached to the bitstream, and the entropy decoding unit 2114 may also use the encoding table indicated by the obtained information.

[0581] Each bit of the occupancy rate encoding (8 bits) included in the bitstream indicates whether or not a point group is included in each of the eight small spaces A (nodes A0 to A7). Furthermore, the three-dimensional data decoding device divides the small space node A0 into eight small spaces B (nodes B0 to B7) to generate an octree, decodes the occupancy rate encoding, and obtains information indicating whether or not a point group is included in each node of the small space B. In this way, the three-dimensional data decoding device decodes the occupancy rate encoding of each node while generating an octree from a large space to a small space.

[0582] Hereinafter, the processing flow of the three-dimensional data encoding device and the three-dimensional data decoding device will be described. Fig.58 This is a flowchart of the three-dimensional data encoding process in the three-dimensional data encoding device. First, the three-dimensional data encoding device determines (defines) a space (object node) that includes a part or all of the input three-dimensional point group (S2101). Next, the three-dimensional data encoding device divides the object node 8 to generate eight small spaces (nodes) (S2102). Next, the three-dimensional data encoding device generates an occupancy rate encoding of the object node according to whether or not a point group is included in each node (S2103).

[0583] Next, the three-dimensional data encoding device calculates (obtains) the occupancy information of the adjacent reference nodes of the object node from the occupancy rate encoding of the parent node of the object node (S2104). Next, the three-dimensional data encoding device selects an encoding table used in the entropy encoding based on the determined occupancy information of the adjacent reference nodes of the object node (S2105). Next, the three-dimensional data encoding device performs entropy encoding on the occupancy rate encoding of the object node using the selected encoding table (S2106).

[0584] In addition, the three-dimensional data encoding device repeatedly performs the process of dividing each node 8 and encoding the occupancy rate encoding of each node until the node cannot be divided (S2107). That is, the processes of steps S2102 to S2106 are recursively repeated.

[0585] Fig.59It is a flowchart of a 3D data decoding method in a 3D data decoding device. First, the 3D data decoding device determines (defines) the space (object node) to be decoded using the header information of the bitstream (S2111). Next, the 3D data decoding device divides the object node 8 to generate 8 small spaces (nodes) (S2112). Next, the 3D data decoding device calculates (obtains) the occupancy information of the adjacent reference nodes of the object node from the occupancy rate coding of the parent node of the object node (S2113).

[0586] Next, the 3D data decoding device selects the coding table used in entropy decoding based on the occupancy information of the adjacent reference nodes (S2114). Next, the 3D data decoding device performs entropy decoding on the occupancy rate coding of the object node using the selected coding table (S2115).

[0587] In addition, the 3D data decoding device repeatedly performs the process of dividing each node 8 and decoding the occupancy rate coding of each node until the node cannot be divided (S2116). That is, the processes of steps S2112 to S2115 are recursively repeated.

[0588] Next, an example of the switching of the coding table will be described. Fig.60 It is a diagram showing an example of the switching of the coding table. For example, as in the coding table 0 shown in Fig.60 , the same context model can also be applied to multiple occupancy rate codings. In addition, different context models can also be assigned to each occupancy rate coding. Thus, the context model can be assigned according to the occurrence probability of the occupancy rate coding, so the coding efficiency can be improved. In addition, a context model that updates the probability table according to the occurrence frequency of the occupancy rate coding can also be used. In addition, a context model that fixes the probability table can also be used.

[0589] Hereinafter, Modification Example 1 of the present embodiment will be described. Fig.61 It is a diagram showing the reference relationship in this modification example. In the above embodiment, the 3D data encoding device does not refer to the occupancy rate coding of the parent adjacent node, but it is also possible to switch whether to refer to the occupancy rate coding of the parent adjacent node according to specific conditions.

[0590] For example, when the 3D data encoding device encodes while performing a breadth-first scan of the octree, it refers to the occupancy information of the nodes in the parent adjacent node and encodes the occupancy rate coding of the object node. On the other hand, when the 3D data encoding device encodes while performing a depth-first scan of the octree, it prohibits referring to the occupancy information of the nodes in the parent adjacent node. In this way, according to the scan order (encoding order) of the nodes of the octree, the nodes that can be referred to are appropriately switched, so that the improvement of the coding efficiency and the suppression of the processing load can be achieved.

[0591] In addition, the three-dimensional data encoding device may attach information such as whether to encode the octree in a breadth-first manner or a depth-first manner to the head of the bitstream. Fig.62 It is a diagram of a syntactic example of the header information indicating this situation. Fig.62 The octree_scan_order shown represents encoding order information (encoding order flag) indicating the encoding order of the octree. For example, when octree_scan_order is 0, it represents breadth-first, and when it is 1, it represents depth-first. Thus, the three-dimensional data decoding device can know whether the bitstream is encoded in a breadth-first or depth-first manner by referring to octree_scan_order and can appropriately decode the bitstream.

[0592] In addition, the three-dimensional data encoding device may also attach information indicating whether to prohibit referring to a parent adjacent node to the header information of the bitstream. Fig.63 It is a diagram of a syntactic example of the header information indicating this situation. limit_refer_flag is prohibition switching information (prohibition switching flag) indicating whether to prohibit referring to a parent adjacent node. For example, when limit_refer_flag is 1, it represents prohibiting referring to a parent adjacent node, and when it is 0, it represents no reference restriction (permission to refer to a parent adjacent node).

[0593] That is, the three-dimensional data encoding device determines whether to prohibit referring to a parent adjacent node, and based on the result of the above determination, switches whether to prohibit or permit referring to a parent adjacent node. In addition, the three-dimensional data encoding device generates a bitstream including the prohibition switching information, which is the result of the above determination and indicates whether to prohibit referring to a parent adjacent node.

[0594] In addition, the three-dimensional data decoding device obtains prohibition switching information indicating whether to prohibit referring to a parent adjacent node from the bitstream, and based on the prohibition switching information, switches whether to prohibit or permit referring to a parent adjacent node.

[0595] Thus, the three-dimensional data encoding device can control the reference to the parent adjacent node and generate a bitstream. In addition, the three-dimensional data decoding device can obtain information indicating whether to prohibit referring to a parent adjacent node from the head of the bitstream.

[0596] In addition, in the present embodiment, as an example of the encoding process of prohibiting referring to a parent adjacent node, the encoding process of occupancy encoding is described as an example, but it is not necessarily limited to this. For example, the same method can also be applied when encoding other information of the nodes of the octree. For example, when encoding other attribute information such as color, normal vector, or reflectance attached to the node, the method of the present embodiment can also be applied. In addition, the same method can also be applied when encoding an encoding table or a predicted value.

[0597] Next, a second modification example of this embodiment will be described. In the above description, as Fig.53 shown, an example of using three reference adjacent nodes is shown, but four or more reference adjacent nodes may also be used. Fig.64 FIG. is a diagram showing an example of an object node and reference adjacent nodes.

[0598] For example, the three-dimensional data encoding device calculates, by the following formula, a coding table for entropy encoding the occupancy rate encoding of the object node shown in Fig.64 .

[0599] CodingTable = (FlagX0 << 3) + (FlagX1 << 2) + (FlagY << 1) + (FlagZ)

[0600] Here, CodingTable represents the coding table for the occupancy rate encoding of the object node, and represents any one of values 0 to 15. FlagXN is the occupancy information of the adjacent node XN (N = 0...1), and represents 1 if the adjacent node XN contains (occupies) a point group, and represents 0 if not. FlagY is the occupancy information of the adjacent node Y, and represents 1 if the adjacent node Y contains (occupies) a point group, and represents 0 if not. FlagZ is the occupancy information of the adjacent node Z, and represents 1 if the adjacent node Z contains (occupies) a point group, and represents 0 if not.

[0601] At this time, if the adjacent node is, for example, Fig.64 the adjacent node X0 that cannot be referred to (reference prohibited), the three-dimensional data encoding device may also use a fixed value such as 1 (occupied) or 0 (not occupied) as a substitute value.

[0602] Fig.65 FIG. is a diagram showing an example of an object node and adjacent nodes. As Fig.65 shown, in the case where the adjacent node cannot be referred to (reference prohibited), the occupancy rate encoding of the grandparent node of the object node may also be referred to to calculate the occupancy information of the adjacent node. For example, instead of Fig.65 the adjacent node X0 shown, the three-dimensional data encoding device may also use the occupancy information of the adjacent node G0 to calculate FlagX0 in the above formula, and determine the value of the coding table using the calculated FlagX0. In addition, Fig.65 the adjacent node G0 shown is an adjacent node that can determine whether it is occupied by the occupancy rate encoding of the grandparent node. The adjacent node X1 is an adjacent node that can determine whether it is occupied by the occupancy rate encoding of the parent node.

[0603] Hereinafter, a third modification example of this embodiment will be described. Fig.66 and Fig.67 FIG. is a diagram showing the reference relationship related to this modification example, Fig.66 is a diagram showing a reference relationship represented on an octree structure, Fig.67 is a diagram showing a reference relationship represented on a spatial region.

[0604] In this modified example, when the three-dimensional data encoding device encodes the encoding information of a node to be encoded (hereinafter referred to as object node 2), it refers to the encoding information of each node in the parent node to which object node 2 belongs. That is, the three-dimensional data encoding device permits referring to the information (such as occupancy information) of the child nodes of the first node whose parent node is the same as the parent node of the object node among multiple adjacent nodes. For example, when the three-dimensional data encoding device encodes the occupancy rate encoding of Fig.66 the object node 2 shown, it refers to the nodes existing in the parent node to which object node 2 belongs. For example, Fig.66 the occupancy rate encoding of the object node shown. As Fig.67 shown, Fig.66 the occupancy rate encoding of the object node shown represents whether each node in the object node adjacent to object node 2, for example, is occupied. Therefore, the three-dimensional data encoding device can switch the encoding table for the occupancy rate encoding of object node 2 according to the finer shape of the object node, and thus can improve the encoding efficiency.

[0605] The three-dimensional data encoding device can also calculate the encoding table for entropy encoding the occupancy rate encoding of object node 2 by, for example, the following formula.

[0606] CodingTable = (FlagX1 << 5) + (FlagX2 << 4) + (FlagX3 << 3) + (FlagX4 << 2) + (FlagY << 1) + (FlagZ)

[0607] Here, CodingTable represents the encoding table for the occupancy rate encoding of object node 2, and represents any value from 0 to 63. FlagXN is the occupancy information of the adjacent node XN (N = 1...4), and represents 1 if the adjacent node XN contains (occupies) a point group, and represents 0 if not. FlagY is the occupancy information of the adjacent node Y, and represents 1 if the adjacent node Y contains (occupies) a point group, and represents 0 if not. FlagZ is the occupancy information of the adjacent node Y, and represents 1 if the adjacent node Z contains (occupies) a point group, and represents 0 if not.

[0608] In addition, the three-dimensional data encoding device can also change the calculation method of the encoding table according to the node position of object node 2 in the parent node.

[0609] In addition, in a case where referring to a parent adjacent node is not prohibited, the three-dimensional data encoding device may refer to the encoding information of each node in the parent adjacent node. For example, in a case where referring to a parent adjacent node is not prohibited, information (such as occupancy information) of a child node of a third node whose parent node is different from the parent node of the object node is permitted to be referred to. For example, in Fig.65 In the example shown, the three-dimensional data encoding device refers to the occupancy rate encoding of an adjacent node X0 whose parent node is different from the parent node of the object node, and obtains the occupancy information of the child node of the adjacent node X0. The three-dimensional data encoding device switches the encoding table used in the entropy encoding of the occupancy rate encoding of the object node based on the obtained occupancy information of the child node of the adjacent node X0.

[0610] As described above, the three-dimensional data encoding device according to the present embodiment encodes information (such as occupancy rate encoding) of an object node included in an N-ary tree structure (where N is an integer of 2 or more) of a plurality of three-dimensional points included in the three-dimensional data. As Fig.51 and Fig.52 shown, in the above encoding, the three-dimensional data encoding device permits referring to information (such as occupancy information) of a first node whose parent node is the same as the parent node of the object node among a plurality of adjacent nodes that are spatially adjacent to the object node, and prohibits referring to information (such as occupancy information) of a second node whose parent node is different from the parent node of the object node. In other words, in the above encoding, the three-dimensional data encoding device permits referring to information of the parent node (such as occupancy rate encoding), and prohibits referring to information (such as occupancy rate encoding) of other nodes (parent adjacent nodes) at the same layer as the parent node.

[0611] Accordingly, the three-dimensional data encoding device can improve the encoding efficiency by referring to information of a first node whose parent node is the same as the parent node of the object node among a plurality of adjacent nodes that are spatially adjacent to the object node. In addition, since the three-dimensional data encoding device does not refer to information of a second node whose parent node is different from the parent node of the object node among the plurality of adjacent nodes, the processing amount can be reduced. In this way, the three-dimensional data encoding device can improve the encoding efficiency and can reduce the processing amount.

[0612] For example, the three-dimensional data encoding device further determines whether to prohibit referring to information of the second node, and in the above encoding, based on the result of the above determination, switches whether to prohibit or permit referring to information of the second node. The three-dimensional data encoding device further generates a bitstream including a prohibition switching information (for example, Fig.63 the limit_refer_flag shown), where the prohibition switching information is the result of the above determination and indicates whether to prohibit referring to information of the second node.

[0613] Thus, the three-dimensional data encoding device can switch whether to prohibit referring to the information of the second node. In addition, the three-dimensional data decoding device can appropriately perform decoding processing using the prohibition switching information.

[0614] For example, the information of the object node is information indicating whether there are three-dimensional points for each of the child nodes belonging to the object node (e.g., occupancy encoding), the information of the first node is information indicating whether there are three-dimensional points in the first node (the occupancy information of the first node), and the information of the second node is information indicating whether there are three-dimensional points in the second node (the occupancy information of the second node).

[0615] For example, in the above encoding, the three-dimensional data encoding device selects a coding table based on whether there are three-dimensional points in the first node, and uses the selected coding table to perform entropy encoding on the information of the object node (e.g., occupancy encoding).

[0616] For example, as Fig.66 and Fig.67 shown, in the above encoding, the three-dimensional data encoding device permits referring to the information (e.g., occupancy information) of the child nodes of the first node among multiple adjacent nodes.

[0617] Thus, since the three-dimensional data encoding device can refer to more detailed information of adjacent nodes, the encoding efficiency can be improved.

[0618] For example, as Fig.53 shown, in the above encoding, the three-dimensional data encoding device switches the adjacent node to be referred to among multiple adjacent nodes according to the spatial position within the parent node of the object node.

[0619] Thus, the three-dimensional data encoding device can refer to appropriate adjacent nodes according to the spatial position within the parent node of the object node.

[0620] 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.

[0621] In addition, the three-dimensional data decoding device according to the present embodiment decodes the information (e.g., occupancy encoding) of the object node included in the N (N is an integer of 2 or more) -ary tree structure of multiple three-dimensional points included in the three-dimensional data. As Fig.51 and Fig.52As shown, in the above decoding, the three-dimensional data decoding device permits referring to the information (such as occupancy information) of the first node among multiple adjacent nodes that are spatially adjacent to the object node and whose parent node is the same as the parent node of the object node, and prohibits referring to the information (such as occupancy information) of the second node whose parent node is different from the parent node of the object node. In other words, in the above decoding, the three-dimensional data decoding device permits referring to the information of the parent node (such as occupancy rate coding) and prohibits referring to the information (such as occupancy rate coding) of other nodes (parent adjacent nodes) at the same layer as the parent node.

[0622] Thereby, by referring to the information of the first node among multiple adjacent nodes that are spatially adjacent to the object node and whose parent node is the same as the parent node of the object node, the three-dimensional data decoding device can improve the coding efficiency. In addition, since the three-dimensional data decoding device does not refer to the information of the second node among multiple adjacent nodes whose parent node is different from the parent node of the object node, the processing amount can be reduced. In this way, the three-dimensional data decoding device can improve the coding efficiency and reduce the processing amount.

[0623] For example, the three-dimensional data decoding device further obtains from the bitstream a prohibition switching information (such as, Fig.63 the shown limit_refer_flag) indicating whether to prohibit referring to the information of the second node, and in the above decoding, based on the prohibition switching information, switches whether to prohibit or permit referring to the information of the second node.

[0624] Thereby, the three-dimensional data decoding device can appropriately perform decoding processing using the prohibition switching information.

[0625] For example, the information of the object node is information (such as occupancy rate coding) indicating whether there are three-dimensional points for each of the child nodes belonging to the object node, the information of the first node is information (the occupancy information of the first node) indicating whether there are three-dimensional points in the first node, and the information of the second node is information (the occupancy information of the second node) indicating whether there are three-dimensional points in the second node.

[0626] For example, in the above decoding, the three-dimensional data decoding device selects a coding table based on whether there are three-dimensional points in the first node, and uses the selected coding table to perform entropy decoding on the information (such as occupancy rate coding) of the object node.

[0627] For example, as Fig.66 and Fig.67 shown, in the above decoding, the three-dimensional data decoding device permits referring to the information (such as occupancy information) of the child nodes of the first node among multiple adjacent nodes.

[0628] Thereby, the three-dimensional data decoding device can refer to more detailed information of adjacent nodes, and thus can improve the coding efficiency.

[0629] For example, as Fig.53 shown, in the above decoding, the three-dimensional data decoding device switches the adjacent node to be referred to among the plurality of adjacent nodes according to the spatial position in the parent node of the object node.

[0630] Thus, the three-dimensional data decoding device can refer to an appropriate adjacent node according to the spatial position in the parent node of the object node.

[0631] 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.

[0632] (Embodiment 9)

[0633] The information of the three-dimensional point cloud includes position information (geometry) and attribute information (attribute). The position information includes coordinates (x coordinate, y coordinate, z coordinate) based on a certain point. When encoding the position information, instead of directly encoding the coordinates of each three-dimensional point, a method is used in which the position of each three-dimensional point is represented by an octree representation and the information of the octree is encoded to reduce the amount of encoding.

[0634] On the other hand, the attribute information includes information such as color information (RGB, YUV, etc.), reflectance, and normal vector representing each three-dimensional point. For example, the three-dimensional data encoding device can encode the attribute information using a coding method different from the position information.

[0635] In the present embodiment, the encoding method of the attribute information will be described. In addition, in the present embodiment, integer values are used as the values of the attribute information for description. For example, when each color component of the color information RGB or YUV has an 8-bit precision, each color component takes an integer value from 0 to 255. When the value of the reflectance has a 10-bit precision, the value of the reflectance takes an integer value from 0 to 1023. In addition, when the bit precision of the attribute information is a fractional precision, the three-dimensional data encoding device can also multiply the value by a scaling value and then round it to an integer value so that the value of the attribute information becomes an integer value. In addition, the three-dimensional data encoding device can also attach the scaling value to the head of the bit stream or the like.

[0636] As a method for encoding the attribute information of three-dimensional points, the predicted value of the attribute information of the three-dimensional points is considered, and the difference (prediction residual) between the value of the original attribute information and the predicted value is encoded. For example, when the value of the attribute information of the three-dimensional point p is Ap and the predicted value is Pp, the three-dimensional data encoding device encodes the absolute value of the difference Diffp = |Ap - Pp|. In this case, if the predicted value Pp can be generated with high accuracy, the value of the absolute difference Diffp becomes smaller. Therefore, for example, by using an encoding table in which the smaller the value, the smaller the number of bits generated, to perform entropy encoding on the absolute difference Diffp, the encoding amount can be reduced.

[0637] As a method for generating the predicted value of the attribute information, the attribute information of other three-dimensional points, that is, reference three-dimensional points, located around the object three-dimensional point to be encoded is considered. Here, the reference three-dimensional points refer to three-dimensional points within a predetermined distance range from the object three-dimensional point. For example, in the case where there are an object three-dimensional point p = (x1, y1, z1) and a three-dimensional point q = (x2, y2, z2), the three-dimensional data encoding device calculates the Euclidean distance d(p, q) between the three-dimensional point p and the three-dimensional point q shown in (Equation A1).

[0638]

Equation 1

[0639]

[0640] When the Euclidean distance d(p, q) is less than a predetermined threshold THd, the three-dimensional data encoding device determines that the position of the three-dimensional point q is close to the position of the object three-dimensional point p, and determines to use the value of the attribute information of the three-dimensional point q in the generation of the predicted value of the attribute information of the object three-dimensional point p. In addition, the distance calculation method can also be other methods, for example, the Mahalanobis distance can also be used. In addition, the three-dimensional data encoding device can also determine not to use three-dimensional points outside the predetermined distance range from the object three-dimensional point for prediction processing. For example, in the case where there is a three-dimensional point r and the distance d(p, r) between the object three-dimensional point p and the three-dimensional point r is greater than or equal to the threshold THd, the three-dimensional data encoding device can also determine not to use the three-dimensional point r for prediction. In addition, the three-dimensional data encoding device can also attach information indicating the threshold THd to the head of the bitstream, etc.

[0641] Fig.68 It is a diagram showing an example of three-dimensional points. In this example, the distance d(p, q) between the object three-dimensional point p and the three-dimensional point q is less than the threshold THd. Therefore, the three-dimensional data encoding device determines the three-dimensional point q as the reference three-dimensional point of the object three-dimensional point p, and determines to use the value of the attribute information Aq of the three-dimensional point q in the generation of the predicted value Pp of the attribute information Ap of the object three-dimensional point p.

[0642] On the other hand, the distance d(p, r) between the object three-dimensional point p and the three-dimensional point r is equal to or greater than the threshold value THd. Therefore, the three-dimensional data encoding device determines that the three-dimensional point r is not the reference three-dimensional point of the object three-dimensional point p, and determines that the value of the attribute information Ar of the three-dimensional point r is not used in generating the predicted value Pp of the attribute information Ap of the object three-dimensional point p.

[0643] In addition, when encoding the attribute information of an object three-dimensional point using a predicted value, the three-dimensional data encoding device uses a three-dimensional point for which the attribute information has been encoded and decoded as a reference three-dimensional point. Similarly, when decoding the attribute information of an object three-dimensional point of a decoding target using a predicted value, the three-dimensional data decoding device uses a three-dimensional point for which the attribute information has been decoded as a reference three-dimensional point. As a result, the same predicted value can be generated during encoding and decoding, and thus the bitstream of the three-dimensional points generated by encoding can be correctly decoded on the decoding side.

[0644] In addition, when encoding the attribute information of three-dimensional points, consider classifying each three-dimensional point into multiple levels using the position information of the three-dimensional points and then performing encoding. Here, each classified level is called LoD (Level of Detail). Fig.69 A method for generating LoD will be described.

[0645] First, the three-dimensional data encoding device selects an initial point a0 and assigns it to LoD0. Next, the three-dimensional data encoding device extracts a point a1 whose distance from the point a0 is greater than the threshold Thres_LoD[0] of LoD0 and assigns it to LoD0. Next, the three-dimensional data encoding device extracts a point a2 whose distance from the point a1 is greater than the threshold Thres_LoD[0] of LoD0 and assigns it to LoD0. In this way, the three-dimensional data encoding device constructs LoD0 such that the distance between each point within LoD0 is greater than the threshold Thres_LoD[0].

[0646] Next, the three-dimensional data encoding device selects a point b0 that has not been assigned an LoD and assigns it to LoD1. Next, the three-dimensional data encoding device extracts a point b1 whose distance from the point b0 is greater than the threshold Thres_LoD[1] of LoD1 and that has not been assigned an LoD and assigns it to LoD1. Next, the three-dimensional data encoding device extracts a point b2 whose distance from the point b1 is greater than the threshold Thres_LoD[1] of LoD1 and that has not been assigned an LoD and assigns it to LoD1. In this way, the three-dimensional data encoding device constructs LoD1 such that the distance between each point within LoD1 is greater than the threshold Thres_LoD[1].

[0647] Next, the three-dimensional data encoding device selects a point c0 that has not been assigned an LoD and assigns it to LoD2. Next, the three-dimensional data encoding device extracts a point c1 that is at a distance greater than the threshold Thres_LoD[2] of LoD2 from the point c0 and that has not been assigned an LoD, and assigns it to LoD2. Next, the three-dimensional data encoding device extracts a point c2 that is at a distance greater than the threshold Thres_LoD[2] of LoD2 from the point c1 and that has not been assigned an LoD, and assigns it to LoD2. In this way, the three-dimensional data encoding device constructs LoD2 such that the distance between each point within LoD2 is greater than the threshold Thres_LoD[2]. For example, as Fig.70 shown, the thresholds Thres_LoD[0], Thres_LoD[1], and Thres_LoD[2] for each LoD are set.

[0648] In addition, the three-dimensional data encoding device may also attach information indicating the thresholds for each LoD to the header of the bitstream or the like. For example, in the case of the example Fig.70 shown, the three-dimensional data encoding device may also attach the thresholds Thres_LoD[0], Thres_LoD[1], and Thres_LoD[2] to the header.

[0649] In addition, the three-dimensional data encoding device may also assign all three-dimensional points that have not been assigned an LoD to the bottom layer of the LoD. In this case, the three-dimensional data encoding device can reduce the encoding amount of the header by not attaching the threshold of the bottom layer of the LoD to the header. For example, in the case of the example Fig.70 shown, the three-dimensional data encoding device attaches the thresholds Thres_LoD[0] and Thres_LoD[1] to the header and does not attach Thres_LoD[2] to the header. In this case, the three-dimensional data decoding device may also estimate the value of Thres_LoD[2] to be 0. In addition, the three-dimensional data encoding device may also attach the number of levels of the LoD to the header. Thereby, the three-dimensional data decoding device can use the number of levels of the LoD to determine the bottom layer of the LoD.

[0650] In addition, as Fig.70 shown, the values of the thresholds for each layer of the LoD are set to be larger for the upper layers, so that the higher the layer (the layer closer to LoD0), the sparser the group of points with a greater distance between the three-dimensional points becomes, and the lower the layer, the denser the group of points with a closer distance between the three-dimensional points becomes. In addition, in the example Fig.70 shown, LoD0 is the topmost layer.

[0651] In addition, the method for selecting the initial three-dimensional points when setting each LoD can also depend on the encoding order during position information encoding. For example, the three-dimensional data encoding device selects the three-dimensional point that was first encoded during position information encoding as the initial point a0 of LoD0, and selects points a1 and a2 with the initial point a0 as the base point to form LoD0. Moreover, the three-dimensional data encoding device can also select the three-dimensional point with the earliest encoded position information among the three-dimensional points not belonging to LoD0 as the initial point b0 of LoD1. That is, the three-dimensional data encoding device can also select the three-dimensional point with the earliest encoded position information among the three-dimensional points in the upper layer (LoD0 to LoDn-1) that do not belong to LoDn as the initial point n0 of LoDn. Thus, the three-dimensional data decoding device can form the same LoD as during encoding by using the same initial point selection method during decoding, and thus can appropriately decode the bitstream. Specifically, the three-dimensional data decoding device selects the three-dimensional point with the earliest decoded position information among the three-dimensional points in the upper layer that do not belong to LoDn as the initial point n0 of LoDn.

[0652] Hereinafter, a method for generating a predicted value of the attribute information of the three-dimensional points using the LoD information will be described. For example, in the case of encoding the three-dimensional points included in LoD0 in sequence, the three-dimensional data encoding device uses the encoded (hereinafter, also simply referred to as "encoded") attribute information included in LoD0 and LoD1 to generate the target three-dimensional points included in LoD1. In this way, the three-dimensional data encoding device uses the encoded attribute information included in LoDn' (n' <= n) to generate a predicted value of the attribute information of the three-dimensional points included in LoDn. That is, the three-dimensional data encoding device does not use the attribute information of the three-dimensional points included in the lower layer of LoDn in the calculation of the predicted value of the attribute information of the three-dimensional points included in LoDn.

[0653] For example, the three-dimensional data encoding device generates a predicted value of the attribute information of the three-dimensional points by calculating the average value of the attribute values of N or fewer three-dimensional points among the encoded three-dimensional points around the target three-dimensional points to be encoded. In addition, the three-dimensional data encoding device can attach the value of N to the head of the bitstream or the like. In addition, the three-dimensional data encoding device can also change the value of N for each three-dimensional point and attach the value of N to each three-dimensional point. Thus, an appropriate N can be selected for each three-dimensional point, so that the accuracy of the predicted value can be improved. Therefore, the prediction residual can be reduced. In addition, the three-dimensional data encoding device can also attach the value of N to the head of the bitstream and fix the value of N within the bitstream. Thus, it is not necessary to encode or decode the value of N for each three-dimensional point, so that the processing amount can be reduced. In addition, the three-dimensional data encoding device can also encode the value of N separately for each LoD. Thus, by selecting an appropriate N for each LoD, the encoding efficiency can be improved.

[0654] Alternatively, the three-dimensional data encoding device may also calculate a predicted value of the attribute information of a three-dimensional point by a weighted average of the attribute information of N encoded three-dimensional points around it. For example, the three-dimensional data encoding device calculates weights using the distance information between the target three-dimensional point and each of the N surrounding three-dimensional points.

[0655] When the three-dimensional data encoding device encodes the value of N for each LoD, for example, the value of N is set larger for the upper layer of the LoD and smaller for the lower layer. In the upper layer of the LoD, the distance between the three-dimensional points belonging to this layer is far. Therefore, by setting the value of N large and selecting multiple surrounding three-dimensional points for averaging, the prediction accuracy can be improved. In addition, since in the lower layer of the LoD, the distance between the three-dimensional points belonging to this layer is close, it is possible to perform efficient prediction while suppressing the processing amount of averaging by setting the value of N small.

[0656] Fig.71 is a diagram showing an example of the attribute information used in the predicted value. As described above, the predicted value of the point P included in LoDn is generated using the encoded surrounding points P' included in LoD N' (N' <= N). Here, the surrounding points P' are selected based on the distance from the point P. For example, the attribute information of points a0, a1, a2, b0, b1 is used to generate Fig.71 the predicted value of the attribute information of the point b2 shown.

[0657] According to the above value of N, the selected surrounding points change. For example, when N = 5, as the surrounding points of the point b2, a0, a1, a2, b0, b1 are selected. When N = 4, based on the distance information, points a0, a1, a2, b1 are selected.

[0658] The predicted value is calculated by weighted average depending on the distance. For example, in Fig.71 the example shown, the predicted value a2p of the point a2 is calculated by the weighted average of the attribute information of the points a0 and a1, as shown in (Equation A2) and (Equation A3). In addition, A i is the value of the attribute information of the point ai.

[0659]

Equation 2

[0660]

[0661]

[0662] In addition, the predicted value b2p of the point b2 is calculated by the weighted average of the attribute information of the points a0, a1, a2, b0, b1, as shown in (Equation A4) to (Equation A6). In addition, B i is the value of the attribute information of the point bi.

[0663]

Mathematical Formula 3

[0664]

[0665]

[0666]

[0667] In addition, the three-dimensional data encoding device can also calculate the difference value (prediction residual) between the value of the attribute information of the three-dimensional point and the predicted value generated from the surrounding points, and quantize the calculated prediction residual. For example, the three-dimensional data encoding device performs quantization by dividing the prediction residual by a quantization scale (also referred to as a quantization step). In this case, the smaller the quantization scale, the smaller the error (quantization error) that may be generated due to quantization. On the contrary, the larger the quantization scale, the larger the quantization error.

[0668] Furthermore, the three-dimensional data encoding device can also change the quantization scale used for each LoD. For example, the higher the layer of the three-dimensional data encoding device, the smaller the quantization scale, and the lower the layer, the larger the quantization scale. The value of the attribute information of the three-dimensional points belonging to the upper layer may be used as the predicted value of the attribute information of the three-dimensional points belonging to the lower layer. Therefore, the quantization scale of the upper layer can be reduced to suppress the quantization error generated in the upper layer, and by improving the accuracy of the predicted value, the encoding efficiency can be improved. In addition, the three-dimensional data encoding device can also attach the quantization scale used for each LoD to the header or the like. As a result, the three-dimensional data decoding device can correctly decode the quantization scale, and thus can appropriately decode the bitstream.

[0669] In addition, the three-dimensional data encoding device can also transform the signed integer value (signed quantization value) that is the quantized prediction residual into an unsigned integer value (unsigned quantization value). As a result, when performing entropy encoding on the prediction residual, there is no need to consider the generation of negative integers. In addition, the three-dimensional data encoding device does not necessarily need to transform the signed integer value into an unsigned integer value. For example, it can also perform entropy encoding on the sign bit separately.

[0670] The prediction residual is calculated by subtracting the predicted value from the original value. For example, as shown in (Equation A7), the prediction residual a2r of point a2 is calculated by subtracting the predicted value a2p of point a2 from the value A2 of the attribute information of point a2. As shown in (Equation A8), the prediction residual b2r of point b2 is calculated by subtracting the predicted value b2p of point b2 from the value B2 of the attribute information of point b2.

[0671] a2r = A2 - a2p … (Equation A7)

[0672] b2r = B2 - b2p … (Equation A8)

[0673] In addition, the prediction residual is quantized by dividing it by QS (Quantization Step). For example, the quantized value a2q of point a2 is calculated by (Equation A9). The quantized value b2q of point b2 is calculated by (Equation A10). Here, QS_LoD0 is the QS for LoD0, and QS_LoD1 is the QS for LoD1. That is, the QS can be changed according to the LoD.

[0674] a2q = a2r / QS_LoD0 … (Equation A9)

[0675] b2q = b2r / QS_LoD1 … (Equation A10)

[0676] Furthermore, as described below, the three-dimensional data encoding device transforms the signed integer value, which is the above-mentioned quantized value, into an unsigned integer value. When the signed integer value a2q is less than 0, the three-dimensional data encoding device sets the unsigned integer value a2u to -1 - (2 × a2q). When the signed integer value a2q is 0 or more, the three-dimensional data encoding device sets the unsigned integer value a2u to 2 × a2q.

[0677] Similarly, when the signed integer value b2q is less than 0, the three-dimensional data encoding device sets the unsigned integer value b2u to -1 - (2 × b2q). When the signed integer value b2q is 0 or more, the three-dimensional data encoding device sets the unsigned integer value b2u to 2 × b2q.

[0678] In addition, the three-dimensional data encoding device can also encode the quantized prediction residual (unsigned integer value) by entropy encoding. For example, binary arithmetic coding can also be applied after binarizing the unsigned integer value.

[0679] In addition, in this case, the three-dimensional data encoding device can also switch the binarization method according to the value of the prediction residual. For example, when the prediction residual pu is less than the threshold R_TH, the three-dimensional data encoding device binarizes the prediction residual pu with a fixed number of bits required to represent the threshold R_TH. In addition, when the prediction residual pu is equal to or greater than the threshold R_TH, the three-dimensional data encoding device binarizes the binarized data of the threshold R_TH and the value of (pu - R_TH) using, for example, Exponential-Golomb.

[0680] For example, when the threshold R_TH is 63 and the prediction residual pu is less than 63, the three-dimensional data encoding device binarizes the prediction residual pu with 6 bits. Additionally, when the prediction residual pu is 63 or more, the three-dimensional data encoding device binarizes the binary data (111111) of the threshold R_TH and (pu - 63) using exponential Golomb, and then performs arithmetic coding accordingly.

[0681] In a more specific example, when the prediction residual pu is 32, the three-dimensional data encoding device generates 6-bit binary data (100000) and performs arithmetic coding on this bit string. Additionally, when the prediction residual pu is 66, the three-dimensional data encoding device generates a bit string (00100) representing the binary data (111111) of the threshold R_TH and the value 3 (66 - 63) using exponential Golomb, and performs arithmetic coding on this bit string (111111 + 00100).

[0682] In this way, the three-dimensional data encoding device switches the binarization method according to the magnitude of the prediction residual, thereby enabling encoding while suppressing a sharp increase in the number of binarization bits when the prediction residual becomes large. In addition, the three-dimensional data encoding device may also attach the threshold R_TH to the head of the bit stream or the like.

[0683] For example, when encoding at a high bit rate, that is, when the quantization scale is small, the quantization error becomes small and the prediction accuracy becomes high. As a result, the prediction residual may not become large. Therefore, in this case, the three-dimensional data encoding device sets the threshold R_TH large. Thereby, the possibility of encoding the binary data of the threshold R_TH becomes low, and the encoding efficiency is improved. On the contrary, when encoding at a low bit rate, that is, when the quantization scale is large, the quantization error becomes large and the prediction accuracy deteriorates. As a result, it is possible that the prediction residual becomes large. Therefore, in this case, the three-dimensional data encoding device sets the threshold R_TH small. Thereby, a sharp increase in the bit length of the binary data can be prevented.

[0684] In addition, the three-dimensional data encoding device may also switch the threshold R_TH for each LoD and attach the threshold R_TH of each LoD to the head or the like. That is, the three-dimensional data encoding device may also switch the binarization method for each LoD. For example, in the upper layer, since the distance between three-dimensional points is far, the prediction accuracy deteriorates, and as a result, the prediction residual may become large. Therefore, the three-dimensional data encoding device sets the threshold R_TH small for the upper layer to prevent a sharp increase in the bit length of the binary data. Additionally, in the lower layer, since the distance between three-dimensional points is close, the prediction accuracy becomes high, and as a result, it is possible that the prediction residual becomes small. Therefore, the three-dimensional data encoding device sets the threshold R_TH large for the layer to improve the encoding efficiency.

[0685] Fig.72 This is a diagram showing an example of an exponential Golomb code, which shows the relationship between the values before binarization (multi-valued) and the bits (codes) after binarization. Additionally, it is also possible to Fig.72 invert the 0 and 1 shown.

[0686] Furthermore, the three-dimensional data encoding device applies arithmetic coding to the binarized data of the prediction residual. Thereby, the encoding efficiency can be improved. In addition, when applying arithmetic coding, in the binarized data, in the part binarized with n bits, i.e., the n-bit code, and the part binarized using exponential Golomb, i.e., the remaining code, the tendency of the occurrence probabilities of 0 and 1 for each bit may be different. Therefore, the three-dimensional data encoding device can also switch the application method of arithmetic coding between the n-bit code and the remaining code.

[0687] For example, for the n-bit code, the three-dimensional data encoding device performs arithmetic coding on each bit using a different coding table (probability table). At this time, the three-dimensional data encoding device can also change the number of coding tables used for each bit. For example, the three-dimensional data encoding device uses 1 coding table to perform arithmetic coding on the leading bit b0 of the n-bit code. Additionally, the three-dimensional data encoding device uses 2 coding tables for the next bit b1. Furthermore, the three-dimensional data encoding device switches the coding table used in the arithmetic coding of bit b1 according to the value of b0 (0 or 1). Similarly, the three-dimensional data encoding device uses 4 coding tables for the next bit b2. Additionally, the three-dimensional data encoding device switches the coding table used in the arithmetic coding of bit b2 according to the values of b0 and b1 (0 to 3).

[0688] In this way, when the three-dimensional data encoding device performs arithmetic coding on each bit bn-1 of the n-bit code, it uses 2 n-1 coding tables. Additionally, the three-dimensional data encoding device switches the coding table used according to the values (occurrence patterns) of the bits before bn-1. Thereby, the three-dimensional data encoding device can use an appropriate coding table for each bit, and thus can improve the encoding efficiency.

[0689] Furthermore, the three-dimensional data encoding device can also reduce the number of coding tables used for each bit. For example, when performing arithmetic coding on each bit bn-1, the three-dimensional data encoding device can also switch 2 mA coding table. Thus, the number of coding tables used in each bit can be suppressed, and the coding efficiency can be improved. In addition, the three-dimensional data coding device can also update the occurrence probabilities of 0 and 1 in each coding table according to the value of the actually generated binarized data. Additionally, the three-dimensional data coding device can also fix the occurrence probabilities of 0 and 1 in the coding tables of a part of the bits. Thus, the number of times of updating the occurrence probability can be suppressed, and the processing amount can be reduced.

[0690] For example, when n bits are encoded as b0b1b2…bn-1, the coding table used for b0 is 1 (CTb0). The coding tables used for b1 are 2 (CTb10, CTb11). Additionally, the coding table to be used is switched according to the value of b0 (0 to 1). The coding tables used for b2 are 4 (CTb20, CTb21, CTb22, CTb23). Further, the coding table to be used is switched according to the values of b0 and b1 (0 to 3). The coding tables used for bn-1 are 2 n-1 (CTbn0, CTbn1, …, CTbn(2 n-1 -1)). Additionally, the coding table to be used is switched according to the value of b0b1…bn-2 (0 to 2 n-1 -1).

[0691] In addition, the three-dimensional data coding device can also apply m-ary arithmetic coding (m = 2 n ) that sets values from 0 to 2 n -1 without binarizing the n-bit encoding. Further, when the three-dimensional data coding device performs arithmetic coding on the n-bit encoding using m-ary, the three-dimensional data decoding device can also restore the n-bit encoding by m-ary arithmetic decoding.

[0692] Fig.73 is a diagram for explaining the processing when, for example, the residual coding is an exponential Golomb code. As Fig.73 shown, the part where binarization is performed using exponential Golomb, that is, the residual coding, includes a prefix part and a suffix part. For example, the three-dimensional data coding device switches the coding table between the prefix part and the suffix part. That is, the three-dimensional data coding device performs arithmetic coding on each bit included in the prefix part using the coding table for the prefix, and performs arithmetic coding on each bit included in the suffix part using the coding table for the suffix.

[0693] In addition, the three-dimensional data encoding device can also update the occurrence probabilities of 0 and 1 in each encoding table according to the values of the actually generated binarized data. Alternatively, the three-dimensional data encoding device can also fix the occurrence probabilities of 0 and 1 in a certain encoding table. Thereby, the number of times of updating the occurrence probabilities can be suppressed, and thus the processing amount can be reduced. For example, the three-dimensional data encoding device can update the occurrence probabilities for the prefix part and fix the occurrence probabilities for the suffix part.

[0694] In addition, the three-dimensional data encoding device decodes the quantized prediction residual through inverse quantization and reconstruction, and uses the decoded prediction residual, that is, the decoded value, for prediction after the three-dimensional points to be encoded. Specifically, the three-dimensional data encoding device calculates the inverse quantization value by multiplying the quantized prediction residual (quantized value) by the quantization scale, and obtains the decoded value (reconstructed value) by adding the inverse quantization value and the predicted value.

[0695] For example, the inverse quantization value a2iq of point a2 is calculated using the quantized value a2q of point a2 through (Equation A11). The inverse quantization value b2iq of point b2 is calculated using the quantized value b2q of point b2 through (Equation A12). Here, QS_LoD0 is the QS for LoD0, and QS_LoD1 is the QS for LoD1. That is, the QS can be changed according to the LoD.

[0696] a2iq = a2q × QS_LoD0…(Equation A11)

[0697] b2iq = b2q × QS_LoD1…(Equation A12)

[0698] For example, as shown in (Equation A13), the decoded value a2rec of point a2 is calculated by adding the inverse quantization value a2iq of point a2 to the predicted value a2p of point a2. As shown in (Equation A14), the decoded value b2rec of point b2 is calculated by adding the inverse quantization value b2iq of point b2 to the predicted value b2p of point b2.

[0699] a2rec = a2iq + a2p…(Equation A13)

[0700] b2rec = b2iq + b2p…(Equation A14)

[0701] Hereinafter, a syntax example of the bitstream of the present embodiment will be described. Fig.74 is a diagram showing a syntax example of the attribute header of the present embodiment. The attribute header is the header information of the attribute information. As Fig.74As shown, the attribute header includes the number of levels information (NumLoD), the three-dimensional point number information (NumOfPoint[i]), the level threshold (Thres_Lod[i]), the surrounding point number information (NumNeighorPoint[i]), the prediction threshold (THd[i]), the quantization scale (QS[i]), and the binarization threshold (R_TH[i]).

[0702] The number of levels information (NumLoD) represents the number of levels of LoD used.

[0703] The three-dimensional point number information (NumOfPoint[i]) represents the number of three-dimensional points belonging to level i. In addition, the three-dimensional data encoding device may also attach the three-dimensional point total number information (AllNumOfPoint) representing the total number of three-dimensional points to other headers. In this case, the three-dimensional data encoding device may not attach NumOfPoint[NumLoD - 1] representing the number of three-dimensional points belonging to the bottommost level to the header. In this case, the three-dimensional data decoding device can calculate NumOfPoint[NumLoD - 1] through (Equation A15). Thus, the encoding amount of the header can be reduced.

[0704]

Equation 4

[0705]

[0706] The level threshold (Thres_Lod[i]) is a threshold for setting level i. The three-dimensional data encoding device and the three-dimensional data decoding device form LoDi such that the distance between each point within LoDi is greater than the threshold Thres_LoD[i]. In addition, the three-dimensional data encoding device may not attach the value of Thres_Lod[NumLoD - 1] (the bottommost level) to the header. In this case, the three-dimensional data decoding device estimates the value of Thres_Lod[NumLoD - 1] as 0. Thus, the encoding amount of the header can be reduced.

[0707] The surrounding point number information (NumNeighorPoint[i]) represents the upper limit value of the number of surrounding points used in the generation of the predicted value of the three-dimensional points belonging to level i. When the number of surrounding points M is less than NumNeighorPoint[i] (M < NumNeighorPoint[i]), the three-dimensional data encoding device may also use M surrounding points to calculate the predicted value. In addition, when it is not necessary to separate the value of NumNeighorPoint[i] in each LoD, the three-dimensional data encoding device may also attach 1 surrounding point number information (NumNeighorPoint) used in all LoDs to the header.

[0708] The prediction threshold (THd[i]) represents the upper limit value of the distance between the surrounding three-dimensional points used in the prediction of the object three-dimensional points for encoding or decoding the object at level i. The three-dimensional data encoding device and the three-dimensional data decoding device do not use the three-dimensional points whose distance from the object three-dimensional points is farther than THd[i] for prediction. Additionally, when it is not necessary to separate the values of THd[i] in each LoD, the three-dimensional data encoding device may also attach one prediction threshold (THd) used in all LoDs to the header.

[0709] The quantization scale (QS[i]) represents the quantization scale used in quantization and inverse quantization at level i.

[0710] The binarization threshold (R_TH[i]) is a threshold for switching the binarization method of the prediction residual of the three-dimensional points belonging to level i. For example, when the prediction residual is less than the threshold R_TH, the three-dimensional data encoding device binarizes the prediction residual pu with a fixed number of bits, and when the prediction residual is equal to or greater than the threshold R_TH, it binarizes the binarized data of the threshold R_TH and the value of (pu - R_TH) using Exponential Golomb. Additionally, when it is not necessary to switch the values of R_TH[i] in each LoD, the three-dimensional data encoding device may also attach one binarization threshold (R_TH) used in all LoDs to the header.

[0711] Furthermore, R_TH[i] may also be the maximum value represented by nbit. For example, in 6bit, R_TH is 63, and in 8bit, R_TH is 255. Additionally, instead of encoding the maximum value represented by nbit as the binarization threshold, the three-dimensional data encoding device may encode the number of bits. For example, the three-dimensional data encoding device may attach the value 6 to the header when R_TH[i] = 63, and attach the value 8 to the header when R_TH[i] = 255. Additionally, the three-dimensional data encoding device may also define the minimum number of bits (minimum bit number) representing R_TH[i] and attach the relative number of bits with respect to the minimum value to the header. For example, the three-dimensional data encoding device may attach the value 0 to the header when R_TH[i] = 63 and the minimum bit number is 6, and attach the value 2 to the header when R_TH[i] = 255 and the minimum bit number is 6.

[0712] Additionally, the three-dimensional data encoding device may also perform entropy encoding on at least one of NumLoD, Thres_Lod[i], NumNeighborPoint[i], THd[i], QS[i], and R_TH[i] and attach it to the header. For example, the three-dimensional data encoding device may also perform arithmetic encoding by binarizing each value. Additionally, in order to suppress the processing amount, the three-dimensional data encoding device may also encode each value with a fixed length.

[0713] In addition, the three-dimensional data encoding device may not attach at least one of NumLoD, Thres_Lod[i], NumNeighborPoint[i], THd[i], QS[i], and R_TH[i] to the header. For example, the value of at least one of them may also be specified by a profile or level of a standard or the like. Thereby, the bit amount of the header can be reduced.

[0714] Fig.75 FIG. is a syntax example of attribute data of the present embodiment. The attribute data includes encoded data of attribute information of a plurality of three-dimensional points. As Fig.75 shown, the attribute data includes an n-bit code and a remaining code.

[0715] The n-bit code is encoded data of a prediction residual of the value of the attribute information or a part thereof. The bit length of the n-bit code depends on the value of R_TH[i]. For example, when the value shown in R_TH[i] is 63, the n-bit code is 6 bits, and when the value shown in R_TH[i] is 255, the n-bit code is 8 bits.

[0716] The remaining code is encoded data encoded by exponential Golomb coding in the encoded data of the prediction residual of the value of the attribute information. When the n-bit code is the same as R_TH[i], the remaining code is encoded or decoded. In addition, the three-dimensional data decoding device adds the value of the n-bit code and the value of the remaining code to decode the prediction residual. Further, when the n-bit code is not the same value as R_TH[i], the remaining code may not be encoded or decoded.

[0717] Hereinafter, the flow of processing in the three-dimensional data encoding device will be described. Fig.76 FIG. is a flowchart of three-dimensional data encoding processing performed by the three-dimensional data encoding device.

[0718] First, the three-dimensional data encoding device encodes position information (geometry) (S3001). For example, the three-dimensional data encoding is performed using an octree representation.

[0719] After the three-dimensional data encoding device encodes the position information, when the positions of the three-dimensional points change due to quantization or the like, the three-dimensional data encoding device reassigns the attribute information of the original three-dimensional points to the changed three-dimensional points (S3002). For example, the three-dimensional data encoding device performs the reassignment by interpolating the value of the attribute information according to the amount of change in the position. For example, the three-dimensional data encoding device detects N three-dimensional points before the change that are close to the changed three-dimensional position, and performs a weighted average on the values of the attribute information of the N three-dimensional points. For example, in the weighted average, the three-dimensional data encoding device determines the weights based on the distances from the changed three-dimensional position to each of the N three-dimensional points. Then, the three-dimensional data encoding device determines the value obtained by the weighted average as the value of the attribute information of the changed three-dimensional point. In addition, when two or more three-dimensional points change to the same three-dimensional position due to quantization or the like, the three-dimensional data encoding device may also assign the average value of the attribute information of the two or more three-dimensional points before the change as the value of the attribute information of the changed three-dimensional point.

[0720] Next, the three-dimensional data encoding device encodes the reassigned attribute information (S3003). For example, when encoding multiple types of attribute information, the three-dimensional data encoding device may also encode the multiple types of attribute information sequentially. For example, when encoding color and reflectance as the attribute information, the three-dimensional data encoding device may also generate a bitstream in which the encoding result of the reflectance is appended after the encoding result of the color. In addition, the order of the multiple encoding results of the attribute information appended to the bitstream is not limited to this order and can be any order.

[0721] In addition, the three-dimensional data encoding device may also append information indicating the start position of the encoded data of each attribute information in the bitstream to the header or the like. As a result, the three-dimensional data decoding device can selectively decode the attribute information that needs to be decoded, and thus can omit the decoding process of the attribute information that does not need to be decoded. Therefore, the processing amount of the three-dimensional data decoding device can be reduced. In addition, the three-dimensional data encoding device may also encode multiple types of attribute information in parallel and merge the encoding results into one bitstream. As a result, the three-dimensional data encoding device can encode multiple types of attribute information at high speed.

[0722] Fig.77 It is a flowchart of the attribute information encoding process (S3003). First, the three-dimensional data encoding device sets the LoD (S3011). That is, the three-dimensional data encoding device assigns each three-dimensional point to any one of the multiple LoDs.

[0723] Next, the three-dimensional data encoding device starts a loop in units of LoD (S3012). That is, the three-dimensional data encoding device repeatedly performs the processes of steps S3013 to S3021 for each LoD.

[0724] Next, the three-dimensional data encoding device starts a loop in units of three-dimensional points (S3013). That is, the three-dimensional data encoding device repeatedly performs the processes of steps S3014 to S3020 for each three-dimensional point.

[0725] First, the three-dimensional data encoding device searches for a plurality of surrounding points, which are three-dimensional points existing around the object three-dimensional point used in the calculation of the predicted value of the object three-dimensional point to be processed (S3014). Next, the three-dimensional data encoding device calculates the weighted average of the values of the attribute information of the plurality of surrounding points and sets the obtained value as the predicted value P (S3015). Next, the three-dimensional data encoding device calculates the difference between the attribute information of the object three-dimensional point and the predicted value, that is, the prediction residual (S3016). Next, the three-dimensional data encoding device calculates the quantization value by quantizing the prediction residual (S3017). Next, the three-dimensional data encoding device performs arithmetic coding on the quantization value (S3018).

[0726] In addition, the three-dimensional data encoding device calculates the inverse quantization value by inverse quantizing the quantization value (S3019). Next, the three-dimensional data encoding device generates the decoded value by adding the inverse quantization value to the predicted value (S3020). Next, the three-dimensional data encoding device ends the loop in units of three-dimensional points (S3021). In addition, the three-dimensional data encoding device ends the loop in units of LoD (S3022).

[0727] Hereinafter, the three-dimensional data decoding process in the three-dimensional data decoding device that decodes the bitstream generated by the above three-dimensional data encoding device will be described.

[0728] The three-dimensional data decoding device generates the decoded binarized data by performing arithmetic decoding on the binarized data of the attribute information in the bitstream generated by the three-dimensional data encoding device in the same method as the three-dimensional data encoding device. In addition, in the three-dimensional data encoding device, when the application method of arithmetic coding is switched between the part binarized with n bits (n-bit coding) and the part binarized with exponential Golomb (residual coding), the three-dimensional data decoding device performs decoding accordingly when applying arithmetic decoding.

[0729] For example, in an arithmetic decoding method of n-bit encoding, a three-dimensional data decoding device performs arithmetic decoding on each bit using a different encoding table (decoding table). At this time, the three-dimensional data decoding device can also change the number of encoding tables used for each bit. For example, for the leading bit b0 of the n-bit encoding, 1 encoding table is used for arithmetic decoding. In addition, the three-dimensional data decoding device uses 2 encoding tables for the next bit b1. Furthermore, the three-dimensional data decoding device switches the encoding table used in the arithmetic decoding of bit b1 according to the value (0 or 1) of b0. Similarly, the three-dimensional data decoding device further uses 4 encoding tables for the next bit b2. In addition, the three-dimensional data decoding device switches the encoding table used in the arithmetic decoding of bit b2 according to the values (0 to 3) of b0 and b1.

[0730] In this way, when the three-dimensional data decoding device performs arithmetic decoding on each bit bn-1 of the n-bit encoding, it uses 2 n-1 encoding tables. In addition, the three-dimensional data decoding device switches the encoding table used according to the values (occurrence patterns) of the bits before bn-1. Thereby, the three-dimensional data decoding device can use an appropriate encoding table for each bit to appropriately decode a bit stream with improved encoding efficiency.

[0731] In addition, the three-dimensional data decoding device can also reduce the number of encoding tables used for each bit. For example, the three-dimensional data decoding device can also switch 2 m encoding tables according to the values (occurrence patterns) of the m bits (m < n-1) before bn-1 when performing arithmetic decoding on each bit bn-1. Thereby, the three-dimensional data decoding device can appropriately decode a bit stream with improved encoding efficiency while suppressing the number of encoding tables used for each bit. In addition, the three-dimensional data decoding device can also update the occurrence probabilities of 0 and 1 in each encoding table according to the values of the actually generated binarized data. In addition, the three-dimensional data decoding device can also fix the occurrence probabilities of 0 and 1 in the encoding tables of a part of the bits. Thereby, the number of times of updating the occurrence probability can be suppressed, and thus the processing amount can be reduced.

[0732] For example, when the n-bit encoding is b0b1b2…bn-1, the encoding table used for b0 is 1 (CTb0). The encoding tables used for b1 are 2 (CTb10, CTb11). In addition, the encoding table is switched according to the value (0 to 1) of b0. The encoding tables used for b2 are 4 (CTb20, CTb21, CTb22, CTb23). In addition, the encoding table is switched according to the values (0 to 3) of b0 and b1. The encoding tables used for bn-1 are 2 n-1 (CTbn0, CTbn1, …, CTbn(2 n-1 -1)). In addition, according to the values (0 to 2 n-1-1) to switch the code table.

[0733] For example, Fig.78 is a diagram for explaining the processing in the case where the remaining code is the Exponential Golomb code. As Fig.78 shown, the three-dimensional data encoding device encodes a binarized part (remaining code) using the Exponential Golomb, which includes a prefix part and a suffix part. For example, the three-dimensional data decoding device switches the code table between the prefix part and the suffix part. That is, the three-dimensional data decoding device performs arithmetic decoding on each bit included in the prefix part using the prefix code table, and performs arithmetic decoding on each bit included in the suffix part using the suffix code table.

[0734] In addition, the three-dimensional data decoding device can also update the occurrence probabilities of 0 and 1 in each code table according to the value of the binarized data generated during decoding. Alternatively, the three-dimensional data decoding device can fix the occurrence probabilities of 0 and 1 in a certain code table. Thus, the number of times of updating the occurrence probabilities can be suppressed, and the processing amount can be reduced. For example, the three-dimensional data decoding device can update the occurrence probability for the prefix part and fix the occurrence probability for the suffix part.

[0735] In addition, the three-dimensional data decoding device multi-values the binarized data of the predicted residual obtained by arithmetic decoding to match the coding method used in the three-dimensional data encoding device, thereby decoding the quantized predicted residual (unsigned integer value). The three-dimensional data decoding device first calculates the value of the n-bit code decoded by performing arithmetic decoding on the binarized data encoded by n bits. Then, the three-dimensional data decoding device compares the value of the n-bit code with the value of R_TH.

[0736] When the value of the n-bit code is consistent with the value of R_TH, the three-dimensional data decoding device determines that there are bits encoded by the Exponential Golomb next, and performs arithmetic decoding on the binarized data encoded by the Exponential Golomb, that is, the remaining code. Then, the three-dimensional data decoding device calculates the value of the remaining code using the inverse table representing the relationship between the remaining code and the value according to the decoded remaining code. Fig.79 is a diagram showing an example of the inverse table representing the relationship between the remaining code and its value. Next, the three-dimensional data decoding device obtains the multi-valued quantized predicted residual by adding the value of the obtained remaining code to R_TH.

[0737] On the other hand, when the value of the n-bit encoding does not match the value of R_TH (the value is less than R_TH), the three-dimensional data decoding device directly determines the value of the n-bit encoding as the quantized prediction residual after multi-valued quantization. Thus, the three-dimensional data decoding device can appropriately decode the bitstream generated by switching the binarization method according to the value of the prediction residual in the three-dimensional data encoding device.

[0738] In addition, when the threshold R_TH is attached to the head of the bitstream or the like, the three-dimensional data decoding device can also decode the value of the threshold R_TH from the head and use the decoded value of the threshold R_TH to switch the decoding method. Additionally, when the threshold R_TH is attached to the head or the like for each LoD, the three-dimensional data decoding device switches the decoding method for each LoD using the decoded threshold R_TH.

[0739] For example, when the threshold R_TH is 63 and the decoded value of the n-bit encoding is 63, the three-dimensional data decoding device decodes the remaining encoding using exponential Golomb to obtain the value of the remaining encoding. For example, in Fig.79 the example shown, the remaining encoding is 00100, and 3 is obtained as the value of the remaining encoding. Then, the three-dimensional data decoding device adds the value 63 of the threshold R_TH and the value 3 of the remaining encoding to obtain the value 66 of the prediction residual.

[0740] In addition, when the decoded value of the n-bit encoding is 32, the three-dimensional data decoding device sets the value 32 of the n-bit encoding as the value of the prediction residual.

[0741] In addition, the three-dimensional data decoding device transforms the decoded quantized prediction residual from an unsigned integer value to a signed integer value through, for example, a process opposite to the process in the three-dimensional data encoding device. Thus, when performing entropy coding on the prediction residual, the three-dimensional data decoding device can appropriately decode the bitstream generated without considering the generation of negative integers. Additionally, the three-dimensional data decoding device does not necessarily need to transform the unsigned integer value to a signed integer value. For example, when decoding a bitstream generated by separately performing entropy coding on the sign bit, it can also decode the sign bit.

[0742] The three-dimensional data decoding device decodes the quantized prediction residual transformed into a signed integer value through inverse quantization and reconstruction, thereby generating a decoded value. In addition, the three-dimensional data decoding device uses the generated decoded value for prediction after the three-dimensional point to be decoded. Specifically, the three-dimensional data decoding device calculates the inverse quantization value by multiplying the quantized prediction residual by the decoded quantization scale, and adds the inverse quantization value and the prediction value to obtain the decoded value.

[0743] The decoded unsigned integer value (unsigned quantization value) is transformed into a signed integer value through the following process. When the least significant bit (LSB) of the decoded unsigned integer value a2u is 1, the 3D data decoding device sets the signed integer value a2q to -((a2u + 1) >> 1). When the LSB of the unsigned integer value a2u is not 1, the 3D data decoding device sets the signed integer value a2q to (a2u >> 1).

[0744] Similarly, when the LSB of the decoded unsigned integer value b2u is 1, the 3D data decoding device sets the signed integer value b2q to -((b2u + 1) >> 1). When the LSB of the unsigned integer value n2u is not 1, the 3D data decoding device sets the signed integer value b2q to (b2u >> 1).

[0745] In addition, the details of the inverse quantization and reconstruction processing performed by the 3D data decoding device are the same as those of the inverse quantization and reconstruction processing in the 3D data encoding device.

[0746] Hereinafter, the processing flow in the 3D data decoding device will be described. Fig.80 It is a flowchart of the 3D data decoding process performed by the 3D data decoding device. First, the 3D data decoding device decodes the position information (geometry) from the bitstream (S3031). For example, the 3D data decoding device performs decoding using an octree representation.

[0747] Next, the 3D data decoding device decodes the attribute information (Attribute) from the bitstream (S3032). For example, when decoding multiple types of attribute information, the 3D data decoding device can also decode the multiple types of attribute information sequentially. For example, when decoding color and reflectance as attribute information, the 3D data decoding device decodes the encoding result of the color and the encoding result of the reflectance in the order attached to the bitstream. For example, when the encoding result of the reflectance is attached after the encoding result of the color in the bitstream, the 3D data decoding device decodes the encoding result of the color and then decodes the encoding result of the reflectance. In addition, the 3D data decoding device can decode the encoding results of the attribute information attached to the bitstream in any order.

[0748] In addition, the three-dimensional data decoding device can also obtain information indicating the start position of the encoded data of each attribute information in the bitstream by decoding the header. Thus, the three-dimensional data decoding device can selectively decode the attribute information that needs to be decoded, and therefore can omit the decoding process of the attribute information that does not need to be decoded. As a result, the processing amount of the three-dimensional data decoding device can be reduced. In addition, the three-dimensional data decoding device can also decode multiple types of attribute information in parallel and merge the decoding results into one three-dimensional point cloud. Thus, the three-dimensional data decoding device can decode multiple types of attribute information at high speed.

[0749] Fig.81 is a flowchart of the attribute information decoding process (S3032). First, the three-dimensional data decoding device sets the LoD (S3041). That is, the three-dimensional data decoding device assigns each of the multiple three-dimensional points with decoded position information to any one of the multiple LoDs. For example, this assignment method is the same as the assignment method used in the three-dimensional data encoding device.

[0750] Next, the three-dimensional data decoding device starts a loop for each LoD (S3042). That is, the three-dimensional data decoding device repeatedly performs the processes of steps S3043 to S3049 for each LoD.

[0751] Next, the three-dimensional data decoding device starts a loop for each three-dimensional point (S3043). That is, the three-dimensional data decoding device repeatedly performs the processes of steps S3044 to S3048 for each three-dimensional point.

[0752] First, the three-dimensional data decoding device searches for multiple surrounding points, which are three-dimensional points existing around the object three-dimensional point used in the calculation of the predicted value of the object three-dimensional point to be processed (S3044). Next, the three-dimensional data decoding device calculates the weighted average of the values of the attribute information of the multiple surrounding points and sets the obtained value as the predicted value P (S3045). In addition, these processes are the same as the processes in the three-dimensional data encoding device.

[0753] Next, the three-dimensional data decoding device performs arithmetic decoding on the quantization value from the bitstream (S3046). In addition, the three-dimensional data decoding device calculates the inverse quantization value by performing inverse quantization on the decoded quantization value (S3047). Next, the three-dimensional data decoding device generates a decoded value by adding the inverse quantization value to the predicted value (S3048). Next, the three-dimensional data decoding device ends the loop for each three-dimensional point (S3049). In addition, the three-dimensional data decoding device ends the loop for each LoD (S3050).

[0754] Next, the structures of the three-dimensional data encoding de...

Claims

1. A three-dimensional data encoding method, which is a three-dimensional data encoding method for encoding a plurality of three-dimensional points with attribute information, wherein, Based on the position information of each of the plurality of three-dimensional points, each of the plurality of three-dimensional points is assigned to one of a plurality of levels such that the distance between the three-dimensional points belonging to the upper level is greater than the distance between the three-dimensional points belonging to the lower level, Using the levels to encode the plurality of attribute information of the plurality of three-dimensional points, Encoding information representing the number of three-dimensional points belonging to each of the plurality of levels.

2. The three-dimensional data encoding method according to claim 1, wherein, In the encoding of the plurality of attribute information, referring to the level to which the target three-dimensional point belongs and the three-dimensional points of the upper levels above this level, and not referring to the three-dimensional points of the lower levels below this level, encoding the attribute information of the target three-dimensional point.

3. A three-dimensional data decoding method, which is a three-dimensional data decoding method for decoding a plurality of three-dimensional points with attribute information, wherein, Decoding from a bitstream information representing the number of three-dimensional points belonging to each of the plurality of levels to which each of the plurality of three-dimensional points is assigned, In the assignment, based on the position information of each of the plurality of three-dimensional points, each of the plurality of three-dimensional points is assigned to one of a plurality of levels such that the distance between the three-dimensional points belonging to the upper level is greater than the distance between the three-dimensional points belonging to the lower level, Decoding the plurality of attribute information of the plurality of three-dimensional points from the bitstream.

4. The three-dimensional data decoding method according to claim 3, wherein, In the decoding of the plurality of attribute information, using the information to determine the level to which the three-dimensional point belongs.

5. The three-dimensional data decoding method according to claim 4, wherein, In the decoding of the plurality of attribute information, Decoding the attribute information of the three-dimensional points belonging to the target level and counting the number of three-dimensional points belonging to the target level whose attribute information has been decoded, Determining the level to which the three-dimensional point belongs by comparing the counted number of the three-dimensional points with the number of the three-dimensional points represented by the information.

6. The three-dimensional data decoding method according to claim 4 or 5, wherein, The three-dimensional data decoding method further, Performing at least a part of the processing for decoding the plurality of attribute information and at least a part of the processing for the assignment in parallel.

7. The three-dimensional data decoding method according to claim 6, wherein, In the assignment, based on the distance between the plurality of three-dimensional points, each of the plurality of three-dimensional points is assigned to one of a plurality of levels.

8. The three-dimensional data decoding method according to claim 6, wherein, In the decoding of the plurality of attribute information, referring to the level to which the target three-dimensional point belongs and the three-dimensional points of the upper levels above this level, and not referring to the three-dimensional points of the lower levels below this level, decoding the attribute information of the target three-dimensional point.

9. A three-dimensional data encoding device is a three-dimensional data encoding device that encodes a plurality of three-dimensional points with attribute information, wherein, Comprises: A processor; and A memory, The processor uses the memory, Based on the position information of each of the plurality of three-dimensional points, assign each of the plurality of three-dimensional points to one of a plurality of hierarchies such that the distance between three-dimensional points belonging to a higher hierarchy is greater than the distance between three-dimensional points belonging to a lower hierarchy, Encode a plurality of attribute information of the plurality of three-dimensional points using the hierarchies, Encode information representing the number of three-dimensional points belonging to each of the plurality of hierarchies.

10. A three-dimensional data decoding device is a three-dimensional data decoding device that decodes a plurality of three-dimensional points having attribute information, wherein, Comprising: A processor; And A memory, The processor uses the memory, Decode from a bitstream information representing the number of three-dimensional points belonging to each of the plurality of hierarchies to which each of the plurality of three-dimensional points is assigned, In the assignment, based on the position information of each of the plurality of three-dimensional points, assign each of the plurality of three-dimensional points to one of a plurality of hierarchies such that the distance between three-dimensional points belonging to a higher hierarchy is greater than the distance between three-dimensional points belonging to a lower hierarchy, Decode a plurality of attribute information of the plurality of three-dimensional points from the bitstream.

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