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

Abstract

To provide a three-dimensional data encoding method capable of reducing the processing amount in the decoding device.SOLUTION: The three-dimensional data encoding method includes the steps of: encoding a piece of first coordinate information that represents coordinates in the first coordinate system where multiple 3D points exist; generating first conversion parameters for converting the first coordinate information into a piece of second coordinate information indicating the coordinates in the second coordinate system where multiple 3D points exist; generating second conversion parameters for converting the second coordinate information into a piece of third coordinate information indicating coordinates in the third coordinate system where multiple 3D points exist; and generating an encoding bitstream that includes the first coordinate information, the first conversion parameters, and the second conversion parameters.SELECTED DRAWING: Figure 101

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 device, and a three-dimensional data decoding device. [Background technology]

[0002] In the future, devices and services that utilize 3D data are expected to become widespread in a wide range of fields, including computer vision for autonomous operation of automobiles or robots, map information, surveillance, infrastructure inspection, video distribution, etc. 3D data can be acquired in a variety of ways, including distance sensors such as range finders, stereo cameras, or a combination of multiple monocular cameras.

[0003] One method of representing three-dimensional data is a point cloud, which represents the shape of a three-dimensional structure using a group of points in three-dimensional space. A point cloud stores the position and color of the points. Point clouds are expected to become the mainstream method of representing three-dimensional data, but point clouds require a very large amount of data. Therefore, when storing or transmitting three-dimensional data, data compression through encoding is essential, just as with two-dimensional video images (examples include MPEG-4 AVC or HEVC standardized by MPEG).

[0004] In addition, compression of point clouds is partially supported by public libraries that perform point cloud-related processing (Point Cloud Library).

[0005] Furthermore, a technique is known in which three-dimensional map data is used to search for and display facilities located around a vehicle (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0006] [Patent Document 1] International Publication No. 2014 / 020663 [Non-patent literature]

[0007] [Non-Patent Document 1] Gerdan, GP and Deakin, RE (1999), Transforming Cartesian Coordinates X, Y, Z to Geographical Coordinates φ, λ, h, The Australian Surveyor, Vol. 44, No. 1, 55-63. Summary of the Invention [Problem to be solved by the invention]

[0008] In such a system using three-dimensional data, it is desirable to be able to reduce the amount of processing in the decoding device.

[0009] 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 device, or a three-dimensional data decoding device that can reduce the amount of processing in a decoding device. [Means for solving the problem]

[0010] A three-dimensional data encoding method according to one embodiment of the present disclosure is a three-dimensional data encoding method for encoding a plurality of three-dimensional points, which encodes first coordinate information indicating the coordinates at which the plurality of three-dimensional points exist in a first coordinate system, generates first transformation parameters for converting the first coordinate information into second coordinate information indicating the coordinates at which the plurality of three-dimensional points exist in a second coordinate system, generates second transformation parameters for converting the second coordinate information into third coordinate information indicating the coordinates at which the plurality of three-dimensional points exist in a third coordinate system, and generates an encoded bitstream including the first coordinate information, the first transformation parameters, and the second transformation parameters.

[0011] A three-dimensional data decoding method according to one embodiment of the present disclosure is a three-dimensional data decoding method for decoding a plurality of three-dimensional points, which decodes, from an encoded bitstream, first coordinate information indicating coordinates at which the plurality of three-dimensional points exist in a first coordinate system, converts the first coordinate information using a first transformation parameter into second coordinate information indicating coordinates at which the plurality of three-dimensional points exist in a second coordinate system, and converts the second coordinate information using the second transformation parameter into third coordinate information indicating coordinates at which the plurality of three-dimensional points exist in a third coordinate system. [Effects of the Invention]

[0012] The present disclosure can provide a three-dimensional data encoding method, a three-dimensional data decoding method, a three-dimensional data encoding device, or a three-dimensional data decoding device that can reduce the amount of processing in a decoding device. [Brief explanation of the drawings]

[0013] [Figure 1] FIG. 1 is a diagram showing the structure of encoded three-dimensional data according to the first embodiment. [Figure 2] FIG. 2 is a diagram illustrating an example of a prediction structure between SPCs belonging to the lowest layer of a GOS according to the first embodiment. [Figure 3] FIG. 3 is a diagram showing an example of an inter-layer prediction structure according to the first embodiment. [Figure 4] FIG. 4 is a diagram showing an example of the coding order of the GOS according to the first embodiment. [Figure 5] FIG. 5 is a diagram showing an example of the coding order of the GOS according to the first embodiment. [Figure 6] FIG. 6 is a block diagram of a three-dimensional data encoding device according to the first embodiment. [Figure 7] FIG. 7 is a flowchart of the encoding process according to the first embodiment. [Figure 8] FIG. 8 is a block diagram of a three-dimensional data decoding device according to the first embodiment. [Figure 9] FIG. 9 is a flowchart of the decoding process according to the first embodiment. [Figure 10]FIG. 10 is a diagram illustrating an example of meta information according to the first embodiment. [Figure 11] FIG. 11 is a diagram illustrating an example of the configuration of the SWLD according to the second embodiment. [Figure 12] FIG. 12 illustrates an example of the operation of the server and the client according to the second embodiment. [Figure 13] FIG. 13 is a diagram illustrating an example of the operation of the server and the client according to the second embodiment. [Figure 14] FIG. 14 is a diagram illustrating an example of the operation of the server and the client according to the second embodiment. [Figure 15] FIG. 15 illustrates an example of the operation of the server and the client according to the second embodiment. [Figure 16] FIG. 16 is a block diagram of a three-dimensional data encoding device according to the second embodiment. [Figure 17] FIG. 17 is a flowchart of the encoding process according to the second embodiment. [Figure 18] FIG. 18 is a block diagram of a three-dimensional data decoding device according to the second embodiment. [Figure 19] FIG. 19 is a flowchart of the decoding process according to the second embodiment. [Figure 20] FIG. 20 is a diagram illustrating an example of the configuration of a WLD according to the second embodiment. [Figure 21] FIG. 21 is a diagram illustrating an example of an octree structure of a WLD according to the second embodiment. [Figure 22] FIG. 22 is a diagram illustrating an example of the configuration of the SWLD according to the second embodiment. [Figure 23] FIG. 23 is a diagram illustrating an example of an octree structure of an SWLD according to the second embodiment. [Figure 24] FIG. 24 is a block diagram of a three-dimensional data creation device according to the third embodiment. [Figure 25] FIG. 25 is a block diagram of a three-dimensional data transmission device according to the third embodiment. [Figure 26] FIG. 26 is a block diagram of a three-dimensional information processing device according to the fourth embodiment. [Figure 27] FIG. 27 is a block diagram of a three-dimensional data creation device according to the fifth embodiment. [Figure 28] FIG. 28 is a diagram illustrating a configuration of a system according to the sixth embodiment. [Figure 29] FIG. 29 is a block diagram of a client device according to the sixth embodiment. [Figure 30] FIG. 30 is a block diagram of a server according to the sixth embodiment. [Figure 31] FIG. 31 is a flowchart of a three-dimensional data creation process by a client device according to the sixth embodiment. [Figure 32] FIG. 32 is a flowchart of a sensor information transmission process performed by a client device according to the sixth embodiment. [Figure 33] FIG. 33 is a flowchart of three-dimensional data creation processing by the server according to the sixth embodiment. [Figure 34] FIG. 34 is a flowchart of a three-dimensional map transmission process performed by the server according to the sixth embodiment. [Figure 35] FIG. 35 is a diagram showing a configuration of a modified example of the system according to the sixth embodiment. [Figure 36] FIG. 36 is a diagram illustrating the configurations of a server and a client device according to the sixth embodiment. [Figure 37] FIG. 37 is a block diagram of a three-dimensional data encoding device according to the seventh embodiment. [Figure 38] FIG. 38 is a diagram showing an example of a prediction residual according to the seventh embodiment. [Figure 39] FIG. 39 is a diagram illustrating an example of a volume according to the seventh embodiment. [Figure 40] FIG. 40 is a diagram showing an example of an octree representation of a volume according to the seventh embodiment. [Figure 41] FIG. 41 is a diagram showing an example of a bit string of a volume according to the seventh embodiment. [Figure 42] FIG. 42 is a diagram showing an example of an octree representation of a volume according to the seventh embodiment. [Figure 43]FIG. 43 is a diagram illustrating an example of a volume according to the seventh embodiment. [Figure 44] FIG. 44 is a diagram illustrating the intra prediction process according to the seventh embodiment. [Figure 45] FIG. 45 is a diagram for explaining the rotation and translation processing according to the seventh embodiment. [Figure 46] FIG. 46 is a diagram showing an example of the syntax of the RT application flag and the RT information according to the seventh embodiment. [Figure 47] FIG. 47 is a diagram illustrating the inter prediction process according to the seventh embodiment. [Figure 48] FIG. 48 is a block diagram of a three-dimensional data decoding device according to the seventh embodiment. [Figure 49] FIG. 49 is a flowchart of three-dimensional data encoding processing by the three-dimensional data encoding device according to the seventh embodiment. [Figure 50] FIG. 50 is a flowchart of three-dimensional data decoding processing by the three-dimensional data decoding device according to the seventh embodiment. [Figure 51] FIG. 51 is a diagram illustrating an example of a tree structure according to the eighth embodiment. [Figure 52] FIG. 52 is a diagram showing an example of a sub 3D point cloud according to the eighth embodiment. [Figure 53] FIG. 53 is a diagram showing an example of a sub 3D point cloud according to the eighth embodiment. [Figure 54] FIG. 54 is a diagram showing an example of the structure of a bitstream according to the eighth embodiment. [Figure 55] FIG. 55 is a diagram illustrating an example of a tree structure according to the eighth embodiment. [Figure 56] FIG. 56 is a diagram for explaining fully parallel coding according to the eighth embodiment, and is a diagram showing an example of a tree structure. [Figure 57] FIG. 57 is a diagram spatially illustrating sub 3D point clouds to be processed in parallel according to the eighth embodiment. [Figure 58] FIG. 58 is a diagram showing an example of the structure of a bitstream according to the eighth embodiment. [Figure 59]FIG. 59 is a diagram illustrating the parallel decoding process according to the eighth embodiment. [Figure 60] FIG. 60 is a diagram schematically illustrating the flow of the fully parallel encoding process according to the eighth embodiment. [Figure 61] FIG. 61 is a diagram schematically illustrating the flow of the fully parallel decoding process according to the eighth embodiment. [Figure 62] FIG. 62 is a diagram for explaining progressive parallel coding according to the eighth embodiment, and is a diagram showing an example of a tree structure. [Figure 63] FIG. 63 is a diagram illustrating the operation of a core in progressive parallel encoding according to the eighth embodiment. [Figure 64] FIG. 64 is a diagram schematically illustrating the flow of the progressive sequence encoding process according to the eighth embodiment. [Figure 65] FIG. 65 is a flowchart of three-dimensional data encoding processing according to the eighth embodiment. [Figure 66] FIG. 66 is a flowchart of three-dimensional data decoding processing according to the eighth embodiment. [Figure 67] FIG. 67 is a diagram schematically illustrating an operation of the automobile according to the ninth embodiment to access a bitstream. [Figure 68] FIG. 68 is a diagram showing an example of tile division according to the ninth embodiment. [Figure 69] FIG. 69 is a diagram showing an example of tile division according to the ninth embodiment. [Figure 70] FIG. 70 is a diagram showing an example of tile division according to the ninth embodiment. [Figure 71] FIG. 71 is a diagram showing an example of tile division in a tree structure according to the ninth embodiment. [Figure 72] FIG. 72 is a diagram showing an example of three-dimensional tile division according to the ninth embodiment. [Figure 73] FIG. 73 shows tiles that are read when a car is moving according to the ninth embodiment. [Figure 74] FIG. 74 is a diagram illustrating an example of the configuration of a system according to the ninth embodiment. [Figure 75]FIG. 75 is a diagram showing an example of a tile area acquired by a car according to the ninth embodiment. [Figure 76] FIG. 76 is a diagram showing an example of a tile area acquired by a car according to the ninth embodiment. [Figure 77] FIG. 77 is a diagram showing an example of three-dimensional data according to the ninth embodiment. [Figure 78] FIG. 78 is a diagram showing an example of slice data according to the ninth embodiment. [Figure 79] FIG. 79 is a diagram showing an example of slice data according to the ninth embodiment. [Figure 80] FIG. 80 is a diagram showing an example of slice data according to the ninth embodiment. [Figure 81] FIG. 81 is a diagram showing an example of a tile according to the ninth embodiment. [Figure 82] FIG. 82 is a diagram showing an example of tiles in an octree according to the ninth embodiment. [Figure 83] FIG. 83 is a diagram showing an example of a bitstream according to the ninth embodiment. [Figure 84] FIG. 84 is a diagram showing an example of a tile table according to the ninth embodiment. [Figure 85] FIG. 85 is a diagram showing an example of a tile table according to the ninth embodiment. [Figure 86] FIG. 86 is a diagram showing an example of a tile table according to the ninth embodiment. [Figure 87] FIG. 87 is a diagram showing an example of a tile table according to the ninth embodiment. [Figure 88] FIG. 88 is a flowchart of three-dimensional data encoding processing according to the ninth embodiment. [Figure 89] FIG. 89 is a flowchart of three-dimensional data decoding processing according to the ninth embodiment. [Figure 90] FIG. 90 is a block diagram of a three-dimensional data encoding device according to the ninth embodiment. [Figure 91] FIG. 91 is a block diagram of a three-dimensional data decoding device according to the ninth embodiment. [Figure 92] FIG. 92 is a flowchart of three-dimensional data encoding processing according to the ninth embodiment. [Figure 93] FIG. 93 is a flowchart of three-dimensional data decoding processing according to the ninth embodiment. [Figure 94] FIG. 94 is a diagram illustrating an example of a three-dimensional point cloud map including a geofence according to the tenth embodiment. [Figure 95] FIG. 95 is a diagram illustrating an example of a three-dimensional point cloud map including a geofence according to the tenth embodiment. [Figure 96] FIG. 96 is a diagram illustrating a process of updating a geo-referenced point cloud according to the tenth embodiment. [Figure 97] FIG. 97 is a diagram showing an example of three-dimensional positions in a geo-referenced point cloud according to the tenth embodiment. [Figure 98] FIG. 98 is a diagram for explaining a method for acquiring a geo-referenced point group according to the tenth embodiment. [Figure 99] FIG. 99 is a diagram showing the flow of the geo-referenced point cloud acquisition process according to the tenth embodiment. [Figure 100] FIG. 100 is a diagram for explaining the process of estimating the moving direction according to the tenth embodiment. [Figure 101] FIG. 101 is a diagram schematically showing the flow of the coordinate transformation process according to the tenth embodiment. [Figure 102] FIG. 102 is a diagram for explaining the coordinate conversion process according to the tenth embodiment. [Figure 103] FIG. 103 is a diagram illustrating an example of geometric parameters of an ellipsoid according to the tenth embodiment. [Figure 104] FIG. 104 is a diagram showing EPSG3414 according to the tenth embodiment. [Figure 105] FIG. 105 is a diagram showing EPSG4947 according to the tenth embodiment. [Figure 106] FIG. 106 is a diagram showing an example of a structure of coded data in the first mode according to the tenth embodiment. [Figure 107]FIG. 107 is a diagram showing an example of the structure of a point cloud in the first mode according to the tenth embodiment. [Figure 108] FIG. 108 is a diagram illustrating an example of a tree structure of a point cloud according to the tenth embodiment. [Figure 109] FIG. 109 is a diagram showing an example of the structure of coded data in the second mode according to the tenth embodiment. [Figure 110] FIG. 110 is a diagram showing an example of the structure of a point cloud in the second mode according to the tenth embodiment. [Figure 111] FIG. 111 is a diagram showing an example of the configuration of a bitstream in the first mode according to the tenth embodiment. [Figure 112] FIG. 112 is a diagram showing an example of the configuration of a bitstream in the second mode according to the tenth embodiment. [Figure 113] FIG. 113 is a diagram showing the configuration of a three-dimensional data encoding device according to the tenth embodiment. In FIG. [Figure 114] FIG. 114 is a diagram showing the configuration of a three-dimensional data encoding device according to the tenth embodiment when coordinate transformation is not performed. [Figure 115] FIG. 115 is a diagram showing the configuration of a three-dimensional data decoding device according to the tenth embodiment. [Figure 116] FIG. 116 is a diagram showing the configuration of a three-dimensional data decoding device according to the tenth embodiment when coordinate transformation is not performed. [Figure 117] FIG. 117 is a diagram showing the configuration of a three-dimensional data decoding device according to the tenth embodiment when coordinate transformation and coordinate extension are performed. [Figure 118] FIG. 118 is a diagram showing the configuration of a three-dimensional data decoding device according to the tenth embodiment when coordinate transformation is not performed but coordinate extension is performed. [Figure 119] FIG. 119 is a flowchart of three-dimensional data encoding processing according to the tenth embodiment. [Figure 120] FIG. 120 is a flowchart of three-dimensional data decoding processing according to the tenth embodiment. [Figure 121]FIG. 121 is a flowchart of three-dimensional data encoding processing according to the tenth embodiment. [Figure 122] FIG. 122 is a flowchart of three-dimensional data decoding processing according to the tenth embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0014] 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 obtained by a sensor, encoding local coordinate information indicating a plurality of local coordinates that are coordinates of the plurality of three-dimensional points and depend on the position of the sensor, and generating an encoded bitstream that includes the encoded local coordinate information and global coordinate information indicating global coordinates that are coordinates of at least one of the plurality of three-dimensional points or a reference point and do not depend on the position of the sensor.

[0015] According to this, since there is no need to calculate global coordinates in the three-dimensional data decoding device, the amount of processing in the three-dimensional data decoding device can be reduced.

[0016] For example, the global coordinate information may indicate the global coordinates of each of the plurality of three-dimensional points.

[0017] According to this, since there is no need to calculate global coordinates in the three-dimensional data decoding device, the amount of processing in the three-dimensional data decoding device can be reduced.

[0018] For example, the global coordinate information may indicate one global coordinate associated with the plurality of three-dimensional points.

[0019] This allows the data amount of the global coordinate information to be reduced, and therefore the data amount of the coded bit stream to be reduced.

[0020] For example, the global coordinate information may indicate the global coordinate of the origin of the local coordinate system.

[0021] This allows the data amount of the global coordinate information to be reduced, and therefore the data amount of the coded bit stream to be reduced.

[0022] For example, the global coordinates may be expressed as latitude, longitude, and altitude.

[0023] For example, the global coordinates may be expressed in Cartesian coordinates.

[0024] For example, the encoded bitstream may further include information indicating whether the global coordinates are expressed in Cartesian coordinates.

[0025] For example, the coded bitstream may further include information indicating a spatial reference of the global coordinates.

[0026] 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 obtained by a sensor, which decodes, from an encoded bitstream, local coordinate information indicating a plurality of local coordinates of the plurality of three-dimensional points, the plurality of local coordinates being dependent on the position of the sensor, and decodes, from the encoded bitstream, global coordinate information indicating a global coordinate, the coordinates of at least one of the plurality of three-dimensional points or a reference point, that is independent of the position of the sensor.

[0027] According to this, since there is no need to calculate global coordinates in the three-dimensional data decoding device, the amount of processing in the three-dimensional data decoding device can be reduced.

[0028] For example, the global coordinate information may indicate the global coordinates of each of the plurality of three-dimensional points.

[0029] According to this, since there is no need to calculate global coordinates in the three-dimensional data decoding device, the amount of processing in the three-dimensional data decoding device can be reduced.

[0030] For example, the global coordinate information may indicate one global coordinate associated with the plurality of three-dimensional points.

[0031] This allows the data amount of the global coordinate information to be reduced, and therefore the data amount of the coded bit stream to be reduced.

[0032] For example, the global coordinate information may indicate the global coordinate of the origin of the local coordinate system.

[0033] This allows the data amount of the global coordinate information to be reduced, and therefore the data amount of the coded bit stream to be reduced.

[0034] For example, the global coordinates may be expressed as latitude, longitude, and altitude.

[0035] For example, the global coordinates may be expressed in Cartesian coordinates.

[0036] For example, the three-dimensional data decoding method may further decode, from the coded bitstream, information indicating whether the global coordinates are expressed in Cartesian coordinates.

[0037] For example, the three-dimensional data decoding method may further include decoding information indicating a spatial reference of the global coordinates from the coded bitstream.

[0038] Furthermore, 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 obtained by a sensor, and includes a processor and a memory, wherein the processor uses the memory to encode local coordinate information that indicates a plurality of local coordinates that are coordinates of the plurality of three-dimensional points and that depend on the position of the sensor, and generates an encoded bitstream that includes the encoded local coordinate information and global coordinate information that indicates global coordinates that are coordinates of at least one of the plurality of three-dimensional points or a reference point and that do not depend on the position of the sensor.

[0039] According to this, since there is no need to calculate global coordinates in the three-dimensional data decoding device, the amount of processing in the three-dimensional data decoding device can be reduced.

[0040] Furthermore, 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 obtained by a sensor, and includes a processor and a memory, wherein the processor uses the memory to decode, from the encoded bitstream, local coordinate information indicating a plurality of coordinates of a plurality of three-dimensional points, the plurality of local coordinates being dependent on the position of the sensor, and decodes, from the encoded bitstream, global coordinate information indicating a global coordinate that is independent of the position of the sensor, the coordinates of at least one of the plurality of three-dimensional points or a reference point.

[0041] According to this, since there is no need to calculate global coordinates in the three-dimensional data decoding device, the amount of processing in the three-dimensional data decoding device can be reduced.

[0042] These comprehensive or specific aspects may be realized as a system, a method, an integrated circuit, a computer program, or a computer-readable recording medium such as a CD-ROM, or may be realized as any combination of a system, a method, an integrated circuit, a computer program, and a recording medium.

[0043] Hereinafter, the embodiments will be described in detail with reference to the drawings. Note that each of the embodiments described below represents a specific example of the present disclosure. The numerical values, shapes, materials, components, component placement and connection configurations, steps, and step order shown in the following embodiments are merely examples and are not intended to limit the present disclosure. Furthermore, among the components in the following embodiments, components that are not described in an independent claim that represents a superordinate concept will be described as optional components.

[0044] (Embodiment 1) First, the data structure of encoded three-dimensional data (hereinafter also referred to as encoded data) according to this embodiment will be described. Fig. 1 is a diagram showing the structure of encoded three-dimensional data according to this embodiment.

[0045] In this embodiment, a three-dimensional space is divided into spaces (SPCs) corresponding to pictures in video encoding, and three-dimensional data is encoded using the spaces as units. The spaces are further divided into volumes (VLMs) corresponding to macroblocks or the like in video encoding, and prediction and conversion are performed using the VLMs as units. A volume includes a plurality of voxels (VXLs), which are the smallest units to which position coordinates can be associated. Note that prediction, like prediction performed for two-dimensional images, refers to generating predicted three-dimensional data similar to the processing unit to be processed by referring to other processing units, and encoding the difference between the predicted three-dimensional data and the processing unit to be processed. Furthermore, this prediction includes not only spatial prediction that refers to other prediction units at the same time, but also temporal prediction that refers to a prediction unit at a different time.

[0046] 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 multiple points contained in a voxel collectively according to the size of the voxel. By subdividing the voxels, the three-dimensional shape of the point cloud can be expressed with high precision, and by increasing the voxel size, the three-dimensional shape of the point cloud can be expressed roughly.

[0047] In the following, an example will be described in which the three-dimensional data is a point cloud, but the three-dimensional data is not limited to a point cloud and may be three-dimensional data in any format.

[0048] Alternatively, voxels with a hierarchical structure may be used. In this case, the nth layer may indicate in order whether a sample point exists in the n-1th layer or lower (a layer below the nth layer). For example, when decoding only the nth layer, if a sample point exists in the n-1th layer or lower, the sample point can be decoded by assuming that the sample point exists at the center of the voxel in the nth layer.

[0049] The encoding device also acquires point cloud data using a distance sensor, a stereo camera, a monocular camera, a gyro, an inertial sensor, or the like.

[0050] Similar to video coding, spaces are classified into at least three prediction structures, including independently decodable intra-space (I-SPC), predictive space (P-SPC), and bidirectional space (B-SPC). Spaces also have two types of time information: decoding time and display time.

[0051] As shown in Figure 1, there is a random access unit called a Group Of Space (GOS), which is a processing unit that includes multiple spaces. There is also a World (WLD), which is a processing unit that includes multiple GOS.

[0052] The spatial region occupied by the world is associated with an absolute position on Earth using GPS or latitude and longitude information. This position information is stored as meta information. Note that the meta information may be included in the encoded data or may be transmitted separately from the encoded data.

[0053] Furthermore, within a GOS, all SPCs may be three-dimensionally adjacent, or there may be SPCs that are not three-dimensionally adjacent to other SPCs.

[0054] In the following, the process of encoding, decoding, referencing, etc. of three-dimensional data included in a processing unit such as a GOS, SPC, or VLM will also be simply referred to as encoding, decoding, or referencing the processing unit, etc. The three-dimensional data included in the processing unit includes, for example, at least one pair of a spatial position such as three-dimensional coordinates and a characteristic value such as color information.

[0055] Next, the prediction structure of SPCs in a GOS will be explained. Multiple SPCs in the same GOS or multiple VLMs in the same SPC occupy different spaces, but have the same time information (decoding time and display time).

[0056] Furthermore, the first SPC in a GOS in decoding order is the I-SPC. There are two types of GOS: closed GOS and open GOS. A closed GOS is a GOS that can decode all SPCs in the GOS when decoding starts from the first I-SPC. In an open GOS, some SPCs that appear earlier in the GOS than the first I-SPC refer to a different GOS, and cannot be decoded using only that GOS.

[0057] In addition, in coded data such as map information, WLDs are sometimes decoded in the reverse order of coding, and if there is dependency between GOSs, reverse playback is difficult. Therefore, in such cases, closed GOSs are generally used.

[0058] Furthermore, the GOS has a layer structure in the height direction, and encoding or decoding is performed in order from the SPC in the lower layer.

[0059] Fig. 2 is a diagram showing an example of a prediction structure between SPCs belonging to the lowest layer of a GOS, and Fig. 3 is a diagram showing an example of a prediction structure between layers.

[0060] A GOS contains one or more I-SPCs. Objects such as people, animals, cars, bicycles, traffic lights, and landmark buildings exist in three-dimensional space, and it is particularly effective to encode small objects as I-SPCs. For example, a three-dimensional data decoding device (hereinafter also referred to as a decoding device) decodes only the I-SPCs in the GOS when decoding the GOS with low processing load or at high speed.

[0061] The encoding device may also switch the encoding interval or frequency of occurrence of I-SPC depending on the density of objects in the WLD.

[0062] 3, the encoding device or decoding device encodes or decodes multiple layers in order from the lowest layer (layer 1). This allows, for example, an autonomous vehicle to prioritize data near the ground, which contains more information.

[0063] In addition, encoded data used by drones, etc. may be encoded or decoded in order from the SPC of the highest layer in the height direction within the GOS.

[0064] Alternatively, the encoding or decoding device may encode or decode multiple layers so that the decoding device can roughly grasp the GOS and gradually increase the resolution. For example, the encoding or decoding device may encode or decode layers 3, 8, 1, 9, etc. in that order.

[0065] Next, we will explain how to handle static and dynamic objects.

[0066] In a three-dimensional space, there exist static objects or scenes such as buildings or roads (hereinafter collectively referred to as static objects), and dynamic objects such as cars or people (hereinafter referred to as dynamic objects). Object detection is performed separately by extracting feature points from point cloud data or camera images such as a stereo camera. Here, an example of a method for encoding dynamic objects will be described.

[0067] The first method is to encode static objects without distinguishing between static and dynamic objects, and the second method is to distinguish between static and dynamic objects using identification information.

[0068] For example, GOS is used as the identification unit. In this case, GOS including SPCs that constitute static objects and GOS including SPCs that constitute dynamic objects are distinguished by identification information stored within the coded data or separately from the coded data.

[0069] Alternatively, the SPC may be used as the identification unit, in which case the SPC including the VLM that constitutes a static object and the SPC including the VLM that constitutes a dynamic object are distinguished by the above-mentioned identification information.

[0070] Alternatively, the VLM or VXL may be used as the identification unit, in which case the VLM or VXL containing static objects and the VLM or VXL containing dynamic objects are distinguished by the above-mentioned identification information.

[0071] The encoding device may also encode a dynamic object as one or more VLMs or SPCs, and encode a VLM or SPC containing a static object and an SPC containing a dynamic object as different GOSs. If the size of the GOS varies depending on the size of the dynamic object, the encoding device stores the size of the GOS separately as meta information.

[0072] The encoding device may also encode static objects and dynamic objects independently of each other, and overlay the dynamic objects on a world made up of static objects. In this case, the dynamic object is made up of one or more SPCs, and each SPC corresponds to one or more SPCs that make up the static object on which the SPC is overlaid. Note that the dynamic object may be represented by one or more VLMs or VXLs instead of SPCs.

[0073] The encoding device may also encode static objects and dynamic objects as different streams.

[0074] The encoding device may also generate a GOS that includes one or more SPCs that make up a dynamic object. Furthermore, the encoding device may set the GOS (GOS_M) that includes the dynamic object and the GOS of the static object that corresponds to the spatial region of GOS_M to the same size (occupy the same spatial region). This allows superimposition processing to be performed on a GOS-by-GOS basis.

[0075] A P-SPC or B-SPC that configures a dynamic object may refer to an SPC included in a different GOS that has already been coded. In cases where the position of a dynamic object changes over time and the same dynamic object is coded as a GOS at different times, referencing across GOSs is effective from the viewpoint of compression ratio.

[0076] The encoding device may switch between the first and second methods depending on the intended use of the encoded data. For example, when the encoded three-dimensional data is used as a map, it is desirable to be able to separate dynamic objects, so the encoding device uses the second method. On the other hand, when encoding three-dimensional data of an event such as a concert or sporting event, the encoding device uses the first method if there is no need to separate dynamic objects.

[0077] The decode time and display time of a GOS or SPC can be stored in the coded data or as meta information. The time information of all static objects may be the same. In this case, the actual decode time and display time may be determined by the decoding device. Alternatively, a different value may be assigned as the decode time for each GOS or SPC, and the same value may be assigned as the display time for all. Furthermore, a model may be introduced that ensures that the decoder has a buffer of a predetermined size and can decode without failure if it reads a bitstream at a predetermined bit rate according to the decode time, as in a decoder model used in video coding, such as the HEVC HRD (Hypothetical Reference Decoder).

[0078] Next, we will explain the arrangement of GOS within a world. The coordinates of the three-dimensional space in a world are expressed by three mutually orthogonal coordinate axes (x-axis, y-axis, and z-axis). By establishing a predetermined rule for the encoding order of GOS, encoding can be performed so that spatially adjacent GOS are continuous within the encoded data. For example, in the example shown in Figure 4, GOS within the xz plane are encoded continuously. After encoding of all GOS within a certain xz plane is completed, the value of the y-axis is updated. In other words, as encoding progresses, the world extends in the y-axis direction. Furthermore, the index numbers of GOS are set in the encoding order.

[0079] Here, the three-dimensional space of the world is associated one-to-one with absolute geographical coordinates such as GPS or latitude and longitude. Alternatively, the three-dimensional space may be expressed by relative positions from a preset reference position. The directions of the x-, y-, and z-axes of the three-dimensional space are expressed as direction vectors determined based on the latitude and longitude, and the direction vectors are stored as meta information together with the encoded data.

[0080] The size of the GOS is fixed, and the encoding device stores the size as meta information. The size of the GOS may be changed depending on, for example, whether the location is an urban area or whether the location is indoors or outdoors. That is, the size of the GOS may be changed depending on the quantity or nature of objects that have information value. Alternatively, the encoding device may adaptively change the size of the GOS or the spacing between I-SPCs within the GOS depending on, for example, the density of objects within the same world. For example, the higher the object density, the smaller the GOS size and the shorter the spacing between I-SPCs within the GOS.

[0081] In the example shown in Figure 5, the third to tenth GOS regions have a high density of objects, so the GOS are subdivided to allow finer granularity for random access. Note that the seventh to tenth GOS regions are located behind the third to sixth GOS regions, respectively.

[0082] Next, the configuration and operation flow of the three-dimensional data encoding device according to this embodiment will be described. Fig. 6 is a block diagram of the three-dimensional data encoding device 100 according to this embodiment. Fig. 7 is a flowchart showing an example of the operation of the three-dimensional data encoding device 100.

[0083] 6 generates encoded three-dimensional data 112 by encoding three-dimensional data 111. This 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.

[0084] As shown in FIG. 7, first, the acquisition unit 101 acquires three-dimensional data 111, which is point cloud data (S101).

[0085] Next, the coding area determination unit 102 determines an area to be coded from the spatial area corresponding to the acquired point cloud data (S102). For example, depending on the position of the user or vehicle, the coding area determination unit 102 determines a spatial area around the position as the area to be coded.

[0086] Next, the dividing unit 103 divides the point cloud data included in the region to be coded into processing units. Here, the processing units are the above-mentioned GOS and SPC, etc. Furthermore, this region to be coded corresponds to, for example, the above-mentioned world. Specifically, the dividing unit 103 divides the point cloud data into processing units based on the size of a preset GOS or the presence or size of a dynamic object (S103). Furthermore, the dividing unit 103 determines the start position of the SPC that is the first in coding order in each GOS.

[0087] Next, the encoding unit 104 generates encoded three-dimensional data 112 by sequentially encoding the plurality of SPCs in each GOS (S104).

[0088] Although an example has been shown in which the area to be coded is divided into GOSs and SPCs and then each GOS is coded, the processing procedure is not limited to the above. For example, a procedure may be used in which the configuration of one GOS is determined, the GOS is coded, and then the configuration of the next GOS is determined.

[0089] In this way, the three-dimensional data encoding device 100 generates encoded three-dimensional data 112 by encoding three-dimensional data 111. Specifically, the three-dimensional data encoding device 100 divides the three-dimensional data into first processing units (GOS), which are random access units and each correspond to a three-dimensional coordinate, 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). Furthermore, the third processing units (VLM) include one or more voxels (VXL), which are the smallest units to which position information can be associated.

[0090] 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). Furthermore, the three-dimensional data encoding device 100 encodes each of the plurality of third processing units (VLM) in each second processing unit (SPC).

[0091] For example, when the first processing unit (GOS) to be processed is a closed GOS, the three-dimensional data encoding device 100 encodes the second processing unit (SPC) to be processed included in the first processing unit (GOS) to be processed by referring to other second processing units (SPC) included in the first processing unit (GOS) to be processed. In other words, the three-dimensional data encoding device 100 does not refer to second processing units (SPC) included in first processing units (GOS) different from the first processing unit (GOS) to be processed.

[0092] On the other hand, if the first processing unit (GOS) to be processed is an open GOS, the second processing unit (SPC) to be processed included in the first processing unit (GOS) to be processed is encoded by referring to other second processing units (SPC) included in the first processing unit (GOS) to be processed, or second processing units (SPC) included in a first processing unit (GOS) different from the first processing unit (GOS) to be processed.

[0093] In addition, the three-dimensional data encoding device 100 selects, as the type of the second processing unit (SPC) to be processed, one of the following: a first type (I-SPC) that does not reference other second processing units (SPCs), a second type (P-SPC) that references one other second processing unit (SPC), or a third type that references two other second processing units (SPCs), and encodes the second processing unit (SPC) to be processed according to the selected type.

[0094] Next, the configuration and operation flow of the three-dimensional data decoding device according to this embodiment will be described. Fig. 8 is a block diagram of the blocks of the three-dimensional data decoding device 200 according to this embodiment. Fig. 9 is a flowchart showing an example of the operation of the three-dimensional data decoding device 200.

[0095] 8 generates decoded three-dimensional data 212 by decoding 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. This 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.

[0096] First, the acquisition unit 201 acquires the encoded 3D data 211 (S201). Next, the decoding start GOS determination unit 202 determines a GOS to be decoded (S202). Specifically, the decoding start GOS determination unit 202 refers to meta information stored in the encoded 3D data 211 or separately from the encoded 3D data, and determines a GOS including an SPC corresponding to a spatial position, object, or time at which decoding starts as the GOS to be decoded.

[0097] Next, the decoding SPC determination unit 203 determines the type (I, P, B) of SPC to be decoded in the GOS (S203). For example, the decoding SPC determination unit 203 determines whether to (1) decode only I-SPC, (2) decode I-SPC and P-SPC, or (3) decode all types. Note that if the type of SPC to be decoded has been determined in advance, such as when all SPCs are to be decoded, this step may not be performed.

[0098] Next, the decoding unit 204 acquires the address position in the encoded 3D data 211 where the first SPC in the GOS in decoding order (the same as the encoding order) starts, acquires the encoded data of the first SPC from the address position, and sequentially decodes each SPC in order starting from the first SPC (S204). Note that the address position is stored in meta information or the like.

[0099] 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 units (GOS) by decoding each of the encoded three-dimensional data 211 of the first processing units (GOS), which are random access units and each of which is associated with a three-dimensional coordinate. More specifically, the three-dimensional data decoding device 200 decodes each of the plurality of second processing units (SPC) in each first processing unit (GOS). Furthermore, the three-dimensional data decoding device 200 decodes each of the plurality of third processing units (VLM) in each second processing unit (SPC).

[0100] The following describes the meta information for random access. This meta information is generated by the three-dimensional data encoding device 100 and is included in the encoded three-dimensional data 112 (211).

[0101] In conventional random access for 2D video, decoding starts from the first frame of the random access unit that is close to the specified time. On the other hand, in the world, random access to space (coordinates, objects, etc.) is assumed in addition to time.

[0102] Therefore, to realize random access to at least three elements, coordinates, objects, and time, a table is prepared that associates each element with a GOS index number. Furthermore, the GOS index number is associated with the address of the I-SPC at the beginning of the GOS. Figure 10 shows an example of a table included in the meta information. Note that it is not necessary to use all of the tables shown in Figure 10; it is sufficient to use at least one table.

[0103] Hereinafter, as an example, random access starting from a coordinate will be described. When accessing coordinates (x2, y2, z2), first, the coordinate-GOS table is referenced and it is found that the point with coordinates (x2, y2, z2) is included in the second GOS. Next, the GOS address table is referenced and it is found that the address of the first I-SPC in the second GOS is addr(2). Therefore, the decoding unit 204 obtains data from this address and starts decoding.

[0104] The address may be an address in a logical format or a physical address on a hard disk drive or in memory. Alternatively, information identifying a file segment may be used instead of the address. For example, a file segment is a segmented unit of one or more GOSs.

[0105] Furthermore, if an object spans multiple GOSs, the object-GOS table may indicate multiple GOSs to which the object belongs. If the multiple GOSs are closed GOSs, the encoding device and decoding device can encode or decode in parallel. On the other hand, if the multiple GOSs are open GOSs, the multiple GOSs can reference each other, thereby improving compression efficiency.

[0106] Examples of objects include people, animals, cars, bicycles, traffic lights, landmark buildings, etc. For example, when encoding a world, the three-dimensional data encoding device 100 can extract feature points specific to objects from a three-dimensional point cloud or the like, detect objects based on the feature points, and set the detected objects as random access points.

[0107] In this way, the three-dimensional data encoding device 100 generates first information indicating a plurality of first processing units (GOS) and three-dimensional coordinates associated with each of the plurality of first processing units (GOS). The encoded three-dimensional data 112 (211) includes this first information. The first information further indicates at least one of an object, a time, and a data storage destination associated with each of the plurality of first processing units (GOS).

[0108] The three-dimensional data decoding device 200 acquires first information from the encoded three-dimensional data 211, and uses the first information to identify 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.

[0109] Other examples of meta information will be described below. In addition to the meta information for random access, the three-dimensional data encoding device 100 may generate and store the following meta information. Furthermore, the three-dimensional data decoding device 200 may use this meta information during decoding.

[0110] When using three-dimensional data as map information, a profile may be defined depending on the application, and information indicating the profile may be included in the meta information. For example, profiles for urban areas, suburban areas, or flying objects may be defined, and the maximum or minimum size of the world, SPC, or VLM may be defined for each. For example, for urban areas, more detailed information is required than for suburban areas, so the minimum size of the VLM is set smaller.

[0111] The meta information may include a tag value indicating the type of object. This tag value is associated with the VLM, SPC, or GOS that constitutes the object. For example, a tag value may be set for each type of object, such as a tag value of "0" indicating a "person," a tag value of "1" indicating a "car," and a tag value of "2" indicating a "traffic light." Alternatively, if the type of object is difficult to determine or does not need to be determined, a tag value indicating a property such as size or whether the object is dynamic or static may be used.

[0112] The meta information may also include information indicating the range of the spatial region occupied by the world.

[0113] The meta information may also store the size of the SPC or VXL as header information common to a plurality of SPCs, such as the entire stream of coded data or an SPC in a GOS.

[0114] The meta information may also include identification information for the range sensor or camera used to generate the point cloud, or information indicating the positional accuracy of the points in the point cloud.

[0115] The meta information may also include information indicating whether the world is made up of only static objects or whether it also includes dynamic objects.

[0116] A modification of this embodiment will now be described.

[0117] The encoding device or decoding device may encode or decode two or more different SPCs or GOSs in parallel. The GOSs to be encoded or decoded in parallel can be determined based on meta-information indicating the spatial positions of the GOSs.

[0118] In cases where three-dimensional data is used as a spatial map for vehicles or flying objects moving around, or where such a spatial map is to be generated, the encoding device or decoding device may encode or decode a GOS or SPC contained in a space identified based on GPS, route information, zoom magnification, etc.

[0119] Furthermore, the decoding device may perform decoding in order from the space closest to the current location or the travel route. The encoding device or decoding device may encode or decode a space farther from the current location or the travel route by lowering the priority compared to a closer space. Here, lowering the priority means lowering the processing order, lowering the resolution (thinning out the data before processing), or lowering the image quality (increasing the encoding efficiency, for example, by increasing the quantization step), etc.

[0120] Furthermore, when decoding coded data that has been coded hierarchically in space, the decoding device may decode only the lower layers.

[0121] The decoding device may also decode data preferentially from the lowest layer depending on the zoom factor or purpose of the map.

[0122] In addition, for applications such as self-position estimation or object recognition performed when a car or robot is driving autonomously, the encoding device or decoding device may encode or decode with reduced resolution except for areas within a specific height from the road surface (area where recognition is performed).

[0123] The encoding device may also encode point clouds representing indoor and outdoor spatial shapes separately. For example, by separating the GOS representing the indoor space (indoor GOS) from the GOS representing the outdoor space (outdoor GOS), the decoding device can select the GOS to decode depending on the viewpoint position when using the encoded data.

[0124] The encoding device may also encode indoor and outdoor GOS with nearby coordinates so that they are adjacent in the encoded stream. For example, the encoding device may associate identifiers for the two and store information indicating the associated identifiers in the encoded stream or in separately stored meta information. This allows the decoding device to identify indoor and outdoor GOS with nearby coordinates by referring to the information in the meta information.

[0125] The encoding device may also switch the size of the GOS or SPC between indoor and outdoor GOS. For example, the encoding device may set a smaller GOS size indoors than outdoors. The encoding device may also change the accuracy of extracting feature points from the point cloud or the accuracy of object detection between indoor and outdoor GOS.

[0126] The encoding device may also add information to the encoded data that enables the decoding device to distinguish dynamic objects from static objects. This allows the decoding device to display dynamic objects together with red frames or explanatory text. The decoding device may also display only red frames or explanatory text instead of dynamic objects. The decoding device may also display more specific object types. For example, a red frame may be used for cars and a yellow frame for people.

[0127] Furthermore, the encoding device or decoding device may determine whether to encode or decode dynamic objects and static objects as different SPCs or GOSs depending on the frequency of appearance of dynamic objects, the ratio of static objects to dynamic objects, etc. For example, if the frequency of appearance or ratio of dynamic objects exceeds a threshold, an SPC or GOS in which dynamic objects and static objects are mixed is permitted, and if the frequency of appearance or ratio of dynamic objects does not exceed the threshold, an SPC or GOS in which dynamic objects and static objects are mixed is not permitted.

[0128] When detecting dynamic objects from two-dimensional camera image information rather than from a point cloud, the encoding device may separately acquire information for identifying the detection result (such as a frame or text) and the object position, and encode this information as part of the three-dimensional encoded data. In this case, the decoding device displays auxiliary information (such as a frame or text) indicating the dynamic object by superimposing it on the decoding result of the static object.

[0129] The encoding device may also change the density of the VXL or VLM in the SPC depending on factors such as the complexity of the shape of the static object. For example, the encoding device may set the VXL or VLM to a higher density as the shape of the static object becomes more complex. Furthermore, the encoding device may determine the quantization step, etc., used when quantizing spatial position or color information depending on the density of the VXL or VLM. For example, the encoding device may set a smaller quantization step as the VXL or VLM becomes denser.

[0130] As described above, the encoding device or decoding device according to this embodiment encodes or decodes space in units of spaces each having coordinate information.

[0131] Furthermore, the encoding device and the decoding device perform encoding and decoding in units of volumes within a space. A volume includes voxels, which are the smallest units to which position information can be associated.

[0132] The encoding device and decoding device perform encoding or decoding by associating any elements using a table that associates each element of spatial information, including coordinates, objects, and time, with a GOP, or a table that associates each element with another element. The decoding device determines coordinates using the value of a selected element, identifies a volume, voxel, or space from the coordinates, and decodes the space including the volume or voxel, or the identified space.

[0133] The encoding device also determines a volume, voxel, or space that can be selected by an element through feature point extraction or object recognition, and encodes it as a randomly accessible volume, voxel, or space.

[0134] Spaces are classified into three types: I-SPC, which can be encoded or decoded by itself; P-SPC, which is encoded or decoded by referring to any one processed space; and B-SPC, which is encoded or decoded by referring to any two processed spaces.

[0135] One or more volumes correspond to static or dynamic objects. The space containing the static objects and the space containing the dynamic objects are coded or decoded as different GOSs. That is, the SPC containing the static objects and the SPC containing the dynamic objects are assigned to different GOSs.

[0136] Dynamic objects are encoded or decoded on an object-by-object basis and associated with one or more spaces containing static objects, i.e., multiple dynamic objects are encoded individually, and the resulting encoded data for the multiple dynamic objects is associated with the SPC containing the static objects.

[0137] The encoding device and the decoding device perform encoding or decoding by increasing the priority of the I-SPC in the GOS. For example, the encoding device performs encoding so as to minimize degradation of the I-SPC (so that the original 3D data is reproduced more faithfully after decoding). Also, the decoding device decodes only the I-SPC, for example.

[0138] The encoding device may perform encoding by changing the frequency of using I-SPCs depending on the density or number (quantity) of objects in the world. In other words, the encoding device changes the frequency of selecting I-SPCs depending on the number or density of objects included in the three-dimensional data. For example, the encoding device may use I-spaces more frequently as the density of objects in the world increases.

[0139] Furthermore, the encoding device sets random access points in units of GOS, and stores information indicating the spatial region corresponding to the GOS in the header information.

[0140] The encoding device uses, for example, a default value as the spatial size of the GOS. Note that the encoding device may change the size of the GOS depending on the number (quantity) or density of objects or dynamic objects. For example, the encoding device reduces the spatial size of the GOS as the density or number of objects or dynamic objects increases.

[0141] The space or volume also includes a set of feature points derived using information obtained by sensors such as a depth sensor, a gyroscope, or a camera. The coordinates of the feature points are set at the center positions of the voxels. Furthermore, by subdividing the voxels, it is possible to achieve high accuracy of the position information.

[0142] The feature point group is derived using multiple pictures, each of which has at least two types of time information: actual time information and time information that is the same for multiple pictures associated with the space (e.g., encoding time used for rate control, etc.).

[0143] Also, encoding or decoding is performed in units of GOS, each GOS including one or more spaces.

[0144] The encoding device and the decoding device refer to the spaces in the processed GOS to predict the P space or the B space in the GOS to be processed.

[0145] Alternatively, the encoding device and the decoding device do not refer to a different GOS, but predict the P space or the B space in the GOS to be processed using the processed space in the GOS to be processed.

[0146] Furthermore, the encoding device and the decoding device transmit or receive the encoded stream in units of worlds each including one or more GOSs.

[0147] Furthermore, the GOS has a layer structure in at least one direction within a world, and the encoding device and decoding device encode or decode from the lower layer. For example, a randomly accessible GOS belongs to the lowest layer. A GOS belonging to a higher layer references a GOS belonging to the same layer or lower. In other words, the GOS is spatially divided in a predetermined direction and includes multiple layers, each containing one or more SPCs. The encoding device and decoding device encode or decode each SPC by referring to an SPC included in the same layer as the SPC or in a layer lower than the SPC.

[0148] Furthermore, the encoding device and the decoding device encode or decode consecutive GOSs within a world unit including multiple GOSs. The encoding device and the decoding device write or read information indicating the encoding or decoding order (direction) as metadata. In other words, the encoded data includes information indicating the encoding order of multiple GOSs.

[0149] Furthermore, the encoding device and the decoding device encode or decode two or more different spaces or GOSs in parallel.

[0150] The encoding device and decoding device also encode and decode spatial information (coordinates, size, etc.) of the space or GOS.

[0151] Furthermore, the encoding device and decoding device encode or decode a space or GOS included in a specific space that is specified based on external information related to its own position and / or area size, such as GPS, route information, or magnification.

[0152] The encoding device or decoding device encodes or decodes spaces farther from its own position with lower priority than spaces closer to its own position.

[0153] The encoding device sets one direction of the world according to the magnification or use, and encodes the GOS having a layer structure in that direction. The decoding device decodes the GOS having a layer structure in one direction of the world set according to the magnification or use, preferentially from the lower layer.

[0154] The encoding device varies the feature point extraction, object recognition accuracy, spatial region size, etc., included in the indoor and outdoor spaces. However, the encoding device and decoding device encode or decode the indoor GOS and outdoor GOS that are close in coordinates as adjacent in the world, and also associate their identifiers and encode or decode them.

[0155] (Embodiment 2) When using encoded point cloud data in an actual device or service, it is desirable to transmit and receive the information required for the application in order to reduce network bandwidth. However, until now, such a function has not existed in the encoding structure of 3D data, and no encoding method for this purpose has existed.

[0156] In this embodiment, we will describe a three-dimensional data encoding method and a three-dimensional data encoding device that provide the function of transmitting and receiving only the information necessary for the purpose in encoded data of a three-dimensional point cloud, as well as a three-dimensional data decoding method and a three-dimensional data decoding device that decodes the encoded data.

[0157] A voxel (VXL) having a certain amount of features or more is defined as a feature voxel (FVXL), and a world (WLD) composed of FVXL is defined as a sparse world (SWLD). Figure 11 shows an example of the configuration of a sparse world and a world. The SWLD includes FGOS, which is a GOS composed of FVXL, FSPC, which is an SPC composed of FVXL, and FVLM, which is a VLM composed of FVXL. The data structures and prediction structures of FGOS, FSPC, and FVLM may be the same as those of GOS, SPC, and VLM.

[0158] The feature is a feature that expresses three-dimensional position information of the VXL or visible light information of the VXL position, and is a feature that is often detected especially at corners and edges of three-dimensional objects. Specifically, this feature is a three-dimensional feature or visible light feature as described below, but any feature that expresses the position, brightness, color information, etc. of the VXL may be used.

[0159] As the three-dimensional feature, SHOT feature (Signature of Histograms of OrienTations), PFH feature (Point Feature Histograms), or PPF feature (Point Pair Feature) is used.

[0160] The SHOT feature is obtained by dividing the area around the VXL, calculating the dot product between the reference point and the normal vector of each divided area, and creating a histogram. This SHOT feature has the advantage of being highly dimensional and expressive.

[0161] The PFH feature is obtained by selecting many pairs of points near the VXL, calculating normal vectors from those two points, and creating a histogram. Because the PFH feature is a histogram feature, it is robust against some disturbances and has high expressive power.

[0162] The PPF feature is calculated using normal vectors etc. for each of two VXL points. Since all VXLs are used for this PPF feature, it is robust against occlusion.

[0163] Furthermore, as the feature amount of visible light, SIFT (Scale-Invariant Feature Transform), SURF (Speeded Up Robust Features), HOG (Histogram of Oriented Gradients), or the like, which use information such as brightness gradient information of an image, can be used.

[0164] The SWLD is generated by calculating the above feature values ​​from each VXL of the WLD and extracting the FVXL. Here, the SWLD may be updated every time the WLD is updated, or may be updated periodically after a certain period of time has elapsed, regardless of the timing of updating the WLD.

[0165] A SWLD may be generated for each feature. For example, a separate SWLD may be generated for each feature, such as SWLD1 based on SHOT features and SWLD2 based on SIFT features, and different SWLDs may be used depending on the application. Furthermore, the calculated features of each FVXL may be stored in each FVXL as feature information.

[0166] Next, we explain how to use sparse world learning (SWLD). SWLDs contain only feature voxels (FVXL), so their data size is generally smaller than WLDs, which contain all VXL.

[0167] In applications that use features to achieve certain purposes, using SWLD information instead of WLD information can reduce the time required to read from the hard disk, as well as the bandwidth and transfer time required for network transfer. For example, by storing WLD and SWLD as map information on the server and switching between WLD and SWLD as the map information to be sent in response to a client request, the network bandwidth and transfer time can be reduced. A specific example is shown below.

[0168] 12 and 13 are diagrams illustrating examples of using SWLDs and WLDs. As shown in FIG. 12, when a client 1, which is an in-vehicle device, needs map information for determining its own location, the client 1 sends a request to the server to acquire map data for self-location estimation (S301). The server transmits an SWLD to the client 1 in response to the acquisition request (S302). The client 1 determines its own location using the received SWLD (S303). In this case, the client 1 acquires VXL information around the client 1 using various methods, such as a distance sensor such as a range finder, a stereo camera, or a combination of multiple monocular cameras, and estimates its own location information from the acquired VXL information and SWLD. Here, the self-location information includes the three-dimensional location information and orientation of the client 1.

[0169] 13, when a client 2, which is an in-vehicle device, needs map information for map drawing purposes such as a three-dimensional map, the client 2 sends a request to the server to acquire map data for map drawing (S311). The server transmits a WLD to the client 2 in response to the acquisition request (S312). The client 2 uses the received WLD to draw the map (S313). In this case, the client 2 creates a rendering image using, for example, an image captured by the client 2 using a visible light camera or the like and the WLD acquired from the server, and draws the created image on the screen of a car navigation system or the like.

[0170] As described above, the server sends SWLD to the client for applications that mainly require the feature values ​​of each VXL, such as self-location estimation, and sends WLD to the client for applications that require detailed VXL information, such as map drawing. This enables efficient transmission and reception of map data.

[0171] The client may decide for itself whether it needs a SWLD or a WLD and request the server to send either a SWLD or a WLD. The server may also decide whether to send a SWLD or a WLD depending on the client or network conditions.

[0172] Next, we will explain how to switch between sending and receiving data in the sparse world (SWLD) and the world (WLD).

[0173] Whether to receive a WLD or an SWLD may be switched depending on the network bandwidth. FIG. 14 shows an example of operation in this case. For example, when a low-speed network with limited available network bandwidth, such as an LTE (Long Term Evolution) environment, is used, the client accesses the server via the low-speed network (S321) and acquires an SWLD as map information from the server (S322). On the other hand, when a high-speed network with ample network bandwidth, such as a Wi-Fi (registered trademark) environment, is used, the client accesses the server via the high-speed network (S323) and acquires a WLD from the server (S324). This allows the client to acquire appropriate map information depending on the client's network bandwidth.

[0174] Specifically, the client receives SWLD via LTE when outdoors, and acquires WLD via Wi-Fi (registered trademark) when inside a facility, etc. This allows the client to obtain more detailed map information for the indoor area.

[0175] In this way, the client may request a WLD or SWLD from the server depending on the bandwidth of the network it uses. Alternatively, the client may send information indicating the bandwidth of the network it uses to the server, and the server may send data (WLD or SWLD) suitable for the client depending on the information. Alternatively, the server may determine the network bandwidth of the client and send data (WLD or SWLD) suitable for the client.

[0176] Moreover, whether to receive a WLD or a SWLD may be switched depending on the moving speed. FIG. 15 shows an example of operation in this case. For example, when the client is moving at high speed (S331), the client receives a SWLD from the server (S332). On the other hand, when the client is moving at low speed (S333), the client receives a WLD from the server (S334). This allows the client to acquire map information suited to the speed while suppressing network bandwidth. Specifically, by receiving a SWLD with a small amount of data while traveling on a highway, the client can update rough map information at an appropriate speed. On the other hand, by receiving a WLD while traveling on an ordinary road, the client can acquire more detailed map information.

[0177] In this way, the client may request a WLD or SWLD from the server according to its own moving speed. Alternatively, the client may send information indicating its own moving speed to the server, and the server may send data (WLD or SWLD) suitable for the client according to the information. Alternatively, the server may determine the moving speed of the client and send data (WLD or SWLD) suitable for the client.

[0178] Alternatively, the client may first obtain SWLD from the server and then obtain WLD for important areas within that. For example, when obtaining map data, the client may first obtain rough map information in SWLD, then narrow down the area to areas where features such as buildings, signs, or people frequently appear, and later obtain WLD for the narrowed down area. This allows the client to obtain detailed information for the required area while reducing the amount of data received from the server.

[0179] Alternatively, the server may create a separate SWLD for each object from the WLD, and the client may receive each one depending on the application. This reduces network bandwidth. For example, the server may recognize people or cars from the WLD in advance and create a SWLD for people and a SWLD for cars. The client may receive a SWLD for people if it wants to obtain information about people around it, or a SWLD for cars if it wants to obtain information about cars. The types of SWLDs may also be distinguished by information (such as a flag or type) added to the header, etc.

[0180] Next, the configuration and operation flow of a three-dimensional data encoding device (e.g., a server) according to this embodiment will be described. Fig. 16 is a block diagram of a three-dimensional data encoding device 400 according to this embodiment. Fig. 17 is a flowchart of three-dimensional data encoding processing by the three-dimensional data encoding device 400.

[0181] 16 encodes input three-dimensional data 411 to generate encoded three-dimensional data 413 and 414, which are encoded streams. Here, the encoded three-dimensional data 413 is encoded three-dimensional data corresponding to a WLD, and the encoded three-dimensional data 414 is encoded three-dimensional data corresponding to a SWLD. This three-dimensional data encoding device 400 includes an acquisition unit 401, a coding region determination unit 402, a SWLD extraction unit 403, a WLD encoding unit 404, and a SWLD encoding unit 405.

[0182] As shown in FIG. 17, first, the acquisition unit 401 acquires input three-dimensional data 411, which is point cloud data in a three-dimensional space (S401).

[0183] Next, the coding region determination unit 402 determines a spatial region to be coded based on the spatial region in which the point cloud data exists (S402).

[0184] Next, the SWLD extraction unit 403 defines the spatial region to be coded as a WLD and calculates a feature amount from each VXL included in the WLD.The SWLD extraction unit 403 then extracts VXLs whose feature amounts are equal to or greater than a predetermined threshold, defines the extracted VXLs as FVXLs, and adds the FVXLs to the SWLD to generate extracted three-dimensional data 412 (S403).In other words, extracted three-dimensional data 412 whose feature amounts are equal to or greater than the threshold are extracted from the input three-dimensional data 411.

[0185] Next, the WLD encoding unit 404 generates 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 adds information to the header of the encoded three-dimensional data 413 to distinguish that the encoded three-dimensional data 413 is a stream including a WLD.

[0186] Furthermore, the SWLD encoding unit 405 generates 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 adds information to the header of the encoded three-dimensional data 414 to distinguish that the encoded three-dimensional data 414 is a stream including an SWLD.

[0187] The order of the process for generating the encoded three-dimensional data 413 and the process for generating the encoded three-dimensional data 414 may be reversed. Also, some or all of these processes may be performed in parallel.

[0188] For example, a parameter called "world_type" is defined as information added to the headers of the encoded 3D data 413 and 414. world_type=0 indicates that the stream includes a WLD, and world_type=1 indicates that the stream includes a SWLD. If many other types are defined, the assigned numerical value may be increased, such as world_type=2. Furthermore, a specific flag may be included in one of the encoded 3D data 413 and 414. For example, a flag indicating that the stream includes a SWLD may be added to the encoded 3D data 414. In this case, the decoding device can determine whether the stream includes a WLD or a SWLD based on the presence or absence of the flag.

[0189] Furthermore, 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.

[0190] For example, in SWLD, data is thinned out, so that correlation with surrounding data may be lower than in WLD. Therefore, in the encoding method used in SWLD, inter prediction may be prioritized over intra prediction and inter prediction in comparison with the encoding method used in WLD.

[0191] Furthermore, the encoding method used for SWLD and the encoding method used for WLD may differ in the way three-dimensional positions are expressed. For example, in SWLD, the three-dimensional position of FVXL may be expressed by three-dimensional coordinates, and in WLD, the three-dimensional position may be expressed by an octree, which will be described later, or vice versa.

[0192] Furthermore, the SWLD encoding unit 405 performs encoding so 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. For example, as described above, there is a possibility that correlation between data in SWLD is lower than that in WLD. This may result in a decrease in encoding efficiency, 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, if the data size of the obtained encoded three-dimensional data 414 is larger than the data size of the encoded three-dimensional data 413 of WLD, the SWLD encoding unit 405 re-encodes the data to regenerate encoded three-dimensional data 414 with a reduced data size.

[0193] For example, the SWLD extraction unit 403 regenerates extracted three-dimensional data 412 with a reduced number of extracted feature points, and the SWLD encoding unit 405 encodes the extracted three-dimensional data 412. Alternatively, the degree of quantization in the SWLD encoding unit 405 may be made coarser. For example, in an octree structure described below, the degree of quantization can be made coarser by rounding the data in the lowest layer.

[0194] Furthermore, if the data size of the encoded three-dimensional data 414 of SWLD cannot be made smaller than the data size of the encoded three-dimensional data 413 of WLD, the SWLD encoding unit 405 may not generate the encoded three-dimensional data 414 of SWLD. Alternatively, the encoded three-dimensional data 413 of WLD may be copied to the encoded three-dimensional data 414 of SWLD. In other words, the encoded three-dimensional data 413 of WLD may be used as is as the encoded three-dimensional data 414 of SWLD.

[0195] Next, the configuration and operation flow of a three-dimensional data decoding device (e.g., a client) according to this embodiment will be described. Fig. 18 is a block diagram of a three-dimensional data decoding device 500 according to this embodiment. Fig. 19 is a flowchart of three-dimensional data decoding processing by the three-dimensional data decoding device 500.

[0196] 18 generates decoded three-dimensional data 512 or 513 by decoding 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.

[0197] This three-dimensional data decoding device 500 includes an acquisition unit 501 , a header analysis unit 502 , a WLD decoding unit 503 , and a SWLD decoding unit 504 .

[0198] 19, first, the acquisition unit 501 acquires encoded three-dimensional data 511 (S501). Next, the header analysis unit 502 analyzes the header of the encoded three-dimensional data 511 and determines whether the encoded three-dimensional data 511 is a stream including a WLD or a stream including a SWLD (S502). For example, the determination is made by referring to the world_type parameter described above.

[0199] If the encoded three-dimensional data 511 is a stream including a WLD (Yes in S503), the WLD decoding unit 503 decodes the encoded three-dimensional data 511 to generate decoded three-dimensional data 512 of the WLD (S504). On the other hand, if the encoded three-dimensional data 511 is a stream including an SWLD (No in S503), the SWLD decoding unit 504 decodes the encoded three-dimensional data 511 to generate decoded three-dimensional data 513 of the SWLD (S505).

[0200] Also, similarly to the encoding device, the decoding method used by the WLD decoding unit 503 when decoding the WLD may be different from the decoding method used by the SWLD decoding unit 504 when decoding the SWLD. For example, in the decoding method used for the SWLD, inter prediction, out of intra prediction and inter prediction, may be given priority over the decoding method used for the WLD.

[0201] Furthermore, the decoding method used for SWLD and the decoding method used for WLD may differ in the way of expressing three-dimensional positions. For example, in SWLD, the three-dimensional position of FVXL may be expressed by three-dimensional coordinates, and in WLD, the three-dimensional position may be expressed by an octree, which will be described later, or vice versa.

[0202] Next, we will explain the octree representation, which is a method of representing three-dimensional positions. VXL data included in three-dimensional data is converted into an octree structure and then encoded. Fig. 20 is a diagram showing an example of a VXL in a WLD. Fig. 21 is a diagram showing the octree structure of the WLD shown in Fig. 20. In the example shown in Fig. 20, there are three VXLs (hereinafter referred to as valid VXLs) VXL1 to VXL3 that contain point clouds. As shown in Fig. 21, the octree structure is composed of nodes and leaves. Each node has a maximum of eight nodes or leaves. Each leaf has VXL information. Here, among the leaves shown in Fig. 21, leaves 1, 2, and 3 represent VXL1, VXL2, and VXL3 shown in Fig. 20, respectively.

[0203] Specifically, each node and leaf corresponds to a three-dimensional position. Node 1 corresponds to the entire block shown in FIG. 20. The block corresponding to node 1 is divided into eight blocks, and of the eight blocks, the block containing a valid VXL is set as a node, and the other blocks are set as leaves. The block corresponding to the node is further divided into eight nodes or leaves, and this process is repeated for each level of the tree structure. In addition, all blocks in the lowest level are set as leaves.

[0204] FIG. 22 is a diagram showing an example of an SWLD generated from the WLD shown in FIG. 20. VXL1 and VXL2 shown in FIG. 20 are determined to be FVXL1 and FVXL2 as a result of feature extraction and are added to the SWLD. On the other hand, VXL3 is not determined to be FVXL and is not included in the SWLD. FIG. 23 is a diagram showing the octree structure of the SWLD shown in FIG. 22. In the octree structure shown in FIG. 23, leaf 3 corresponding to VXL3 shown in FIG. 21 is deleted. As a result, node 3 shown in FIG. 21 no longer has a valid VXL and is changed to a leaf. As such, the number of leaves in an SWLD is generally smaller than the number of leaves in a WLD, and the encoded 3D data of the SWLD is also smaller than the encoded 3D data of the WLD.

[0205] A modification of this embodiment will now be described.

[0206] For example, when a client such as an in-vehicle device estimates its own position, it receives a SWLD from a server and estimates its own position using the SWLD, and when detecting an obstacle, it may perform obstacle detection based on three-dimensional information of the surrounding area that it has acquired using various methods such as a distance sensor such as a range finder, a stereo camera, or a combination of multiple monocular cameras.

[0207] In addition, SWLDs generally do not contain VXL data for flat areas. Therefore, the server may store a subsampled world (subWLD) that is a subsample of the WLD for static obstacle detection, and transmit the SWLD and subWLD to the client. This allows the client to perform localization and obstacle detection while reducing network bandwidth.

[0208] Furthermore, when a client wants to quickly draw 3D map data, it may be more convenient for the map information to have a mesh structure. Therefore, the server may generate a mesh from the WLD and store it in advance as a mesh world (MWLD). For example, a client may receive an MWLD when it needs a coarse 3D drawing, and a WLD when it needs a detailed 3D drawing. This reduces network bandwidth.

[0209] Furthermore, although the server sets the VXLs among the VXLs whose feature quantities are equal to or greater than a threshold as FVXLs, FVXLs may be calculated using a different method. For example, the server may determine that the VXLs, VLMs, SPCs, or GOSs constituting a traffic light or intersection are necessary for self-localization, driving assistance, or autonomous driving, and include them in the SWLD as FVXLs, FVLMs, FSPCs, and FGOSs. This determination may also be performed manually. The FVXLs obtained by the above method may be added to the FVXLs set based on the feature quantities. That is, the SWLD extraction unit 403 may further extract data corresponding to objects having predetermined attributes from the input three-dimensional data 411 as extracted three-dimensional data 412.

[0210] Furthermore, the fact that it is necessary for such purposes may be labeled separately from the features. Furthermore, the server may separately store FVXL necessary for self-localization at traffic lights or intersections, driving assistance, autonomous driving, etc. as a higher layer (e.g., lane world) of SWLD.

[0211] The server may also add attributes to the VXL in the WLD for each random access unit or for each predetermined unit. The attributes include, for example, information indicating whether the VXL is necessary or unnecessary for self-location estimation, or information indicating whether the VXL is important as traffic information such as a traffic light or intersection. The attributes may also include a correspondence relationship with a feature (such as an intersection or road) in lane information (such as GDF: Geographic Data Files).

[0212] Furthermore, the following method may be used as a method for updating the WLD or SWLD.

[0213] Updates indicating changes in people, construction, or tree-lined streets (for trucks) are uploaded to the server as point clouds or metadata. The server updates the WLD based on the upload, and then updates the SWLD using the updated WLD.

[0214] In addition, if the client detects an inconsistency between the 3D information it generated during self-location estimation and the 3D information it received from the server, it may send the 3D information it generated to the server along 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 out of date.

[0215] Although information for distinguishing between WLD and SWLD is added to the header information of the coded stream, if there are multiple types of worlds, such as mesh worlds or lane worlds, information for distinguishing between them may be added to the header information. Also, if there are multiple SWLDs with different features, information for distinguishing between them may be added to the header information.

[0216] Furthermore, although the SWLD is described as being composed of FVXL, it may also include VXL that has not been determined to be FVXL. For example, the SWLD may include adjacent VXL that are used when calculating the feature of FVXL. This allows the client to calculate the feature of FVXL when receiving the SWLD, even if feature information is not added to each FVXL in the SWLD. In this case, the SWLD may include information for distinguishing whether each VXL is FVXL or VXL.

[0217] As described above, the three-dimensional data encoding device 400 extracts extracted three-dimensional data 412 (second three-dimensional data) whose feature amount is greater than or equal to a threshold value from input three-dimensional data 411 (first three-dimensional data), and generates encoded three-dimensional data 414 (first encoded three-dimensional data) by encoding the extracted three-dimensional data 412.

[0218] According to this, the three-dimensional data encoding device 400 generates encoded three-dimensional data 414 by encoding data whose feature amount is equal to or greater than a threshold. This allows the amount of data to be reduced compared to when the input three-dimensional data 411 is encoded as is. Therefore, the three-dimensional data encoding device 400 can reduce the amount of data to be transmitted.

[0219] Moreover, the three-dimensional data encoding device 400 further encodes the input three-dimensional data 411 to generate encoded three-dimensional data 413 (second encoded three-dimensional data).

[0220] This allows the three-dimensional data encoding device 400 to selectively transmit the encoded three-dimensional data 413 and the encoded three-dimensional data 414 depending on, for example, the intended use.

[0221] Furthermore, the extracted three-dimensional data 412 is coded by a first coding method, and the input three-dimensional data 411 is coded by a second coding method that is different from the first coding method.

[0222] This allows the three-dimensional data encoding device 400 to use encoding methods suited to the input three-dimensional data 411 and the extracted three-dimensional data 412, respectively.

[0223] Furthermore, in the first encoding method, of intra prediction and inter prediction, inter prediction is given priority over the second encoding method.

[0224] This allows the 3D data encoding device 400 to increase the priority of inter prediction for the extracted 3D data 412, which tends to have low correlation between adjacent data.

[0225] Furthermore, the first and second encoding methods differ in the way they represent three-dimensional positions: for example, the second encoding method represents three-dimensional positions using an octree, while the first encoding method represents three-dimensional positions using three-dimensional coordinates.

[0226] This allows the three-dimensional data encoding device 400 to use a more suitable three-dimensional position representation method for three-dimensional data with different numbers of data (number of VXLs or FVXLs).

[0227] Furthermore, at least one of the encoded three-dimensional data 413 and 414 includes an identifier indicating whether the encoded three-dimensional data is encoded three-dimensional data obtained by encoding the input three-dimensional data 411, or encoded three-dimensional data obtained by encoding a portion of the input three-dimensional data 411. In other words, the identifier indicates whether the encoded three-dimensional data is encoded three-dimensional data 413 of WLD or encoded three-dimensional data 414 of SWLD.

[0228] This allows the decoding device to easily determine whether the acquired encoded three-dimensional data is encoded three-dimensional data 413 or encoded three-dimensional data 414.

[0229] Furthermore, the three-dimensional data encoding device 400 encodes the extracted three-dimensional data 412 so that the amount of data of the encoded three-dimensional data 414 is smaller than the amount of data of the encoded three-dimensional data 413 .

[0230] According to this, the three-dimensional data encoding device 400 can make the data amount of the encoded three-dimensional data 414 smaller than the data amount of the encoded three-dimensional data 413 .

[0231] Furthermore, the three-dimensional data encoding device 400 further extracts data corresponding to an object having a predetermined attribute from the input three-dimensional data 411 as extracted three-dimensional data 412. For example, the object having the predetermined attribute is an object necessary for self-position estimation, driving assistance, automatic driving, or the like, such as a traffic light or an intersection.

[0232] This allows the three-dimensional data encoding device 400 to generate encoded three-dimensional data 414 that includes data required by the decoding device.

[0233] Furthermore, the three-dimensional data encoding device 400 (server) further transmits one of the encoded three-dimensional data 413 and 414 to the client depending on the state of the client.

[0234] This allows the three-dimensional data encoding device 400 to transmit appropriate data depending on the state of the client.

[0235] The state of the client also includes the communication status of the client (for example, the network bandwidth) or the movement speed of the client.

[0236] Furthermore, the three-dimensional data encoding device 400 further transmits one of the encoded three-dimensional data 413 and 414 to the client in response to a request from the client.

[0237] This allows the three-dimensional data encoding device 400 to transmit appropriate data in response to a client request.

[0238] Furthermore, the three-dimensional data decoding device 500 according to this embodiment decodes the encoded three-dimensional data 413 or 414 generated by the three-dimensional data encoding device 400 described above.

[0239] That is, the three-dimensional data decoding device 500 decodes, by a first decoding method, encoded three-dimensional data 414 obtained by encoding extracted three-dimensional data 412 whose feature amount extracted from input three-dimensional data 411 is equal to or greater than a threshold value. Also, the three-dimensional data decoding device 500 decodes, by a second decoding method different from the first decoding method, encoded three-dimensional data 413 obtained by encoding the input three-dimensional data 411.

[0240] According to this, the three-dimensional data decoding device 500 can selectively receive the encoded three-dimensional data 414, which is generated by encoding data whose feature amount is equal to or greater than a threshold, and the encoded three-dimensional data 413, depending on, for example, the intended use. This allows the three-dimensional data decoding device 500 to reduce the amount of data to be transmitted. Furthermore, the three-dimensional data decoding device 500 can use decoding methods suitable for the input three-dimensional data 411 and the extracted three-dimensional data 412, respectively.

[0241] Furthermore, in the first decoding method, of intra prediction and inter prediction, inter prediction is given priority over the second decoding method.

[0242] This allows the 3D data decoding device 500 to increase the priority of inter prediction for extracted 3D data in which the correlation between adjacent data is likely to be low.

[0243] Furthermore, the first and second decoding methods differ in the way they represent three-dimensional positions. For example, the second decoding method represents three-dimensional positions using an octree, while the first decoding method represents three-dimensional positions using three-dimensional coordinates.

[0244] This allows the three-dimensional data decoding device 500 to use a more suitable three-dimensional position representation method for three-dimensional data with different numbers of data (numbers of VXLs or FVXLs).

[0245] Furthermore, at least one of the encoded three-dimensional data 413 and 414 includes an identifier indicating whether the encoded three-dimensional data is encoded three-dimensional data obtained by encoding the input three-dimensional data 411, or encoded three-dimensional data obtained by encoding a portion of the input three-dimensional data 411. The three-dimensional data decoding device 500 identifies the encoded three-dimensional data 413 and 414 by referring to the identifier.

[0246] This allows the three-dimensional data decoding device 500 to easily determine whether the acquired encoded three-dimensional data is the encoded three-dimensional data 413 or the encoded three-dimensional data 414.

[0247] Furthermore, the three-dimensional data decoding device 500 notifies the server of the status of the client (three-dimensional data decoding device 500). The three-dimensional data decoding device 500 receives one of the encoded three-dimensional data 413 and 414 transmitted from the server depending on the status of the client.

[0248] This allows the three-dimensional data decoding device 500 to receive appropriate data depending on the state of the client.

[0249] The state of the client also includes the communication status of the client (for example, the network bandwidth) or the movement speed of the client.

[0250] Furthermore, 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 transmitted from the server in response to the request.

[0251] This allows the three-dimensional data decoding device 500 to receive appropriate data according to the application.

[0252] (Embodiment 3) 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 a vehicle and a surrounding vehicle.

[0253] 24 is a block diagram of a three-dimensional data creation device 620 according to this embodiment. This three-dimensional data creation device 620 is included in, for example, the vehicle itself, and creates denser third three-dimensional data 636 by combining received second three-dimensional data 635 with first three-dimensional data 632 created by the three-dimensional data creation device 620.

[0254] This three-dimensional data creation device 620 includes a three-dimensional data creation unit 621 , a requested range determination unit 622 , a search unit 623 , a reception unit 624 , a decoding unit 625 , and a synthesis unit 626 .

[0255] First, three-dimensional data creation unit 621 creates first three-dimensional data 632 using sensor information 631 detected by a sensor equipped in the vehicle. Next, required range determination unit 622 determines a required range, which is a three-dimensional spatial range for which data is insufficient in created first three-dimensional data 632.

[0256] Next, the search unit 623 searches for nearby vehicles that have three-dimensional data within the requested range, and transmits requested range information 633 indicating the requested range to the nearby vehicles identified by the search. Next, the receiving unit 624 receives encoded three-dimensional data 634, which is an encoded stream of the requested range, from the nearby vehicles (S624). Note that the search unit 623 may indiscriminately issue a request to all vehicles present within a specific range, and receive the encoded three-dimensional data 634 from those that respond. Furthermore, the search unit 623 may issue a request to objects other than vehicles, such as traffic lights or signs, and receive the encoded three-dimensional data 634 from the objects.

[0257] Next, the decoding unit 625 obtains second three-dimensional data 635 by decoding the received encoded three-dimensional data 634. Next, the combining unit 626 combines the first three-dimensional data 632 and the second three-dimensional data 635 to create denser third three-dimensional data 636.

[0258] Next, a description will be given of the configuration and operation of three-dimensional data transmission device 640 according to this embodiment.

[0259] The three-dimensional data transmission device 640 is included in, for example, the surrounding vehicle described above, processes the fifth three-dimensional data 652 created by the surrounding vehicle into sixth three-dimensional data 654 requested by the host vehicle, encodes the sixth three-dimensional data 654 to generate encoded three-dimensional data 634, and transmits the encoded three-dimensional data 634 to the host vehicle.

[0260] The three-dimensional data transmission device 640 includes a three-dimensional data creation unit 641 , a receiving unit 642 , an extracting unit 643 , an encoding unit 644 , and a transmitting unit 645 .

[0261] First, three-dimensional data creation unit 641 uses sensor information 651 detected by a sensor equipped in a surrounding vehicle to create fifth three-dimensional data 652. Next, receiving unit 642 receives requested range information 633 transmitted from the host vehicle.

[0262] Next, extraction unit 643 extracts three-dimensional data of the requested range indicated by requested range information 633 from fifth three-dimensional data 652, thereby processing fifth three-dimensional data 652 into sixth three-dimensional data 654. Next, encoding unit 644 encodes sixth three-dimensional data 654 to generate encoded three-dimensional data 634, which is an encoded stream. Then, transmission unit 645 transmits encoded three-dimensional data 634 to the host vehicle.

[0263] Here, we will explain an example in which the vehicle itself is equipped with a three-dimensional data creation device 620 and the surrounding vehicles are equipped with a three-dimensional data transmission device 640, but each vehicle may also have the functions of both the three-dimensional data creation device 620 and the three-dimensional data transmission device 640.

[0264] (Fourth embodiment) In this embodiment, an abnormal operation in self-location estimation based on a three-dimensional map will be described.

[0265] It is expected that applications such as self-driving cars, or autonomous movement of mobile objects such as robots, drones, and other flying objects will expand in the future. One example of a means to realize such autonomous movement is a method in which a mobile object estimates its own position within a three-dimensional map (self-location estimation) and travels according to the map.

[0266] Self-position estimation can be achieved by matching a three-dimensional map with three-dimensional information about the surroundings of the vehicle (hereinafter referred to as vehicle-detected three-dimensional data) obtained by sensors such as a rangefinder (such as LiDAR) or a stereo camera mounted on the vehicle, and estimating the vehicle's position within the three-dimensional map.

[0267] 3D maps, such as the HD maps proposed by HERE, may include not only 3D point clouds but also 2D map data such as road and intersection shape information, or real-time changing information such as traffic congestion and accidents. 3D maps are made up of multiple layers, including 3D data, 2D data, and real-time changing metadata, and devices can acquire or reference only the necessary data.

[0268] The point cloud data may be the SWLD described above, or may include point group data other than feature points. Furthermore, the transmission and reception of point cloud data is performed in units of one or more random accesses.

[0269] The following methods can be used to match the 3D map with the vehicle-detected 3D data. For example, the device compares the shapes of the point groups in each point cloud and determines that areas with high similarity between feature points are in the same location. Furthermore, if the 3D map is composed of SWLDs, the device performs matching by comparing the feature points that make up the SWLDs with the 3D feature points extracted from the vehicle-detected 3D data.

[0270] Here, to estimate the vehicle's position with high accuracy, (A) it is necessary to acquire a 3D map and 3D vehicle detection data, and (B) the accuracy of these must meet a predetermined standard. However, in the following abnormal cases, neither (A) nor (B) can be met.

[0271] (1) Three-dimensional maps cannot be obtained via communication.

[0272] (2) The 3D map does not exist, or the 3D map is obtained but is corrupted.

[0273] (3) The vehicle's sensor is out of order or the weather is bad, so the accuracy of the generated 3D data detected by the vehicle is insufficient.

[0274] The operation for dealing with these abnormal cases will be described below. Although the operation will be described below using a car as an example, the following method can be applied to any autonomously moving animal, such as a robot or a drone.

[0275] The configuration and operation of a three-dimensional information processing device according to this embodiment for dealing with abnormal cases in the three-dimensional map or the vehicle-detected three-dimensional data will be described below. Fig. 26 is a block diagram showing an example of the configuration of a three-dimensional information processing device 700 according to this embodiment.

[0276] 26 , the three-dimensional information processing device 700 is mounted on a moving object such as an automobile. The three-dimensional information processing device 700 includes a three-dimensional map acquisition unit 701, a host vehicle detection data acquisition unit 702, an abnormality case determination unit 703, a countermeasure operation determination unit 704, and an operation control unit 705.

[0277] The three-dimensional information processing device 700 may include a two-dimensional or one-dimensional sensor (not shown) for detecting structures or animals around the vehicle, such as a camera for acquiring two-dimensional images or a one-dimensional data sensor using ultrasound or a laser. The three-dimensional information processing device 700 may also include a communication unit (not shown) for acquiring the three-dimensional map via a mobile communication network such as 4G or 5G, or via vehicle-to-vehicle communication or road-to-vehicle communication.

[0278] The three-dimensional map acquisition unit 701 acquires a three-dimensional map 711 of the vicinity of the travel 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.

[0279] Next, the host vehicle detection data acquisition unit 702 acquires host vehicle detection three-dimensional data 712 based on the sensor information. For example, the host vehicle detection data acquisition unit 702 generates the host vehicle detection three-dimensional data 712 based on sensor information acquired by a sensor provided in the host vehicle.

[0280] Next, the abnormality case determination unit 703 detects an abnormality case by performing a predetermined check on at least one of the acquired three-dimensional map 711 and the host vehicle detected three-dimensional data 712. In other words, the abnormality case determination unit 703 determines whether at least one of the acquired three-dimensional map 711 and the host vehicle detected three-dimensional data 712 is abnormal.

[0281] When an abnormal case is detected, the countermeasure operation determination unit 704 determines the countermeasure operation for the abnormal case. Next, the operation control unit 705 controls the operation of each processing unit required to implement the countermeasure operation, such as the three-dimensional map acquisition unit 701.

[0282] On the other hand, if no abnormal case is detected, the three-dimensional information processing apparatus 700 ends the process.

[0283] Furthermore, the three-dimensional information processing device 700 uses the three-dimensional map 711 and the vehicle-detected three-dimensional data 712 to estimate the self-position of the vehicle having the three-dimensional information processing device 700. Next, the three-dimensional information processing device 700 automatically drives the vehicle using the result of the self-position estimation.

[0284] In this way, the three-dimensional information processing device 700 acquires map data (three-dimensional map 711) including first three-dimensional position information via a communication path. For example, the first three-dimensional position information is encoded in units of subspaces having three-dimensional coordinate information, and each is a collection of one or more subspaces, and includes multiple random access units that can be independently decoded. For example, the first three-dimensional position information is data (SWLD) in which feature points whose three-dimensional feature amounts are equal to or greater than a predetermined threshold are encoded.

[0285] Furthermore, the three-dimensional information processing device 700 generates second three-dimensional position information (subject vehicle-detected three-dimensional data 712) from the information detected by the sensor. Next, the three-dimensional information processing device 700 performs an abnormality determination process on the first three-dimensional position information or the second three-dimensional position information, thereby determining whether the first three-dimensional position information or the second three-dimensional position information is abnormal.

[0286] When the first three-dimensional position information or the second three-dimensional position information is determined to be abnormal, the three-dimensional information processing apparatus 700 determines a countermeasure action for the abnormality. Next, the three-dimensional information processing apparatus 700 performs control necessary for carrying out the countermeasure action.

[0287] This allows the three-dimensional information processing apparatus 700 to detect an abnormality in the first three-dimensional position information or the second three-dimensional position information and take appropriate action.

[0288] (Embodiment 5) In this embodiment, a method of transmitting three-dimensional data to a following vehicle will be described.

[0289] 27 is a block diagram showing an example of the configuration of a three-dimensional data creation device 810 according to this embodiment. This three-dimensional data creation device 810 is mounted on a vehicle, for example. The three-dimensional data creation device 810 transmits and receives three-dimensional data to and from an external traffic monitoring cloud, a leading vehicle, or a following vehicle, and also creates and stores three-dimensional data.

[0290] The three-dimensional data creation device 810 includes a data receiving unit 811, a communication unit 812, a reception control unit 813, a format conversion unit 814, multiple sensors 815, a three-dimensional data creation 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.

[0291] The data receiving unit 811 receives three-dimensional data 831 from a traffic monitoring cloud or a preceding vehicle. The three-dimensional data 831 includes, for example, information such as a point cloud, visible light image, depth information, sensor position information, or speed information, including areas that cannot be detected by the sensor 815 of the vehicle itself.

[0292] The communication unit 812 communicates with the traffic monitoring cloud or the vehicle ahead, and transmits data transmission requests and the like to the traffic monitoring cloud or the vehicle ahead.

[0293] The reception control unit 813 exchanges information such as compatible formats with the communication destination via the communication unit 812, and establishes communication with the communication destination.

[0294] The format conversion unit 814 generates three-dimensional data 832 by performing format conversion or the like on the three-dimensional data 831 received by the data receiving unit 811. Furthermore, if the three-dimensional data 831 is compressed or encoded, the format conversion unit 814 performs decompression or decoding processing.

[0295] The multiple sensors 815 are a group of sensors such as LiDAR, a visible light camera, or an infrared camera that acquire information about the outside of the vehicle, and generate sensor information 833. For example, if the sensor 815 is a laser sensor such as LiDAR, the sensor information 833 is three-dimensional data such as a point cloud (point cloud data). Note that the number of sensors 815 does not need to be multiple.

[0296] The three-dimensional data creation unit 816 generates three-dimensional data 834 from the sensor information 833. The three-dimensional data 834 includes information such as a point cloud, a visible light image, depth information, sensor position information, or velocity information.

[0297] The three-dimensional data synthesis unit 817 synthesizes three-dimensional data 834 created based on the host vehicle's sensor information 833 with three-dimensional data 832 created by the traffic monitoring cloud or a preceding vehicle, etc., to construct three-dimensional data 835 that includes the space ahead of the preceding vehicle that cannot be detected by the host vehicle's sensor 815.

[0298] The three-dimensional data storage unit 818 stores the generated three-dimensional data 835 and the like.

[0299] The communication unit 819 communicates with the traffic monitoring cloud or the following vehicle, and transmits data transmission requests and the like to the traffic monitoring cloud or the following vehicle.

[0300] The transmission control unit 820 exchanges information such as supported formats with the communication destination and establishes communication with the communication destination via the communication unit 819. Furthermore, the transmission control unit 820 determines a transmission region, which is 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 a data transmission request from the communication destination.

[0301] Specifically, in response to a data transmission request from the traffic monitoring cloud or a following vehicle, the transmission control unit 820 determines a transmission area that includes the space ahead of the vehicle that cannot be detected by the sensor of the following vehicle. The transmission control unit 820 also determines the transmission area by determining whether the transmittable space or the transmitted space has been updated based on the three-dimensional data construction information. For example, the transmission control unit 820 determines the area specified in the data transmission request and in which the corresponding three-dimensional data 835 exists as the transmission area. The transmission control unit 820 then notifies the format conversion unit 821 of the format supported by the communication destination and the transmission area.

[0302] The format conversion unit 821 converts three-dimensional data 836 of the transmission area, out of the three-dimensional data 835 stored in the three-dimensional data storage unit 818, into a format supported by the receiving side, thereby generating three-dimensional data 837. Note that the format conversion unit 821 may reduce the amount of data by compressing or encoding the three-dimensional data 837.

[0303] The data transmission unit 822 transmits three-dimensional data 837 to the traffic monitoring cloud or the following vehicle. This three-dimensional data 837 includes, for example, information such as a point cloud ahead of the vehicle, including blind spots of the following vehicle, visible light images, depth information, or sensor position information.

[0304] Although an example in which format conversion is performed by the format conversion units 814 and 821 has been described, format conversion does not necessarily have to be performed.

[0305] With this configuration, the three-dimensional data creation device 810 externally acquires three-dimensional data 831 of an area that cannot be detected by the sensor 815 of the host vehicle, and generates three-dimensional data 835 by combining the three-dimensional data 831 with three-dimensional data 834 based on sensor information 833 detected by the sensor 815 of the host vehicle. In this way, the three-dimensional data creation device 810 can generate three-dimensional data of an area that cannot be detected by the sensor 815 of the host vehicle.

[0306] In addition, in response to a data transmission request from a traffic monitoring cloud or a following vehicle, the three-dimensional data creation device 810 can transmit three-dimensional data including the space ahead of the vehicle that cannot be detected by the sensors of the following vehicle to the traffic monitoring cloud or the following vehicle, etc.

[0307] (Sixth embodiment) In the fifth embodiment, an example has been described in which a client device such as a vehicle transmits three-dimensional data to another vehicle or a server such as a traffic monitoring cloud. In this embodiment, the client device transmits sensor information obtained by a sensor to the server or another client device.

[0308] First, the configuration of a system according to this embodiment will be described. Fig. 28 is a diagram showing the configuration of a system for transmitting and receiving 3D maps and sensor information according to this embodiment. This system includes a server 901 and client devices 902A and 902B. When there is no need to distinguish between the client devices 902A and 902B, they will also be referred to as client device 902.

[0309] The client device 902 is, for example, an in-vehicle device mounted on a mobile object such as a vehicle. The server 901 is, for example, a traffic monitoring cloud or the like, and is capable of communicating with a plurality of client devices 902.

[0310] The server 901 transmits a three-dimensional map composed of a point cloud to the client device 902. Note that the composition of the three-dimensional map is not limited to a point cloud, and may represent other three-dimensional data such as a mesh structure.

[0311] The client device 902 transmits sensor information acquired by the client device 902 to the server 901. The sensor information includes, for example, at least one of LiDAR acquisition information, a visible light image, an infrared image, a depth image, sensor position information, and speed information.

[0312] Data transmitted between the server 901 and the client device 902 may be compressed to reduce data size, or may remain uncompressed to maintain data accuracy. When compressing data, a three-dimensional compression method based on an octree structure, for example, can be used for point clouds. Also, a two-dimensional image compression method can be used for visible light images, infrared images, and depth images. Examples of two-dimensional image compression methods include MPEG-4 AVC or HEVC standardized by MPEG.

[0313] Furthermore, the server 901 transmits the three-dimensional map managed by the server 901 to the client device 902 in response to a three-dimensional map transmission request from the client device 902. Note that the server 901 may transmit the three-dimensional map without waiting for a three-dimensional map transmission request from the client device 902. For example, the server 901 may broadcast the three-dimensional map to one or more client devices 902 that are in a predetermined space. Furthermore, the server 901 may transmit a three-dimensional map appropriate for the position of the client device 902 to the client device 902 that has received a transmission request once, at regular intervals. Furthermore, the server 901 may transmit the three-dimensional map to the client device 902 every time the three-dimensional map managed by the server 901 is updated.

[0314] The client device 902 issues a request to send a three-dimensional map to the server 901. For example, when the client device 902 wants to estimate its own position while driving, the client device 902 sends a request to send a three-dimensional map to the server 901.

[0315] In the following cases, the client device 902 may issue a request to the server 901 to transmit a three-dimensional map. If the three-dimensional map held by the client device 902 is old, the client device 902 may issue a request to the server 901 to transmit a three-dimensional map. For example, if a certain period of time has passed since the client device 902 obtained the three-dimensional map, the client device 902 may issue a request to the server 901 to transmit a three-dimensional map.

[0316] The client device 902 may issue a request to the server 901 to transmit a three-dimensional map a certain time before the client device 902 leaves the space shown in the three-dimensional map held by the client device 902. For example, when the client device 902 is located within a predetermined distance from the boundary of the space shown in the three-dimensional map held by the client device 902, the client device 902 may issue a request to the server 901 to transmit a three-dimensional map. Furthermore, when the movement route and movement speed of the client device 902 are known, the time when the client device 902 will leave the space shown in the three-dimensional map held by the client device 902 may be predicted based on these.

[0317] If the error in aligning the three-dimensional data created by the client device 902 from sensor information with the three-dimensional map is equal to or greater than a certain level, the client device 902 may issue a request to the server 901 to send the three-dimensional map.

[0318] The client device 902 transmits sensor information to the server 901 in response to a request to transmit sensor information transmitted from the server 901. Note that the client device 902 may transmit sensor information to the server 901 without waiting for a request to transmit sensor information from the server 901. For example, once the client device 902 receives a request to transmit sensor information from the server 901, the client device 902 may periodically transmit the sensor information to the server 901 for a certain period of time. Furthermore, if the error in aligning the three-dimensional data created by the client device 902 based on the sensor information with the three-dimensional map obtained from the server 901 is equal to or greater than a certain level, the client device 902 may determine that a change may have occurred in the three-dimensional map around the client device 902, and may transmit this information along with the sensor information to the server 901.

[0319] The server 901 issues a request to transmit 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 little information in the three-dimensional map managed by the server 901, the server 901 issues a request to transmit sensor information to the client device 902 in order to generate a new three-dimensional map. The server 901 may also issue a request to transmit sensor information when it wants to update the three-dimensional map, when it wants to check road conditions during snowfall or a disaster, when it wants to check traffic congestion, or when it wants to check the status of incidents and accidents, etc.

[0320] Furthermore, the client device 902 may set the amount of data of the sensor information to be transmitted to the server 901 depending on the communication state or bandwidth at the time of receiving a request to transmit the sensor information from the server 901. Setting the amount of data of the sensor information to be transmitted to the server 901 means, for example, increasing or decreasing the amount of the data itself or appropriately selecting a compression method.

[0321] 29 is a block diagram showing an example configuration of a client device 902. The client device 902 receives a three-dimensional map composed of a point cloud or the like from the server 901, and estimates the self-position of the client device 902 from three-dimensional data created based on sensor information of the client device 902. The client device 902 also transmits the acquired sensor information to the server 901.

[0322] The client device 902 includes a data receiving unit 1011, a communication unit 1012, a reception control unit 1013, a format conversion unit 1014, multiple 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.

[0323] The data receiving unit 1011 receives a three-dimensional map 1031 from the server 901. The three-dimensional map 1031 is data including a point cloud such as a WLD or SWLD. The three-dimensional map 1031 may include either compressed data or uncompressed data.

[0324] The communication unit 1012 communicates with the server 901 and transmits data transmission requests (for example, requests to transmit a three-dimensional map) to the server 901.

[0325] The reception control unit 1013 exchanges information such as compatible formats with the communication destination via the communication unit 1012, and establishes communication with the communication destination.

[0326] The format conversion unit 1014 generates a three-dimensional map 1032 by performing format conversion and the like on the three-dimensional map 1031 received by the data receiving unit 1011. Furthermore, if the three-dimensional map 1031 is compressed or encoded, the format conversion unit 1014 performs decompression or decoding processing. Note that if the three-dimensional map 1031 is uncompressed data, the format conversion unit 1014 does not perform decompression or decoding processing.

[0327] The multiple sensors 1015 are a group of sensors, such as LiDAR, a visible light camera, an infrared camera, or a depth sensor, that acquire information about the outside of the vehicle on which the client device 902 is mounted, and generate sensor information 1033. For example, if the sensor 1015 is a laser sensor such as LiDAR, the sensor information 1033 is three-dimensional data such as a point cloud (point cloud data). Note that the number of sensors 1015 does not need to be multiple.

[0328] The three-dimensional data creation unit 1016 creates three-dimensional data 1034 of the surroundings of the vehicle based on the sensor information 1033. For example, the three-dimensional data creation unit 1016 creates point cloud data with color information of the surroundings of the vehicle using information acquired by LiDAR and visible light images acquired by a visible light camera.

[0329] The three-dimensional image processing unit 1017 performs a process of estimating the vehicle's own position using a three-dimensional map 1032 such as a received point cloud and three-dimensional data 1034 of the surroundings of the vehicle generated from the sensor information 1033. The three-dimensional image processing unit 1017 may generate three-dimensional data 1035 of the surroundings of the vehicle by combining the three-dimensional map 1032 and the three-dimensional data 1034, and perform a process of estimating the vehicle's own position using the generated three-dimensional data 1035.

[0330] The three-dimensional data storage unit 1018 stores a three-dimensional map 1032, three-dimensional data 1034, three-dimensional data 1035, and the like.

[0331] The format conversion unit 1019 generates sensor information 1037 by converting the sensor information 1033 into a format supported by the receiving side. The format conversion unit 1019 may reduce the amount of data by compressing or encoding the sensor information 1037. The format conversion unit 1019 may omit the process if format conversion is not necessary. The format conversion unit 1019 may also control the amount of data to be transmitted in accordance with a specified transmission range.

[0332] The communication unit 1020 communicates with the server 901 and receives data transmission requests (sensor information transmission requests) and the like from the server 901.

[0333] The transmission control unit 1021 exchanges information such as compatible formats with the communication destination via the communication unit 1020, and establishes communication.

[0334] The data transmission unit 1022 transmits the sensor information 1037 to the server 901. The sensor information 1037 includes information acquired by a plurality of sensors 1015, such as information acquired by a LiDAR, a luminance image acquired by a visible light camera, an infrared image acquired by an infrared camera, a depth image acquired by a depth sensor, sensor position information, and speed information.

[0335] Next, the configuration of the server 901 will be described. Fig. 30 is a block diagram showing an example of the configuration of the server 901. The server 901 receives sensor information transmitted from the client device 902 and creates three-dimensional data based on the received sensor information. The server 901 uses the created three-dimensional data to update the three-dimensional map managed by the server 901. In addition, in response to a request from the client device 902 to transmit the three-dimensional map, the server 901 transmits the updated three-dimensional map to the client device 902.

[0336] The server 901 includes a data receiving unit 1111, a communication unit 1112, a receiving 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.

[0337] The data receiving unit 1111 receives sensor information 1037 from the client device 902. The sensor information 1037 includes, for example, information acquired by a LiDAR, a luminance image acquired by a visible light camera, an infrared image acquired by an infrared camera, a depth image acquired by a depth sensor, sensor position information, and speed information.

[0338] The communication unit 1112 communicates with the client device 902 and transmits a data transmission request (for example, a request to transmit sensor information) to the client device 902 .

[0339] The reception control unit 1113 exchanges information such as compatible formats with the communication destination via the communication unit 1112, and establishes communication.

[0340] If the received sensor information 1037 is compressed or encoded, the format conversion unit 1114 performs decompression or decoding processing to generate the sensor information 1132. Note that if the sensor information 1037 is uncompressed data, the format conversion unit 1114 does not perform decompression or decoding processing.

[0341] The three-dimensional data creation unit 1116 creates three-dimensional data 1134 of the periphery of the client device 902 based on the sensor information 1132. For example, the three-dimensional data creation unit 1116 creates point cloud data with color information of the periphery of the client device 902 using information acquired by the LiDAR and visible light images acquired by the visible light camera.

[0342] A three-dimensional data synthesis unit 1117 synthesizes three-dimensional data 1134 created based on sensor information 1132 with a three-dimensional map 1135 managed by the server 901, thereby updating the three-dimensional map 1135.

[0343] The three-dimensional data storage unit 1118 stores a three-dimensional map 1135 and the like.

[0344] The format conversion unit 1119 generates the three-dimensional map 1031 by converting the three-dimensional map 1135 into a format supported by the receiving side. The format conversion unit 1119 may reduce the amount of data by compressing or encoding the three-dimensional map 1135. The format conversion unit 1119 may also omit processing if format conversion is not necessary. The format conversion unit 1119 may also control the amount of data to be transmitted in accordance with the designation of the transmission range.

[0345] The communication unit 1120 communicates with the client device 902 and receives a data transmission request (a request to transmit a three-dimensional map) or the like from the client device 902 .

[0346] The transmission control unit 1121 exchanges information such as compatible formats with the communication destination via the communication unit 1120, and establishes communication.

[0347] The data transmission unit 1122 transmits the three-dimensional map 1031 to the client device 902. The three-dimensional map 1031 is data including a point cloud such as a WLD or SWLD. The three-dimensional map 1031 may include either compressed data or uncompressed data.

[0348] Next, a description will be given of the operational flow of the client device 902. Fig. 31 is a flowchart showing the operation of the client device 902 when acquiring a three-dimensional map.

[0349] First, the client device 902 requests the server 901 to transmit a three-dimensional map (such as a point cloud) (S1001). At this time, the client device 902 may also transmit location information of the client device 902 obtained by GPS or the like, thereby requesting the server 901 to transmit a three-dimensional map related to the location information.

[0350] 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).

[0351] Next, the client device 902 creates three-dimensional data 1034 of the surroundings of the client device 902 from sensor information 1033 obtained by the multiple 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 from the sensor information 1033 (S1005).

[0352] 32 is a flowchart showing the operation of the client device 902 when transmitting sensor information. First, the client device 902 receives a request to transmit sensor information from the server 901 (S1011). Upon receiving the transmission request, the client device 902 transmits sensor information 1037 to the server 901 (S1012). Note that, when the sensor information 1033 includes multiple pieces of information obtained by multiple sensors 1015, the client device 902 may generate the sensor information 1037 by compressing each piece of information using a compression method suitable for that piece of information.

[0353] Next, the operation flow of the server 901 will be described. Fig. 33 is a flowchart showing the operation when the server 901 acquires 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 response to the request (S1022). Next, the server 901 creates three-dimensional data 1134 using the received sensor information 1037 (S1023). Next, the server 901 reflects the created three-dimensional data 1134 in the three-dimensional map 1135 (S1024).

[0354] 34 is a flowchart showing the operation of the server 901 when transmitting a three-dimensional map. First, the server 901 receives a request to transmit a three-dimensional map from the client device 902 (S1031). Having received the request to transmit the three-dimensional map, the server 901 transmits a three-dimensional map 1031 to the client device 902 (S1032). At this time, the server 901 may extract a three-dimensional map of the vicinity based on the location information of the client device 902 and transmit the extracted three-dimensional map. The server 901 may also compress the three-dimensional map made up of a point cloud using, for example, a compression method with an octree structure, and transmit the compressed three-dimensional map.

[0355] A modification of this embodiment will now be described.

[0356] The server 901 uses the sensor information 1037 received from the client device 902 to create three-dimensional data 1134 of the vicinity of the position of the client device 902. Next, the server 901 matches the created three-dimensional data 1134 with a three-dimensional map 1135 of the same area managed by the server 901, thereby calculating the difference between the three-dimensional data 1134 and the three-dimensional map 1135. If the difference is equal to or greater than a predetermined threshold, the server 901 determines that some abnormality has occurred in the vicinity of the client device 902. For example, when ground subsidence occurs due to a natural disaster such as an earthquake, a large difference may occur between the three-dimensional map 1135 managed by the server 901 and the three-dimensional data 1134 created based on the sensor information 1037.

[0357] The sensor information 1037 may include information indicating at least one of the sensor type, sensor performance, and sensor model number. A class ID or the like according to the sensor performance may also be added to the sensor information 1037. For example, if the sensor information 1037 is information acquired by a LiDAR, an identifier may be assigned to the sensor performance, such as Class 1 for a sensor capable of acquiring information with an accuracy of several millimeters, Class 2 for a sensor capable of acquiring information with an accuracy of several centimeters, and Class 3 for a sensor capable of acquiring information with an accuracy of several meters. The server 901 may also estimate the sensor performance information and the like from the model number of the client device 902. For example, if the client device 902 is installed in a vehicle, the server 901 may determine the sensor specification information from the vehicle model. In this case, the server 901 may acquire vehicle model information in advance, or the information may be included in the sensor information. The server 901 may also use the acquired sensor information 1037 to switch the degree of correction for the three-dimensional data 1134 created using the sensor information 1037. For example, if the sensor performance is high accuracy (class 1), the server 901 does not perform correction on the three-dimensional data 1134. If the sensor performance is low accuracy (class 3), the server 901 applies correction according to the accuracy of the sensor to the three-dimensional data 1134. For example, the server 901 increases the degree (strength) of correction as the accuracy of the sensor decreases.

[0358] The server 901 may simultaneously issue requests to send sensor information to multiple client devices 902 in a certain space. When the server 901 receives multiple pieces of sensor information from multiple client devices 902, the server 901 does not need to use all of the sensor information to create the three-dimensional data 1134, and may select the sensor information to use, for example, depending on the performance of the sensor. For example, when updating the three-dimensional map 1135, the server 901 may select high-precision sensor information (Class 1) from the multiple pieces of sensor information it has received, and create the three-dimensional data 1134 using the selected sensor information.

[0359] The server 901 is not limited to a server such as a traffic monitoring cloud, but may be another client device (mounted in a vehicle). Figure 35 is a diagram showing the system configuration in this case.

[0360] For example, client device 902C issues a request to transmit sensor information to nearby client device 902A and acquires the sensor information from client device 902A. Client device 902C then creates three-dimensional data using the acquired sensor information from client device 902A and updates the three-dimensional map of client device 902C. This allows client device 902C to generate a three-dimensional map of the space that can be acquired from client device 902A by taking advantage of the performance of client device 902C. For example, such a case is likely to occur when client device 902C has high performance.

[0361] In this case, the client device 902A that provided the sensor information is granted the right to obtain the high-precision 3D map generated by the client device 902C. The client device 902A receives the high-precision 3D map from the client device 902C in accordance with the right.

[0362] In addition, client device 902C may issue requests to send sensor information to multiple nearby client devices 902 (client device 902A and client device 902B). If the sensor of client device 902A or client device 902B is high performance, client device 902C can create three-dimensional data using the sensor information obtained by this high performance sensor.

[0363] 36 is a block diagram showing the functional configuration of the server 901 and the client device 902. The server 901 includes, for example, a 3D map compression / decoding processing unit 1201 that compresses and decodes 3D maps, and a sensor information compression / decoding processing unit 1202 that compresses and decodes sensor information.

[0364] The client device 902 includes a three-dimensional map decoding processor 1211 and a sensor information compression processor 1212. The three-dimensional map decoding processor 1211 receives encoded data of the compressed three-dimensional map and decodes the encoded data to acquire the three-dimensional map. The sensor information compression processor 1212 compresses the sensor information itself instead of three-dimensional data created from the acquired sensor information, and transmits the encoded data of the compressed sensor information to the server 901. With this configuration, the client device 902 only needs to internally include a processing unit (device or LSI) that performs processing to decode the three-dimensional map (point cloud, etc.), and does not need to internally include a processing unit that performs processing to compress the three-dimensional data of the three-dimensional map (point cloud, etc.). This allows the cost and power consumption of the client device 902 to be reduced.

[0365] As described above, the client device 902 according to this embodiment is mounted on a mobile body and generates three-dimensional data 1034 of the surroundings of the mobile body from sensor information 1033 indicating the surrounding conditions of the mobile body, which is obtained by the sensor 1015 mounted on the mobile body. The client device 902 estimates the self-position of the mobile body using the generated three-dimensional data 1034. The client device 902 transmits the acquired sensor information 1033 to the server 901 or another mobile body 902.

[0366] According to this, the client device 902 transmits the sensor information 1033 to the server 901 or the like. This may reduce the amount of data to be transmitted compared to when transmitting three-dimensional data. Furthermore, since the client device 902 does not need to perform processing such as compression or encoding of the three-dimensional data, the amount of processing by the client device 902 can be reduced. Therefore, the client device 902 can reduce the amount of data to be transmitted or simplify the device configuration.

[0367] Furthermore, the client device 902 further transmits a request to the server 901 to send a three-dimensional map, and receives a three-dimensional map 1031 from the server 901. The client device 902 estimates its own location using the three-dimensional data 1034 and the three-dimensional map 1032.

[0368] The sensor information 1033 includes at least one of information obtained by a laser sensor, a luminance image, an infrared image, a depth image, sensor position information, and sensor speed information.

[0369] The sensor information 1033 also includes information indicating the performance of the sensor.

[0370] Furthermore, the client device 902 encodes or compresses the sensor information 1033, and transmits the encoded or compressed sensor information 1037 to the server 901 or another mobile body 902. This allows the client device 902 to reduce the amount of data to be transmitted.

[0371] For example, the client device 902 includes a processor and a memory, and the processor uses the memory to perform the above-described processing.

[0372] Furthermore, server 901 according to this embodiment is capable of communicating with client device 902 mounted on the mobile object, and receives sensor information 1037 indicating the surrounding conditions of the mobile object, obtained by sensor 1015 mounted on the mobile object, from client device 902. Server 901 creates three-dimensional data 1134 of the surroundings of the mobile object from the received sensor information 1037.

[0373] According to this, the server 901 creates three-dimensional data 1134 using the sensor information 1037 transmitted from the client device 902. This may reduce the amount of data to be transmitted compared to when the client device 902 transmits the three-dimensional data. Furthermore, since the client device 902 does not need to perform processing such as compression or encoding of the three-dimensional data, the amount of processing by the client device 902 can be reduced. Therefore, the server 901 can reduce the amount of data to be transmitted or simplify the device configuration.

[0374] Furthermore, the server 901 further transmits a request to the client device 902 to transmit the sensor information.

[0375] The server 901 also updates a three-dimensional map 1135 using the created three-dimensional data 1134 and transmits the three-dimensional map 1135 to the client device 902 in response to a request from the client device 902 to transmit the three-dimensional map 1135.

[0376] The sensor information 1037 includes at least one of information obtained by a laser sensor, a luminance image, an infrared image, a depth image, sensor position information, and sensor speed information.

[0377] The sensor information 1037 also includes information indicating the performance of the sensor.

[0378] Furthermore, the server 901 further corrects the three-dimensional data in accordance with the performance of the sensor, thereby enabling the three-dimensional data creation method to improve the quality of the three-dimensional data.

[0379] Furthermore, when receiving 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 to create the three-dimensional data 1134 based on a plurality of pieces of information indicating the performance of the sensors included in the plurality of pieces of sensor information 1037. This allows the server 901 to improve the quality of the three-dimensional data 1134.

[0380] Furthermore, the server 901 decodes or decompresses the received sensor information 1037, and creates three-dimensional data 1134 from the decoded or decompressed sensor information 1132. This allows the server 901 to reduce the amount of data to be transmitted.

[0381] For example, the server 901 includes a processor and a memory, and the processor uses the memory to perform the above-mentioned processing.

[0382] (Embodiment 7) In this embodiment, a method for encoding and decoding three-dimensional data using inter prediction processing will be described.

[0383] 37 is a block diagram of a three-dimensional data encoding device 1300 according to this embodiment. This three-dimensional data encoding device 1300 generates an encoded bitstream (hereinafter also simply referred to as a bitstream), which is an encoded signal, by encoding three-dimensional data. As shown in FIG. 37, the three-dimensional data encoding device 1300 includes a dividing 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 prediction unit 1309, a reference space memory 1310, an inter prediction unit 1311, a prediction control unit 1312, and an entropy encoding unit 1313.

[0384] The dividing unit 1301 divides each space (SPC) included in the three-dimensional data into multiple volumes (VLM), which are encoding units. The dividing unit 1301 also converts the voxels in each volume into an octree representation. The dividing unit 1301 may make the spaces and volumes the same size and convert the spaces into an octree representation. The dividing unit 1301 may also add information required for the octree representation (depth information, etc.) to a bitstream header, etc.

[0385] The subtraction unit 1302 calculates the difference between the volume (volume to be coded) output from the division unit 1301 and a prediction volume generated by intra prediction or inter prediction, which will be described later, and outputs the calculated difference as a prediction residual to the conversion unit 1303. Fig. 38 is a diagram showing an example of how a prediction residual is calculated. Note that the bit strings of the volume to be coded and the prediction volume shown here are, for example, position information indicating the positions of three-dimensional points (e.g., a point cloud) included in the volume.

[0386] The octree representation and the voxel scanning order will be explained below. A volume is converted into an octree structure (octreeing) and then encoded. The octree structure consists of nodes and leaves. Each node has eight nodes or leaves, and each leaf has voxel (VXL) information. Fig. 39 is a diagram showing an example of the structure of a volume containing multiple voxels. Fig. 40 is a diagram showing an example of the volume shown in Fig. 39 converted into an octree structure. Here, among the leaves shown in Fig. 40, leaves 1, 2, and 3 represent voxels VXL1, VXL2, and VXL3 shown in Fig. 39, respectively, and represent a VXL containing a point cloud (hereinafter referred to as an effective VXL).

[0387] An octree is represented by a binary sequence of, for example, 0 and 1. For example, if a node or a valid VXL is set to value 1 and the rest to value 0, then the binary sequence shown in FIG. 40 is assigned to each node and leaf. This binary sequence is then scanned according to the scan order, either breadth-first or depth-first. For example, when scanned breadth-first, the binary sequence shown in A of FIG. 41 is obtained. When scanned depth-first, the binary sequence shown in B of FIG. 41 is obtained. The binary sequence obtained by this scan is then coded by entropy coding to reduce the amount of information.

[0388] Next, we will explain depth information in octree representation. Depth in octree representation is used to control the granularity of the point cloud information contained in the volume to be retained. Setting a larger depth allows the point cloud information to be reproduced at a finer level, but the amount of data required to represent nodes and leaves increases. Conversely, setting a smaller depth reduces the amount of data, but since multiple point cloud information with different positions and colors is considered to be in the same position and with the same color, the information contained in the original point cloud information will be lost.

[0389] For example, FIG. 42 is a diagram showing an example in which the octree with depth=2 shown in FIG. 40 is represented by an octree with depth=1. The octree shown in FIG. 42 has a smaller amount of data than the octree shown in FIG. 40. In other words, the octree shown in FIG. 42 has a smaller number of bits after binarization than the octree shown in FIG. 42. Here, leaf 1 and leaf 2 shown in FIG. 40 are represented by leaf 1 shown in FIG. 41. In other words, the information that leaf 1 and leaf 2 shown in FIG. 40 were in different positions is lost.

[0390] FIG. 43 is a diagram showing volumes corresponding to the octree shown in FIG. 42. VXL1 and VXL2 shown in FIG. 39 correspond to VXL12 shown in FIG. 43. In this case, the three-dimensional data encoding device 1300 generates color information for VXL12 shown in FIG. 43 from the color information for VXL1 and VXL2 shown in FIG. 39. For example, the three-dimensional data encoding device 1300 calculates the average value, median value, or weighted average value of the color information for VXL1 and VXL2 as the color information for VXL12. In this way, the three-dimensional data encoding device 1300 may control the reduction of data amount by changing the depth of the octree.

[0391] The three-dimensional data encoding device 1300 may set the depth information of the octree in any unit of world, space, or volume. In this case, the three-dimensional data encoding device 1300 may add the depth information to the header information of the world, the header information of the space, or the header information of the volume. Furthermore, the same value may be used as the depth information for all worlds, spaces, and volumes at different times. In this case, the three-dimensional data encoding device 1300 may add the depth information to the header information that manages the worlds of all times.

[0392] If the voxels contain color information, the transform unit 1303 applies a frequency transform, such as an orthogonal transform, to the prediction residuals of the color information of the voxels in the volume. For example, the transform unit 1303 creates a one-dimensional array by scanning the prediction residuals in a certain scan order. The transform unit 1303 then applies a one-dimensional orthogonal transform to the created one-dimensional array to convert it into the frequency domain. As a result, when the values ​​of the prediction residuals in the volume are close, the values ​​of the low-frequency components become larger and the values ​​of the high-frequency components become smaller. This allows the quantization unit 1304 to reduce the amount of code more efficiently.

[0393] Furthermore, the transform unit 1303 may use a two- or more-dimensional orthogonal transform instead of a one-dimensional one. For example, the transform unit 1303 maps prediction residuals to a two-dimensional array in a certain scan order and applies a two-dimensional orthogonal transform to the obtained two-dimensional array. The transform unit 1303 may also select an orthogonal transform method to use from a plurality of orthogonal transform methods. In this case, the 3D data encoding device 1300 adds information indicating which orthogonal transform method was used to the bitstream. The transform unit 1303 may also select an orthogonal transform method to use from a plurality of orthogonal transform methods of different dimensions. In this case, the 3D data encoding device 1300 adds information indicating which orthogonal transform method was used to the bitstream.

[0394] For example, the conversion unit 1303 matches the scan order of the prediction residuals to the scan order (breadth-first or depth-first, etc.) of the octet tree in the volume. This eliminates the need to add information indicating the scan order of the prediction residuals to the bitstream, thereby reducing overhead. The conversion unit 1303 may also apply a scan order different from the scan order of the octet tree. In this case, the 3D data encoding device 1300 adds information indicating the scan order of the prediction residuals to the bitstream. This allows the 3D data encoding device 1300 to efficiently encode the prediction residuals. The 3D data encoding device 1300 may also add information (such as a flag) indicating whether or not to apply the octet scan order to the bitstream, and add information indicating the scan order of the prediction residuals to the bitstream when the octet scan order is not applied.

[0395] The conversion unit 1303 may convert not only the prediction residual of the color information but also other attribute information of the voxels. For example, the conversion unit 1303 may convert and encode information such as reflectance obtained when the point cloud is acquired by LiDAR or the like.

[0396] If the space does not have attribute information such as color information, the conversion unit 1303 may skip the process. Furthermore, the three-dimensional data encoding device 1300 may add information (a flag) indicating whether or not the process of the conversion unit 1303 is to be skipped to the bitstream.

[0397] The quantization unit 1304 generates quantized coefficients by quantizing the frequency components of the prediction residual generated by the transform unit 1303 using the quantization control parameters. This reduces the amount of information. The generated quantized coefficients are output to the entropy coding unit 1313. The quantization unit 1304 may control the quantization control parameters on a world-by-world, space-by-space, or volume-by-volume basis. In this case, the three-dimensional data coding device 1300 adds the quantization control parameters to the respective header information, etc. The quantization unit 1304 may also control quantization by changing the weight for each frequency component of the prediction residual. For example, the quantization unit 1304 may finely quantize low-frequency components and coarsely quantize high-frequency components. In this case, the three-dimensional data coding device 1300 may add a parameter indicating the weight of each frequency component to the header.

[0398] If the space does not have attribute information such as color information, the quantization unit 1304 may skip the process. Furthermore, the three-dimensional data encoding device 1300 may add information (a flag) indicating whether or not the process of the quantization unit 1304 is to be skipped to the bitstream.

[0399] The inverse quantization unit 1305 uses the quantization control parameter to inverse quantize the quantized coefficients generated by the quantization unit 1304 to generate inverse quantized coefficients of the prediction residuals, and outputs the generated inverse quantized coefficients to the inverse transform unit 1306.

[0400] The inverse transform unit 1306 generates a post-inverse transform prediction residual by applying inverse transform to the inverse quantized coefficients generated by the inverse quantization unit 1305. This post-inverse transform prediction residual is a prediction residual generated after quantization, and therefore does not need to completely match the prediction residual output by the transform unit 1303.

[0401] The adder 1307 generates a reconstructed volume by adding the prediction residual after inverse transform applied, generated by the inverse transformer 1306, and a prediction volume generated by intra prediction or inter prediction, which will be described later, and used to generate the prediction residual before quantization. This reconstructed volume is stored in a reference volume memory 1308 or a reference space memory 1310.

[0402] The intra prediction unit 1309 generates a predicted volume of the volume to be encoded using attribute information of the adjacent volume stored in the reference volume memory 1308. The attribute information includes color information or reflectance of voxels. The intra prediction unit 1309 generates a predicted value of the color information or reflectance of the volume to be encoded.

[0403] FIG. 44 is a diagram illustrating the operation of the intra prediction unit 1309. For example, the intra prediction unit 1309 generates a prediction volume of a volume to be coded (volume idx=3) shown in FIG. 44 from an adjacent volume (volume idx=0). Here, volume idx is identifier information assigned to volumes in a space, and a different value is assigned to each volume. The order in which the volumes idx are assigned may be the same as the encoding order, or may be different from the encoding order. For example, the intra prediction unit 1309 uses the average value of color information of voxels included in volume idx=0, which is an adjacent volume, as a prediction value of color information of the volume to be coded shown in FIG. 44. In this case, a prediction residual is generated by subtracting the prediction value of color information from the color information of each voxel included in the volume to be coded. The processing of the conversion unit 1303 and subsequent processes is performed on this prediction residual. In this case, the 3D data encoding device 1300 also adds adjacent volume information and prediction mode information to the bitstream. Here, the adjacent volume information is information indicating the adjacent volume used for prediction, for example, the volume idx of the adjacent volume used for prediction. Also, the prediction mode information indicates the mode used to generate the predicted volume. The mode is, for example, an average mode that generates a predicted value from the average value of the voxels in the adjacent volume, or an intermediate value mode that generates a predicted value from the intermediate value of the voxels in the adjacent volume.

[0404] The intra prediction unit 1309 may generate a prediction volume from multiple adjacent volumes. For example, in the configuration shown in Fig. 44 , the intra prediction unit 1309 generates prediction volume 0 from the volume with volume idx=0, and generates prediction volume 1 from the volume with volume idx=1. The intra prediction unit 1309 then generates the average of prediction volume 0 and prediction volume 1 as the final prediction volume. In this case, the three-dimensional data encoding device 1300 may add multiple volume idx of the multiple volumes used to generate the prediction volume to the bitstream.

[0405] 45 is a diagram schematically illustrating inter prediction processing according to this embodiment. The inter prediction unit 1311 encodes (inter predicts) a space (SPC) at a certain time T_Cur using an encoded space at a different time T_LX. In this case, the inter prediction unit 1311 performs encoding processing by applying rotation and translation processing to the encoded space at the different time T_LX.

[0406] Furthermore, the three-dimensional data encoding device 1300 adds RT information related to the rotation and translation processing applied to the space at a different time T_LX to the bitstream. The different time T_LX is, for example, time T_L0, which is before the certain time T_Cur. In this case, the three-dimensional data encoding device 1300 may add RT information RT_L0 related to the rotation and translation processing applied to the space at time T_L0 to the bitstream.

[0407] Alternatively, the different time T_LX may be, for example, time T_L1 that is later than the certain time T_Cur. In this case, the three-dimensional data encoding device 1300 may add RT information RT_L1 related to the rotation and translation processing applied to the space of time T_L1 to the bitstream.

[0408] Alternatively, the inter prediction unit 1311 performs encoding (bi-prediction) by referencing both spaces at different times T_L0 and T_L1. In this case, the 3D data encoding device 1300 may add both pieces of RT information RT_L0 and RT_L1 related to the rotation and translation applied to the respective spaces to the bitstream.

[0409] In the above, T_L0 is a time before T_Cur and T_L1 is a time after T_Cur, but this is not necessarily limited to this. For example, T_L0 and T_L1 may both be times before T_Cur. Alternatively, T_L0 and T_L1 may both be times after T_Cur.

[0410] Furthermore, when the three-dimensional data encoding device 1300 performs encoding by referencing multiple spaces at different times, it may add RT information related to the rotation and translation applied to each space to the bitstream. For example, the three-dimensional data encoding device 1300 manages multiple referenced encoded spaces using two reference lists (an L0 list and an L1 list). If the first reference space in the L0 list is L0R0, the second reference space in the L0 list is L0R1, the first reference space in the L1 list is L1R0, and the second reference space in the L1 list is L1R1, the three-dimensional data encoding device 1300 adds RT information RT_L0R0 of L0R0, RT information RT_L0R1 of L0R1, RT information RT_L1R0 of L1R0, and RT information RT_L1R1 of L1R1 to the bitstream. For example, the three-dimensional data encoding device 1300 adds this RT information to a header or the like of the bitstream.

[0411] Furthermore, when the three-dimensional data encoding device 1300 performs encoding by referring to reference spaces at multiple different times, it determines whether rotation and translation are applied for each reference space. In this case, the three-dimensional data encoding device 1300 may add information (such as an RT application flag) indicating whether rotation and translation are applied for each reference space to header information or the like of the bitstream. For example, the three-dimensional data encoding device 1300 calculates RT information and an ICP error value using an ICP (Interactive Closest Point) algorithm for each reference space referenced from the encoding target space. If the ICP error value is equal to or less than a predetermined value, the three-dimensional data encoding device 1300 determines that rotation and translation are not necessary and sets the RT application flag to OFF. On the other hand, if the ICP error value is greater than the predetermined value, the three-dimensional data encoding device 1300 sets the RT application flag to ON and adds RT information to the bitstream.

[0412] 46 is a diagram showing an example of syntax for adding RT information and an RT application flag to a header. The number of bits allocated to each syntax element may be determined within the range that the syntax element can take. For example, if the number of reference spaces included in the reference list L0 is eight, three bits may be allocated to MaxRefSpc_l0. The number of allocated bits may be variable depending on the value that each syntax element can take, or may be fixed regardless of the value that each syntax element can take. When the number of allocated bits is fixed, the three-dimensional data encoding device 1300 may add the fixed number of bits to other header information.

[0413] Here, MaxRefSpc_l0 shown in Figure 46 indicates the number of reference spaces included in the reference list L0. RT_flag_l0[i] is the RT application flag for reference space i in reference list L0. When RT_flag_l0[i] is 1, rotation and translation are applied to reference space i. When RT_flag_l0[i] is 0, rotation and translation are not applied to reference space i.

[0414] R_l0[i] and T_l0[i] are RT information of reference space i in reference list L0. R_l0[i] is rotation information of reference space i in reference list L0. The rotation information indicates the content of the applied rotation process, such as a rotation matrix or a quaternion. T_l0[i] is translation information of reference space i in reference list L0. The translation information indicates the content of the applied translation process, such as a translation vector.

[0415] MaxRefSpc_l1 indicates the number of reference spaces included in the reference list L1. RT_flag_l1[i] is the RT application flag for reference space i in reference list L1. If RT_flag_l1[i] is 1, rotation and translation are applied to reference space i. If RT_flag_l1[i] is 0, rotation and translation are not applied to reference space i.

[0416] R_l1[i] and T_l1[i] are the RT information of the reference space i in the reference list L1. R_l1[i] is the rotation information of the reference space i in the reference list L1. The rotation information indicates the content of the applied rotation process, such as a rotation matrix or a quaternion. T_l1[i] is the translation information of the reference space i in the reference list L1. The translation information indicates the content of the applied translation process, such as a translation vector.

[0417] The inter prediction unit 1311 generates a predicted volume of the volume to be coded using information about the coded reference space stored in the reference space memory 1310. As described above, before generating a predicted volume of the volume to be coded, the inter prediction unit 1311 obtains RT information for the space to be coded and the reference space using an ICP (Interactive Closest Point) algorithm to approximate the overall positional relationship between the space to be coded and the reference space. The inter prediction unit 1311 then obtains reference space B by applying rotation and translation processing to the reference space using the obtained RT information. The inter prediction unit 1311 then generates a predicted volume of the volume to be coded in the space to be coded using information in reference space B. Here, the three-dimensional data coding device 1300 adds the RT information used to obtain reference space B to header information, etc., of the space to be coded.

[0418] In this way, the inter prediction unit 1311 can improve the accuracy of the predicted volume by applying rotation and translation processing to the reference space to bring the overall positional relationship between the encoding target space and the reference space closer together, and then generating a predicted volume using information about the reference space. Furthermore, since prediction residuals can be suppressed, the amount of coding can be reduced. Note that, while an example of performing ICP using the encoding target space and the reference space has been shown here, this is not necessarily limited to this. For example, in order to reduce the amount of processing, the inter prediction unit 1311 may obtain RT information by performing ICP using at least one of the encoding target space in which the number of voxels or point clouds has been thinned out and the reference space in which the number of voxels or point clouds has been thinned out.

[0419] Furthermore, if the ICP error value obtained as a result of the ICP is smaller than a predetermined first threshold, that is, for example, if the positional relationship between the encoding target space and the reference space is close, the inter prediction unit 1311 may determine that rotation and translation processing is unnecessary and may not perform rotation and translation. In this case, the 3D data encoding device 1300 may reduce overhead by not adding RT information to the bitstream.

[0420] Furthermore, if the ICP error value is greater than a predetermined second threshold, the inter prediction unit 1311 may determine that there is a large change in shape between spaces and apply intra prediction to all volumes in the encoding target space. Hereinafter, a space to which intra prediction is applied is referred to as an intra space. The second threshold is a value greater than the first threshold. Furthermore, the method is not limited to ICP, and any method for obtaining RT information from two voxel sets or two point cloud sets may be applied.

[0421] Furthermore, when the three-dimensional data includes attribute information such as shape or color, the inter prediction unit 1311 searches, for example, a volume in the reference space whose attribute information, such as shape or color, is closest to that of the volume to be coded in the coding space as a prediction volume for the volume to be coded in the coding space. This reference space is, for example, the reference space after the above-described rotation and translation processing has been performed. The inter prediction unit 1311 generates a prediction volume from the volume (reference volume) obtained by the search. FIG. 47 is a diagram for explaining the operation of generating a prediction volume. When encoding the volume to be coded (volume idx=0) shown in FIG. 47 using inter prediction, the inter prediction unit 1311 sequentially scans the reference volumes in the reference space and searches for the volume with the smallest prediction residual, which is the difference between the volume to be coded and the reference volume. The inter prediction unit 1311 selects the volume with the smallest prediction residual as the prediction volume. The prediction residual between the volume to be coded and the prediction volume is encoded by processing from the conversion unit 1303 onwards. Here, the prediction residual is the difference between the attribute information of the volume to be coded and the attribute information of the prediction volume. Furthermore, the three-dimensional data encoding device 1300 adds the volume idx of the reference volume in the reference space referenced as the prediction volume to the header of the bitstream or the like.

[0422] 47, the reference volume of volume idx=4 in the reference space L0R0 is selected as the prediction volume of the volume to be encoded. Then, the prediction residual between the volume to be encoded and the reference volume and the reference volume idx=4 are encoded and added to the bitstream.

[0423] Although an example of generating a predicted volume for attribute information has been described here, the same processing may be performed for a predicted volume for position information.

[0424] The prediction control unit 1312 controls whether to use intra prediction or inter prediction to encode the volume to be encoded. Here, a mode including intra prediction and inter prediction is referred to as a prediction mode. For example, the prediction control unit 1312 calculates, as evaluation values, the prediction residual when the volume to be encoded is predicted by intra prediction and the prediction residual when the volume to be encoded is predicted by inter prediction, and selects the prediction mode with the smaller evaluation value. The prediction control unit 1312 may calculate the actual code amount by applying orthogonal transform, quantization, and entropy coding to the prediction residual of intra prediction and the prediction residual of inter prediction, respectively, and select the prediction mode using the calculated code amount as the evaluation value. Additionally, overhead information other than the prediction residual (such as reference volume idx information) may be added to the evaluation value. Furthermore, the prediction control unit 1312 may always select intra prediction when it is predetermined that the space to be encoded is to be encoded in intra space.

[0425] The entropy coding unit 1313 generates a coded signal (coded bit stream) by variable-length coding the quantized coefficients that are input from the quantization unit 1304. Specifically, the entropy coding unit 1313, for example, binarizes the quantized coefficients and arithmetically codes the resulting binary signal.

[0426] Next, we will explain a three-dimensional data decoding device that decodes the coded signal generated by the three-dimensional data coding device 1300. Fig. 48 is a block diagram of a three-dimensional data decoding device 1400 according to this embodiment. This three-dimensional data decoding device 1400 includes an entropy decoding unit 1401, an inverse quantization unit 1402, an inverse transform 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.

[0427] The entropy decoding unit 1401 performs variable-length decoding on the coded signal (coded bit stream). For example, the entropy decoding unit 1401 arithmetically decodes the coded signal to generate a binary signal, and generates quantization coefficients from the generated binary signal.

[0428] The inverse quantization unit 1402 inversely quantizes the quantized coefficients input from the entropy decoding unit 1401 using a quantization parameter added to the bitstream or the like, thereby generating inverse quantized coefficients.

[0429] The inverse transform unit 1403 generates prediction residuals by inverse transforming the inverse quantized coefficients input from the inverse quantization unit 1402. For example, the inverse transform unit 1403 generates prediction residuals by performing inverse orthogonal transform on the inverse quantized coefficients based on information added to the bitstream.

[0430] The adder 1404 generates a reconstructed volume by adding the prediction residual generated by the inverse transformer 1403 and the prediction volume generated by intra prediction or inter prediction. This reconstructed volume is output as decoded 3D data and is also stored in a reference volume memory 1405 or a reference space memory 1407.

[0431] The intra prediction unit 1406 generates a prediction volume by intra prediction using a reference volume in the reference volume memory 1405 and information added to the bitstream. Specifically, the intra prediction unit 1406 acquires adjacent volume information (e.g., volume idx) and prediction mode information added to the bitstream, and generates a prediction volume in the mode indicated by the prediction mode information using adjacent volumes indicated by the adjacent volume information. Note that the details of these processes are similar to the processes performed by the intra prediction unit 1309 described above, except that information added to the bitstream is used.

[0432] The inter prediction unit 1408 generates a prediction volume by inter prediction using the reference space in the reference space memory 1407 and information added to the bitstream. Specifically, the inter prediction unit 1408 applies rotation and translation processing to the reference space using RT information for each reference space added to the bitstream, and generates a prediction volume using the reference space after application. Note that, if an 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 in accordance with the RT application flag. Note that the details of these processes are similar to the processes by the inter prediction unit 1311 described above, except that information added to the bitstream is used.

[0433] The prediction control unit 1409 controls whether to decode the volume to be decoded using intra prediction or inter prediction. For example, the prediction control unit 1409 selects intra prediction or inter prediction according to information that indicates the prediction mode to be used and that is added to the bitstream. Note that the prediction control unit 1409 may always select intra prediction if it has been determined in advance that the space to be decoded will be decoded using intra space.

[0434] Modifications of this embodiment will be described below. In this embodiment, an example in which rotation and translation are applied on a space-by-space basis has been described; however, rotation and translation may be applied on a smaller unit basis. For example, the three-dimensional data encoding device 1300 may divide a space into subspaces and apply rotation and translation on a subspace-by-subspace basis. In this case, the three-dimensional data encoding device 1300 generates RT information for each subspace and adds the generated RT information to a bitstream header or the like. The three-dimensional data encoding device 1300 may also apply rotation and translation on a volume-by-volume basis, which is the encoding unit. In this case, the three-dimensional data encoding device 1300 generates RT information on an encoding volume-by-volume basis and adds the generated RT information to a bitstream header or the like. Furthermore, the above may be combined. That is, the three-dimensional data encoding device 1300 may apply rotation and translation on a larger unit basis, and then apply rotation and translation on a smaller unit basis. For example, the three-dimensional data encoding device 1300 may apply rotation and translation on a space-by-space basis, and then apply different rotations and translations to each of multiple volumes included in the resulting space.

[0435] Furthermore, although the present embodiment has been described with reference to an example in which rotation and translation are applied to the reference space, this is not necessarily limited to this. For example, the three-dimensional data encoding device 1300 may change the size of the three-dimensional data by applying a scale process. Furthermore, the three-dimensional data encoding device 1300 may apply any one or two of rotation, translation, and scale. Furthermore, when applying processes in different units in multiple stages as described above, different types of processes may be applied to each unit. For example, rotation and translation may be applied in space units, and translation may be applied in volume units.

[0436] These modifications can also be applied to the three-dimensional data decoding device 1400 in the same manner.

[0437] As described above, the three-dimensional data encoding device 1300 according to this embodiment performs the following processes. FIG.

[0438] First, the three-dimensional data encoding device 1300 generates predicted position information (e.g., predicted volume) using position information of three-dimensional points included in reference three-dimensional data (e.g., reference space) at a time different from that of the target three-dimensional data (e.g., encoding target space) (S1301). Specifically, the three-dimensional data encoding device 1300 generates predicted position information by applying rotation and translation processing to the position information of three-dimensional points included in the reference three-dimensional data.

[0439] The three-dimensional data encoding device 1300 may perform the rotation and translation processing in a first unit (e.g., space) and generate the predicted position information in a second unit (e.g., volume) that is finer than the first unit. For example, the three-dimensional data encoding device 1300 searches for a volume among multiple volumes included in the reference space after the rotation and translation processing that has the smallest difference in position information from the encoding target volume included in the encoding target space, and uses the obtained volume as the predicted volume. The three-dimensional data encoding device 1300 may perform the rotation and translation processing and the generation of the predicted position information in the same unit.

[0440] In addition, the three-dimensional data encoding device 1300 may generate predicted position information by applying a first rotation and translation process to the position information of three-dimensional points included in the reference three-dimensional data in a first unit (e.g., space), and applying a second rotation and translation process to the position information of the three-dimensional points obtained by the first rotation and translation process in a second unit (e.g., volume) that is finer than the first unit.

[0441] Here, the position information and predicted position information of the 3D points are expressed in an octree structure, for example, as shown in Fig. 41. For example, the position information and predicted position information of the 3D points are expressed in a scan order that prioritizes width over depth and width in the octree structure. Alternatively, the position information and predicted position information of the 3D points are expressed in a scan order that prioritizes depth over width in the octree structure.

[0442] 46, the three-dimensional data encoding device 1300 encodes an RT application flag indicating whether or not rotation and translation processing is applied to position information of 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. The three-dimensional data encoding device 1300 also encodes RT information indicating the details 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. Note that the three-dimensional data encoding device 1300 may encode the RT information when the RT application flag indicates that rotation and translation processing is to be applied, and may not need to encode the RT information when the RT application flag indicates that rotation and translation processing is not to be applied.

[0443] The three-dimensional data includes, for example, position information of the three-dimensional points and attribute information (such as color information) of each three-dimensional point. The three-dimensional data encoding device 1300 generates predicted attribute information using the attribute information of the three-dimensional points included in the reference three-dimensional data (S1302).

[0444] Next, the three-dimensional data encoding device 1300 encodes the position information of the three-dimensional point included in the target three-dimensional data using the predicted position information. For example, the three-dimensional data encoding device 1300 calculates differential position information, which is the difference between the position information of the three-dimensional point included in the target three-dimensional data and the predicted position information, as shown in Fig. 38 (S1303).

[0445] Furthermore, the three-dimensional data encoding device 1300 encodes attribute information of three-dimensional points included in the target three-dimensional data using the predicted attribute information. For example, the three-dimensional data encoding device 1300 calculates differential attribute information, which is the difference between the attribute information of three-dimensional points included in the target three-dimensional data and the predicted attribute information (S1304). Next, the three-dimensional data encoding device 1300 converts and quantizes the calculated differential attribute information (S1305).

[0446] Finally, the three-dimensional data encoding device 1300 encodes (e.g., entropy encodes) the differential position information and the quantized differential attribute information (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.

[0447] If the three-dimensional data does not include attribute information, the three-dimensional data encoding device 1300 may not perform steps S1302, S1304, and S1305. Furthermore, the three-dimensional data encoding device 1300 may perform only one of encoding the position information of the three-dimensional points and encoding the attribute information of the three-dimensional points.

[0448] 49 is an example and is not limited to this. For example, the processing for the location information (S1301, S1303) and the processing for the attribute information (S1302, S1304, S1305) are independent of each other, and therefore may be performed in any order, or some of them may be processed in parallel.

[0449] As described above, the three-dimensional data encoding device 1300 of this embodiment generates predicted position information using position information of three-dimensional points included in reference three-dimensional data at a different time from the target three-dimensional data, and encodes differential position information that is the difference between the position information of three-dimensional points included in the target three-dimensional data and the predicted position information. This reduces the data amount of the encoded signal, thereby improving encoding efficiency.

[0450] Furthermore, the three-dimensional data encoding device 1300 of this embodiment generates predicted attribute information using attribute information of three-dimensional points included in the reference three-dimensional data, and encodes differential attribute information that is the difference between the attribute information of three-dimensional points included in the target three-dimensional data and the predicted attribute information. This reduces the data amount of the encoded signal, thereby improving encoding efficiency.

[0451] For example, the three-dimensional data encoding device 1300 includes a processor and a memory, and the processor performs the above-described processing using the memory.

[0452] FIG. 48 is a flowchart of the inter prediction process performed by the 3D data decoding device 1400.

[0453] First, the three-dimensional data decoding device 1400 decodes (for example, entropy decodes) the differential position information and differential attribute information from the coded signal (coded bit stream) (S1401).

[0454] Furthermore, the three-dimensional data decoding device 1400 decodes, from the encoded signal, an RT application flag indicating whether or not rotation and translation processing is to be applied to position information of three-dimensional points included in the reference three-dimensional data. Furthermore, the three-dimensional data decoding device 1400 decodes RT information indicating the contents of the rotation and translation processing. Note that the three-dimensional data decoding device 1400 may decode the RT information when the RT application flag indicates that rotation and translation processing is to be applied, and may not need to decode the RT information when the RT application flag indicates that rotation and translation processing is not to be applied.

[0455] Next, the three-dimensional data decoding device 1400 performs inverse quantization and inverse transformation on the decoded differential attribute information (S1402).

[0456] Next, the three-dimensional data decoding device 1400 generates predicted position information (e.g., predicted volume) using position information of three-dimensional points included in reference three-dimensional data (e.g., reference space) at a time different from that of the target three-dimensional data (e.g., decoding target space) (S1403). Specifically, the three-dimensional data decoding device 1400 generates predicted position information by applying rotation and translation processing to the position information of three-dimensional points included in the reference three-dimensional data.

[0457] More specifically, when the RT application flag indicates that rotation and translation processing is to be applied, 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. On the other hand, when the RT application flag indicates that rotation and translation processing is not to be applied, 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.

[0458] The three-dimensional data decoding device 1400 may perform the rotation and translation processing in a first unit (e.g., space) and generate the predicted position information in a second unit (e.g., volume) that is smaller than the first unit. The three-dimensional data decoding device 1400 may perform the rotation and translation processing and the generation of the predicted position information in the same unit.

[0459] In addition, the three-dimensional data decoding device 1400 may generate predicted position information by applying a first rotation and translation process to the position information of three-dimensional points included in the reference three-dimensional data in a first unit (e.g., space), and applying a second rotation and translation process to the position information of the three-dimensional points obtained by the first rotation and translation process in a second unit (e.g., volume) that is finer than the first unit.

[0460] Here, the position information and predicted position information of the 3D points are expressed in an octree structure, for example, as shown in Fig. 41. For example, the position information and predicted position information of the 3D points are expressed in a scan order that prioritizes width over depth and width in the octree structure. Alternatively, the position information and predicted position information of the 3D points are expressed in a scan order that prioritizes depth over width in the octree structure.

[0461] The three-dimensional data decoding device 1400 generates predicted attribute information using attribute information of the three-dimensional points included in the reference three-dimensional data (S1404).

[0462] Next, the three-dimensional data decoding device 1400 restores the position information of the three-dimensional points included in the target three-dimensional data by decoding the encoded position information included in the encoded signal using the predicted position information. 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).

[0463] Furthermore, the three-dimensional data decoding device 1400 restores the attribute information of the three-dimensional points included in the target three-dimensional data by decoding the coded attribute information included in the coded signal using the predicted attribute information. Here, the coded 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).

[0464] Note that if the three-dimensional data does not include attribute information, the three-dimensional data decoding device 1400 does not need to perform steps S1402, S1404, and S1406. Furthermore, the three-dimensional data decoding device 1400 may perform only one of decoding the position information of the three-dimensional points and decoding the attribute information of the three-dimensional points.

[0465] 50 is an example and is not limited to this. For example, the processing for the location information (S1403, S1405) and the processing for the attribute information (S1402, S1404, S1406) are independent of each other, and therefore may be performed in any order, or some of them may be processed in parallel.

[0466] (Embodiment 8) In this embodiment, the three-dimensional data encoding device separates an input three-dimensional point cloud into two or more sub-three-dimensional point clouds and encodes each sub-three-dimensional point cloud so that no dependency occurs between the multiple sub-three-dimensional point clouds. This allows the three-dimensional data encoding device to encode multiple sub-three-dimensional point clouds in parallel. For example, the three-dimensional data encoding device separates an input three-dimensional point cloud into sub-three-dimensional point cloud A and sub-three-dimensional point cloud B, and encodes sub-three-dimensional point cloud A and sub-three-dimensional point cloud B in parallel.

[0467] As a method of separation, when encoding using an octree structure, the three-dimensional data encoding device encodes eight child nodes divided into the octree in parallel. For example, the three-dimensional data encoding device encodes multiple tree structures rooted at each child node in parallel.

[0468] The three-dimensional data encoding device does not necessarily need to encode multiple sub three-dimensional point clouds in parallel, and may encode multiple sub three-dimensional point clouds sequentially so as to avoid dependency. Furthermore, the method of this embodiment is not limited to an octet tree, and may also be applied to an N-ary tree (N is an integer equal to or greater than 2), such as a quad tree or a hexadecimal tree. The three-dimensional data encoding device may perform division using attribute information such as the color, reflectance, or normal vector of the point cloud. The three-dimensional data encoding device may also perform division based on differences in point cloud density.

[0469] Furthermore, the three-dimensional data encoding device may combine multiple pieces of encoded data for multiple encoded sub-three-dimensional point clouds into a single bit stream. In this case, the three-dimensional data encoding device may include the start position of each piece of encoded data for each sub-three-dimensional point cloud in a header or the like of the bit stream. For example, the three-dimensional data encoding device may include an address (bit position or number of bytes, etc.) from the beginning of the bit stream in a header or the like. This allows the three-dimensional data decoding device to know the start position of the encoded data for each sub-three-dimensional point cloud by decoding the beginning of the bit stream. Furthermore, the three-dimensional data decoding device can decode the encoded data for multiple sub-three-dimensional point clouds in parallel, thereby reducing processing time.

[0470] The 3D data encoding device may add a flag to the header of the bitstream indicating that the multiple sub 3D point clouds have been encoded so that no dependency occurs among the multiple sub 3D point clouds, or indicating that the multiple sub 3D point clouds have been encoded in parallel, so that the 3D data decoding device can determine whether the multiple encoded data of the multiple 3D point clouds can be decoded in parallel by decoding the header.

[0471] Here, "no dependency relationship occurs among multiple sub-3D point clouds" means, for example, that a coding table (such as a probability table used in entropy coding) for coding the occupancy codes or leaf information of multiple nodes of the multiple sub-3D point clouds is provided independently for each sub-3D point cloud. For example, a three-dimensional data coding device may use different coding tables for sub-3D point cloud A and sub-3D point cloud B to encode sub-3D point cloud A and sub-3D point cloud B without creating a dependency relationship between them. Alternatively, when processing sub-3D point cloud A and sub-3D point cloud B sequentially, the three-dimensional data coding device may initialize the coding table after encoding sub-3D point cloud A and before encoding sub-3D point cloud B so that a dependency relationship does not occur between sub-3D point cloud A and sub-3D point cloud B. In this way, the three-dimensional data coding device may provide an independent coding table for each sub-3D point cloud or initialize the coding table before encoding, thereby encoding multiple sub-3D point clouds without creating a dependency relationship between the multiple sub-3D point clouds. Similarly, a three-dimensional data decoding device can appropriately decode each sub-three-dimensional point cloud by having an independent encoding table (decoding table) for each sub-three-dimensional point cloud, or by initializing the encoding table before decoding each sub-three-dimensional point cloud.

[0472] Furthermore, "no dependency relationship occurs among multiple sub-3D point clouds" may mean, for example, that references between sub-3D point clouds are prohibited when encoding occupancy codes or leaf information of multiple nodes in multiple sub-3D point clouds. For example, when encoding the occupancy code of a target node to be encoded, a three-dimensional data encoding device performs encoding using information of adjacent nodes in an octree. In this case, if an adjacent node is included in another sub-3D point cloud, the three-dimensional data encoding device encodes the target node without referencing the adjacent node. In this case, the three-dimensional data encoding device may encode the target node assuming that no adjacent node exists, or may encode the target node under the condition that an adjacent node exists but the adjacent node is included in another sub-3D point cloud.

[0473] Similarly, the three-dimensional data decoding device prohibits reference between sub-three-dimensional point clouds, for example, when decoding occupancy codes or leaf information of multiple nodes in multiple sub-three-dimensional point clouds. For example, when decoding the occupancy code of a target node to be decoded, the three-dimensional data decoding device performs the decoding using information of adjacent nodes in an octree. In this case, if the adjacent node is included in another sub-three-dimensional point cloud, the three-dimensional data decoding device decodes the target node without referencing the adjacent node. In this case, the three-dimensional data decoding device may perform decoding assuming that no adjacent node exists, or may decode the target node under the condition that an adjacent node exists but the adjacent node is included in another sub-three-dimensional point cloud.

[0474] Furthermore, when encoding the three-dimensional position information and attribute information (such as color, reflectance, or normal vector) of multiple sub-three-dimensional point clouds, the three-dimensional data encoding device may encode one so that a dependency does not occur, and encode the other so that a dependency occurs. For example, the three-dimensional data encoding device may encode the three-dimensional position information so that a dependency does not occur, and encode the attribute information so that a dependency occurs. This allows the three-dimensional data encoding device to reduce processing time by encoding the three-dimensional position information in parallel, and reduce the amount of coding by encoding the attribute information sequentially. The three-dimensional data encoding device may add both information indicating whether the three-dimensional position information has been encoded so that a dependency does not occur and information indicating whether the attribute information has been encoded so that a dependency does not occur to the header. This allows the three-dimensional data decoding device to determine whether the three-dimensional position information can be decoded so that a dependency does not occur and whether the attribute information can be decoded so that a dependency does not occur by decoding the header. This allows the three-dimensional data decoding device to perform parallel decoding if there is no dependency. For example, if the three-dimensional position information is encoded so that there is no dependency, and the attribute information is encoded so that there is a dependency, the three-dimensional data decoding device reduces processing time by decoding the three-dimensional position information in parallel and decodes the attribute information sequentially.

[0475] FIG. 51 is a diagram showing an example of a tree structure. Note that FIG. 51 shows an example of a quadtree, but other tree structures such as an octtree may also be used. The three-dimensional data encoding device divides the tree structure shown in FIG. 51 into, for example, a sub-three-dimensional point cloud A shown in FIG. 52 and a sub-three-dimensional point cloud B shown in FIG. 53. Note that in this example, the division is performed at the active nodes of layer 1. That is, in the case of a quadtree, a maximum of four sub-three-dimensional point clouds are generated, and in the case of an octtree, a maximum of eight sub-three-dimensional point clouds are generated. The three-dimensional data encoding device may also perform the division using attribute information, point cloud density, or other information.

[0476] The three-dimensional data encoding device performs encoding so that no dependency occurs between sub-three-dimensional point cloud A and sub-three-dimensional point cloud B. For example, the three-dimensional data encoding device switches the encoding table used for entropy encoding of the occupancy code for each sub-three-dimensional point cloud. Alternatively, the three-dimensional data encoding device initializes the encoding table before encoding each sub-three-dimensional point cloud. Alternatively, when calculating node neighbor information, the three-dimensional data encoding device prohibits reference to an adjacent node if the adjacent node is included in a different sub-three-dimensional point cloud.

[0477] Fig. 54 is a diagram showing an example of the configuration of a bit stream according to this embodiment. As shown in Fig. 54, the bit stream includes a header, coded data of sub 3D point group A, and coded data of sub 3D point group B. The header includes point group number information, dependency relationship information, start address information A, and start address information B.

[0478] The point cloud number information indicates the number of sub 3D point clouds included in the bitstream. Note that the number may be indicated by an occupancy code as the point cloud number information. For example, in the example shown in Figure 51, the occupancy code "1010" for layer 0 is used, and the number of "1"s included in the occupancy code indicates the number of sub 3D point clouds.

[0479] The dependency information indicates whether the sub 3D point clouds are encoded without any dependency relationships. For example, the 3D data decoding device determines whether to decode the sub 3D point clouds in parallel based on the dependency information.

[0480] The start address information A indicates the start address of the encoded data of the sub 3D point group A. The start address information B indicates the start address of the encoded data of the sub 3D point group B.

[0481] The effects of parallel coding are explained below. In octree data of a 3D point group (point cloud), processing time can be reduced by dividing the geometric information (3D position information) or attribute information and encoding it in parallel. Parallel coding can be achieved when a node is independent of other nodes in the parent node hierarchy. In other words, it is necessary not to reference adjacent parent nodes. This condition must also be met for all child and grandchild nodes.

[0482] Figure 55 is a diagram showing an example of a tree structure. In the example shown in Figure 55, when depth-first coding is used, node A is independent of node C from layer 1. Also, node C is independent of node D from layer 2. Node A is independent of node B from layer 3.

[0483] The three-dimensional data encoding device uses this independent information of each node to select a parallel encoding method to use from the two parallel encoding methods based on the type of hardware, user settings, algorithm, data adaptability, etc.

[0484] The two methods are full parallel encoding and incremental parallel encoding.

[0485] First, we will explain fully parallel coding. Parallel processing or parallel programming requires processing a large amount of data simultaneously, which makes the processing very heavy.

[0486] The number of nodes that can be processed in parallel is determined by the number of processing units (PUs) included in a GPU (Graphics Processing Unit), the number of cores included in a CPU, or the number of threads in software implementation.

[0487] Generally, the number of nodes in an octree is greater than the number of available PUs. The three-dimensional data encoding device uses information indicating the number of encoded nodes in a layer to determine whether the number of nodes in the layer is an optimal number corresponding to the number of available PUs, and starts full parallel encoding when the number of nodes in the layer reaches the optimal number. Note that breadth-first or depth-first processing can be used in parallel processing.

[0488] The three-dimensional data encoding device may store information indicating the node (layer) at which the parallel encoding process has started in a header of the bitstream. This allows the three-dimensional data decoding device to use this information to perform parallel decoding if necessary. Note that the format of the information indicating the node at which the parallel encoding process has started may be any format, and for example, a location code may be used.

[0489] The 3D data encoding device also prepares an encoding table (probability table) for each node (sub-3D point cloud) that undergoes parallel encoding. This encoding table is initialized to an initial value or a value that differs for each node. For example, the value that differs for each node is a value based on the occupancy code of the parent node. This fully parallel encoding has the advantage that the GPU only needs to be initialized once.

[0490] Fig. 56 is a diagram for explaining fully parallel encoding, and shows an example of a tree structure. Fig. 57 is a diagram spatially showing sub 3D point clouds to be processed in parallel. The 3D data encoding device starts parallel processing when the number of nodes correlated with the number of PUs or threads reaches an optimal point.

[0491] In the example shown in Figure 56, the number of occupied nodes included in layer 3 is 9, which exceeds the optimal number. Therefore, the 3D data encoding device divides the 3D points (nodes) in layer 3 and below into multiple sub 3D point clouds with each occupied node in layer 3 as the root, and processes each sub 3D point cloud in parallel. For example, in the example shown in Figure 56, 9 sub 3D point clouds are generated.

[0492] The three-dimensional data encoding device may encode layer information indicating the layer where the parallel processing started. The three-dimensional data encoding device may also encode information indicating the number of occupied nodes when the parallel processing started (9 in the example of FIG. 56).

[0493] Furthermore, the three-dimensional data encoding device encodes, for example, multiple sub-three-dimensional groups while prohibiting mutual reference between them, and initializes a coding table (probability table, etc.) used for entropy coding before encoding each sub-three-dimensional point group.

[0494] Fig. 58 is a diagram showing an example of the configuration of a bitstream according to this embodiment. As shown in Fig. 58, the bitstream includes a header, upper-layer encoded data, a sub-header, encoded data of sub 3D point group A, and encoded data of sub 3D point group B.

[0495] The header includes space maximum size information and parallel start layer information. The space size information indicates the initial 3D space for dividing the 3D point cloud into an octree. For example, the space size information indicates the maximum coordinates (x, y, z) of the initial 3D space.

[0496] The parallel start layer information indicates a parallel start layer, which is a layer where parallel processing can be started. Here, the parallel start layer information indicates, for example, layer N.

[0497] The upper layer encoded data is encoded data up to layer N before parallel processing is started, and is node information up to layer N. For example, the upper layer encoded data includes occupancy codes of nodes up to layer N, etc.

[0498] The subheader includes information necessary for decoding layers N and beyond. For example, the subheader indicates the start address of the encoded data of each sub 3D point cloud. In the example shown in FIG. 58, the subheader includes start address information A and start address information B. Start address information A indicates the start address of the encoded data of sub 3D point cloud A. Start address information B indicates the start address of the encoded data of sub 3D point cloud B.

[0499] The three-dimensional data encoding device may store start address information A and start address information B in the header. This allows the three-dimensional data decoding device to decode the encoded data of the sub-three-dimensional point clouds in parallel before the upper-layer encoded data. In this case, the sub-header may include information indicating the space of each sub-three-dimensional point cloud. This information indicates the maximum coordinates (x, y, z) of the space of each sub-three-dimensional point cloud.

[0500] Fig. 59 is a diagram for explaining parallel decoding processing. As shown in Fig. 59, the three-dimensional data decoding device decodes the encoded data of sub three-dimensional point group A and the encoded data of sub three-dimensional point group B in parallel to generate decoded data of sub three-dimensional point A and decoded data of sub three-dimensional point group B. Next, the three-dimensional data decoding device integrates the generated decoded data of sub three-dimensional point A with the decoded data of sub three-dimensional point group B to generate decoded data of the three-dimensional point group. In this way, the three-dimensional data decoding device integrates three-dimensional position information and attribute information (color information, reflectance, etc.) included in the decoded data of multiple sub three-dimensional point groups. Furthermore, the three-dimensional data decoding device may output the integrated data as a single file.

[0501] The three-dimensional data decoding device does not necessarily need to decode all of the sub-three-dimensional point clouds, and may selectively decode the sub-three-dimensional point clouds that the three-dimensional data decoding device needs. For example, if the three-dimensional data decoding device is a mobility device such as an in-vehicle device, the three-dimensional data decoding device decodes the sub-three-dimensional point cloud of an area that is close to the current position acquired by GPS or the like, among the multiple sub-three-dimensional point clouds.

[0502] The 3D data encoding device may also store information indicating the priority of each sub-3D point cloud in the subheader. In this case, the 3D data decoding device performs parallel decoding by preferentially allocating computational resources, such as a CPU, to sub-3D point clouds with higher priorities according to the priorities indicated by the information included in the subheader. This allows the 3D data decoding device to efficiently decode sub-3D point clouds that include areas that are important to the 3D data decoding device.

[0503] 60 is a diagram showing a schematic flow of the fully parallel encoding process. First, the three-dimensional data encoding device determines the number of available PUs (S2601). Next, the three-dimensional data encoding device processes the octree and stores the node positions (S2602). Next, the three-dimensional data encoding device determines whether the number of occupied nodes is greater than the number of PUs (S2603).

[0504] If the number of occupied nodes is equal to or less than the number of PUs (No in S2603), the three-dimensional data encoding device performs the process of step S2602 for the next node. If the number of occupied nodes is greater than the number of PUs (Yes in S2603), the three-dimensional data encoding device sets the current layer as the parallel start layer, which is the layer that starts parallel processing (S2604).

[0505] Next, the three-dimensional data encoding device initializes multiple encoding tables and starts parallel encoding (S2605). After parallel encoding is completed, the three-dimensional data encoding device reconstructs the octree based on the positions stored in step S2602 (S2606).

[0506] The three-dimensional data encoding device may add parallel start layer information, which indicates the parallel start layer at which parallel encoding started, to the header of the bitstream. This allows the three-dimensional data decoding device to determine from which layer parallel decoding is possible by decoding the header.

[0507] It should be noted that the layer from which parallel processing starts may be determined in advance. Alternatively, parallel start layer information indicating the layer from which parallelization starts may not be added to the bitstream, and the layer from which parallelization starts may be specified by a standard or the like. For example, the three-dimensional data encoding device may add a flag indicating whether or not to perform parallelization to the bitstream. The three-dimensional data decoding device may start parallel processing from the first layer if the flag is on, and may apply sequential processing if the flag is off.

[0508] 61 is a diagram showing a schematic flow of the full parallel decoding process. First, the three-dimensional data decoding device obtains parallel start layer information indicating a layer N that can be processed in parallel by decoding the header (S2611). Next, the three-dimensional data decoding device decodes the occupancy code to divide the current node into eight, and proceeds to processing of the occupied subnodes (S2612).

[0509] Next, the three-dimensional data decoding device determines whether the layer to be processed has reached parallel start layer N (S2613). If the layer to be processed has not reached parallel start layer N (No in S2613), the three-dimensional data decoding device performs the processing of step S2612 on the next node. If the layer to be processed has reached parallel start layer N (Yes in S2613), the three-dimensional data decoding device decodes the subheader and obtains the start addresses of multiple sub-three-dimensional point clouds (S2614).

[0510] Next, the 3D data decoding device initializes multiple encoding tables and starts parallel decoding of multiple sub 3D point clouds (S2615). After parallel decoding is completed, the 3D data decoding device integrates the 3D position information and attribute information of the decoded sub 3D point clouds (S2616).

[0511] For example, the 3D data decoder decodes parallel start layer information from the header of the bitstream, which allows the 3D data decoder to determine which layer can be decoded in parallel.

[0512] Note that parallel start layer information indicating the layer at which parallelization starts may not be added to the bitstream, and the layer at which parallelization starts may be specified by a standard, etc. For example, the three-dimensional data encoding device may add a flag indicating whether or not to parallelize to the bitstream. The three-dimensional data decoding device may start parallel processing from the first layer if the flag is on, and may apply sequential processing if the flag is off.

[0513] Next, progressive parallel coding will be explained. Figures 62 and 63 are diagrams for explaining this progressive parallel coding. Figure 62 is a diagram showing parallel processing in a tree structure, and Figure 63 is a diagram showing time-series changes in parallel processing.

[0514] In progressive parallel coding, when a parent node is divided into one or more child nodes, if there are available PUs, the number of parallel nodes is gradually increased. Also, each time a new node that requires additional PUs is found, the coding table is set to a predetermined table.

[0515] In the example shown in Figure 62, processing begins at the top (root) with core 1. In layer 1, core 2 is used to process the right-hand node, while core 1 processes the left-hand node. In layer 2, core 1 continues processing the left-hand node. In layer 3, core 1 processes node A, while core 4 processes the newly discovered node B. Also, for the right-hand node in layer 1, core 2 continues processing the left-hand node in layer 2, while processing node C in layer 3. Also, core 3 is added in layer 2, and core 3 processes the right-hand node, while processing node D in layer 3.

[0516] The three-dimensional data encoding device may add a flag indicating whether or not initialization of the encoding table for entropy encoding is required for each node, allowing the three-dimensional data decoding device to determine that parallel processing is possible for the next child node of the node for which initialization is turned on by the flag.

[0517] 64 is a diagram showing a schematic flow of the progressive parallel encoding process. First, the three-dimensional data encoding device processes the octree and stores the node positions (S2621). Next, the three-dimensional data encoding device determines whether the target node has multiple occupied child nodes that are child nodes in an occupied state (S2622). If the target node has multiple occupied child nodes (Yes in S2613), the three-dimensional data encoding device determines whether there is a PU available for parallel encoding (S2623).

[0518] If the target node does not have multiple occupied child nodes (No in S2613), or if there are no PUs available for parallel encoding (No in S2623), the three-dimensional data encoding device continues processing the octree using the currently used PU (S2624). For example, in the initial state, the three-dimensional data encoding device continues processing using one PU in the initial state. In addition, cases where the target node does not have multiple occupied child nodes include cases where the target node does not have any occupied child nodes and cases where the target node has one occupied child node.

[0519] On the other hand, if there is a PU available for parallel encoding (Yes in S2623), the three-dimensional data encoding device adds the new PU to the PUs in use, initializes multiple encoding tables, and starts parallel encoding (S2625).

[0520] If processing of all nodes has not been completed (No in S2626), the three-dimensional data encoding device performs the processing of step S2621 on the next node. If processing of all nodes has been completed (Yes in S2626), the three-dimensional data encoding device reconstructs the octree based on the positions stored in step S2602 (S2627).

[0521] In this type of progressive parallel coding, if a target node has multiple occupied nodes and there is an available PU, parallel processing begins immediately. This allows the PU to be assigned to the next process if the PU's processing is completed in a short time, achieving an ideal balance of processing availability.

[0522] On the other hand, initialization processing is required each time parallel processing is requested. Also, since processing in multiple PUs may not necessarily be completed by the time the next processing in the vertical or horizontal processing order is completed, a mechanism is required to synchronize all nodes and child nodes in each layer in order to write back data. In other words, the above-mentioned fully parallel coding does not require such processing, thereby achieving the effect of reducing the amount of processing.

[0523] As described above, in this embodiment, the original 3D points are divided into branches that can be processed in parallel. In an octree, for example, eight branches that can be processed in parallel are generated for a node. In addition, a new parameter is defined that indicates from which layer of the octree the branches that can be processed in parallel start.

[0524] When the process moves to the next parallel-processable branch, the coding table for entropy coding is reset, or different coding tables are used for multiple parallel-processable branches.

[0525] For example, reference to nodes included in different parallel processable edges, such as information on adjacent nodes, is prohibited.

[0526] Several modes for parallel processing are defined. For example, mode 0 is a mode that does not perform parallel processing. Mode 1 is a mode that processes position information (structural information) in parallel. In this mode, it is permitted to refer to other branches that can be processed in parallel for attribute information. Mode 2 is a mode that processes position information and attribute information in parallel. In other words, it is prohibited to refer to other branches that can be processed in parallel for both position information and attribute information.

[0527] The starting address of the data for each parallelizable branch is encoded in a header, such as a slice header.

[0528] The three-dimensional data decoding device may process all parallel-processable branches in parallel, or may process some of the parallel-processable branches in parallel.

[0529] As described above, the three-dimensional data encoding device according to this embodiment performs the processing shown in FIG. 65. First, the three-dimensional data encoding device separates an N-ary tree structure of a plurality of three-dimensional points included in the three-dimensional data (N is an integer equal to or greater than 2) into a first branch (first sub-three-dimensional point cloud) and a second branch (second sub-three-dimensional point cloud) (S2631). Next, the three-dimensional data encoding device encodes the first branch and the second branch so that they can be decoded independently (S2632). In other words, the three-dimensional data encoding device encodes the first branch and the second branch so that they are not dependent on each other. For example, the three-dimensional data encoding device encodes the first branch and the second branch in parallel.

[0530] For example, the root of the first branch is a first node included in a first layer in the N-ary tree structure, and the root of the second branch is a second node different from the first node included in the first layer. In other words, the root of the first branch and the root of the second branch belong to the same layer.

[0531] For example, the three-dimensional data encoding device encodes information indicating the first layer (parallel start layer information). That is, the three-dimensional data encoding device generates a bitstream including information indicating the first layer (parallel start layer information).

[0532] For example, the three-dimensional data encoding device entropy encodes the first branch and the second branch using different encoding tables.

[0533] For example, the three-dimensional data encoding device initializes the encoding table after entropy encoding the first branch and before entropy encoding the second branch.

[0534] For example, the three-dimensional data encoding device prohibits reference to the second branch when encoding the first branch, and prohibits reference to the first branch when encoding the second branch.

[0535] For example, the three-dimensional data encoding device encodes the position information of the plurality of first three-dimensional points included in the first branch and the position information of the plurality of second three-dimensional points included in the second branch so that they can be decoded independently, and encodes the attribute information of the plurality of first three-dimensional points and the attribute information of the plurality of second three-dimensional points so that they can be decoded independently. In other words, the three-dimensional data encoding device encodes the position information and the attribute information so that there is no dependency between them.

[0536] For example, the three-dimensional data encoding device (1) encodes one of the position information of the plurality of first three-dimensional points included in the first branch and the position information of the plurality of second three-dimensional points included in the second branch, and (2) each of the attribute information of the plurality of first three-dimensional points and the attribute information of the plurality of second three-dimensional points so that they can be decoded independently, and encodes the other of (1) each of the position information of the plurality of first three-dimensional points and the position information of the plurality of second three-dimensional points and (2) each of the attribute information of the plurality of first three-dimensional points and the attribute information of the plurality of second three-dimensional points so that there is a dependency between them. In other words, the three-dimensional data encoding device encodes one of the position information and the attribute information so that there is no dependency between them, and encodes the other of the position information and the attribute information so that there is a dependency between them. Note that a dependency means, for example, that the first branch and the second branch are entropy coded using the same coding table, or that the coding table is not initialized after the first branch is entropy coded and before the second branch is entropy coded, or that the second branch is allowed to be referenced when coding the first branch, or that the first branch is allowed to be referenced when coding the second branch.

[0537] For example, the three-dimensional data encoding device encodes a flag indicating whether or not the first branch and the second branch have been encoded so that they can be decoded independently. That is, the three-dimensional data encoding device generates a bitstream including a flag indicating whether or not the first branch and the second branch have been encoded so that they can be decoded independently.

[0538] For example, the three-dimensional data encoding device includes a processor and a memory, and the processor performs the above-mentioned processing using the memory.

[0539] Furthermore, the three-dimensional data decoding device according to this embodiment performs the processing shown in FIG. 66. First, the three-dimensional data decoding device acquires first coded data and second coded data generated by coding a first branch and a second branch included in an N-ary tree structure of a plurality of three-dimensional points included in the three-dimensional data so that each of the first branch and the second branch can be independently decoded (S2641). For example, the three-dimensional data decoding device acquires the first coded data and the second coded data from a bit stream. Next, the three-dimensional data decoding device restores the first branch and the second branch by decoding each of the first coded data and the second coded data (S2642). For example, the three-dimensional data decoding device decodes the first coded data and the second coded data in parallel.

[0540] For example, the root of the first branch is a first node included in a first layer in the N-ary tree structure, and the root of the second branch is a second node different from the first node included in the first layer. In other words, the root of the first branch and the root of the second branch belong to the same layer.

[0541] For example, the three-dimensional data decoding device decodes the information indicating the first layer (parallel starting layer information). That is, the three-dimensional data decoding device obtains the information indicating the first layer (parallel starting layer information) from the bitstream.

[0542] For example, the three-dimensional data decoding device entropy decodes the first branch and the second branch using different code tables.

[0543] For example, the three-dimensional data decoding device initializes the encoding table after entropy decoding the first branch and before entropy decoding the second branch.

[0544] For example, the three-dimensional data decoding device does not refer to the second branch when decoding the first branch, and does not refer to the first branch when decoding the second branch.

[0545] For example, the first encoded data includes first encoded position data generated by encoding position information of a plurality of first three-dimensional points included in the first branch, and first encoded attribute data generated by encoding attribute information of the plurality of first three-dimensional points. The second encoded data includes second encoded position data generated by encoding position information of a plurality of second three-dimensional points included in the second branch, and second encoded attribute data generated by encoding attribute information of the plurality of second three-dimensional points. The first encoded position data and the second encoded position data are generated so as to be independently decodable, and the first encoded attribute data and the second encoded attribute data are generated so as to be independently decodable. In other words, the three-dimensional data encoding device generates the first encoded data and the second encoded data by encoding both the position information and the attribute information so as not to have a dependency relationship.

[0546] For example, the first coded data and the second coded data are generated by encoding one of the position information and the attribute information of the plurality of first three-dimensional points included in the first branch and the plurality of second three-dimensional points included in the second branch so that they can be decoded independently. The three-dimensional data decoding device restores the one of the position information and the attribute information of the plurality of first three-dimensional points and the plurality of second three-dimensional points by decoding each of the first coded data and the second coded data. The three-dimensional data decoding device further obtains third coded data and fourth coded data generated by encoding the other of the position information and the attribute information of the plurality of first three-dimensional points and the plurality of second three-dimensional points so that they are dependent on each other. The three-dimensional data decoding device restores the other of the position information and the attribute information of the plurality of first three-dimensional points and the plurality of second three-dimensional points by decoding each of the third coded data and the fourth coded data. In other words, the three-dimensional data encoding device performs coding so that one of the position information and the attribute information is not dependent on each other, and performs coding so that the other of the position information and the attribute information is dependent on each other. For example, when two pieces of coded data are dependent on each other, the three-dimensional data decoding device entropy decodes the two pieces of coded data using the same coding table, or after entropy decoding one piece of coded data, does not initialize the coding table before entropy decoding the other piece of coded data, or when decoding one piece of coded data, references the other piece of coded data.

[0547] For example, the three-dimensional data decoding device decodes a flag indicating whether the first branch and the second branch are encoded to be independently decodable. That is, the three-dimensional data decoding device acquires a flag indicating whether the first branch and the second branch are encoded to be independently decodable from a bitstream. For example, if the flag indicates that the first branch and the second branch are encoded to be independently decodable, the three-dimensional data decoding device decodes the first encoded data and the second encoded data in parallel, and if the flag does not indicate that the first branch and the second branch are encoded to be independently decodable, the three-dimensional data decoding device decodes the first encoded data and the second encoded data sequentially.

[0548] For example, the three-dimensional data decoding device includes a processor and a memory, and the processor performs the above processing using the memory.

[0549] (Embodiment 9) The bitstream of encoded 3D points (point clouds) requires large storage or cache capacity due to its large data size. Furthermore, the bitstream is complex and requires high-performance hardware. Furthermore, the distributed bitstream requires multiple PCC (Point Cloud Compression) bitstreams.

[0550] However, users may not always need all the information in a 3D point bitstream, and may need several PCC bitstreams or a combined bitstream containing multiple components of a bitstream. Therefore, a method for efficiently and parallelly obtaining 3D point information is desired.

[0551] Fig. 67 is a diagram showing the operation when a car having GPS coordinates accesses a bit stream of 3D points to obtain top-view map information. For example, as shown in Fig. 67, bit stream A is a PCC bit stream of 3D points in the area around the vehicle, and bit stream B and bit stream C are PCC bit streams of 3D points in adjacent areas. Each bit stream shows a top-view state in block format.

[0552] In this embodiment, tile division is used as a method for dividing a bitstream of 3D points into small regions. Tiles are divided regions obtained by dividing a bitstream of 3D points into different block regions based on user description.

[0553] The tiles have multiple levels from the root node, and tiles at different levels may have different sizes that are variable, and multiple tiles may be independent of each other.

[0554] The three-dimensional data encoding device may encode one or more tile levels, and the three-dimensional data decoding device may decode one or more tile levels required for the application from among the one or more tile levels.

[0555] Figure 68 is a diagram showing a top view of a single PCC bitstream divided into small tiles, Figure 69 is a diagram showing a top view of a single PCC bitstream divided into large tiles, and Figure 80 is a diagram showing a top view of a single PCC bitstream divided into tiles of multiple sizes.

[0556] In this way, the three-dimensional data encoding device may encode the same region using small tile division (FIG. 68) and large tile division (FIG. 69). For example, the three-dimensional data encoding device may switch between transmitting the results of small tile division and the results of large tile division for each region in response to a request from the three-dimensional data decoding device. Alternatively, the three-dimensional data encoding device may transmit both results to the three-dimensional data decoding device, and the three-dimensional data decoding device may switch between using the results of small tile division and the results of large tile division depending on the state of the vehicle (e.g., speed, location, etc.).

[0557] The following describes composite tile division using multiple tile sizes. A three-dimensional data encoding device may encode tiles of multiple sizes into a bitstream. For example, in the example shown in Figure 70, when a car is moving, the surrounding environment changes at different speeds. For example, areas far from the car, or areas in front or behind the car, change more slowly than areas to the sides of the car. In this way, encoding efficiency can be improved by applying larger tile sizes to areas where the change is slower.

[0558] 71 is a diagram showing an example of tile division in a quadtree. In the example shown in Fig. 71, some tiles are at the level of layer 2, and some other tiles are at the level of layer 3.

[0559] Although the above describes two-dimensional (top view) tile division, a similar method can be applied to three-dimensional tile division. Fig. 72 is a diagram showing an example of three-dimensional tile division. Note that Fig. 72 shows only some tiles for the sake of simplicity.

[0560] Tiles further ahead of the car, in the direction the car is moving, are set to larger sizes because they are more likely to need to be loaded, while tiles to the sides of the car are set to smaller sizes because the car is less likely to move in those directions.

[0561] Note that, similar to the examples shown in Figures 68 and 69, fixed size tiles may also be used in the three-dimensional case.

[0562] Also, encoded data generated using large-sized tiles and encoded data generated using small-sized tiles for the same area on the map may be stored in a server or an external storage device. When a vehicle moves into that area, the data for that area will be needed anyway, so the data for the large tiles will be sent to the vehicle. Also, in an area not in the vehicle's direction of travel, the vehicle will only need data for part of that area, so the data for the small tiles will be sent to the vehicle.

[0563] The use of tiles also improves spatial random accessibility. A 3D data decoder (for example, installed in a car) can decode loaded tiles in parallel according to their encoding method. The 3D data decoder can also control the memory size required during movement (for example, 3x3 tiles) to a constant value.

[0564] Fig. 73 is a diagram showing an example of tiles that are read when a car is moving. As shown in Fig. 73, when a car is moving in the x direction, the car reads (loads) new data for three tiles in the direction of travel (x direction) into memory, and deletes data for three tiles in the direction opposite to the direction of travel (-x direction) from memory.

[0565] The three-dimensional data decoding device may decode the read tiles in parallel. The three-dimensional data decoding device may also prioritize the read tiles and decode the tiles in the order of priority. For example, the three-dimensional data decoding device may prioritize decoding of tiles in an area closer to the direction of travel of the vehicle (e.g., tile A shown in FIG. 73).

[0566] Similarly, when the car is moving in the -y direction, the car loads new data for the three tiles in the direction of travel (-y direction) into memory and deletes data for the three tiles in the direction opposite to the direction of travel (y direction) from memory.

[0567] Also, when the car is moving diagonally (towards the upper right in the same figure), the car reads (loads) new data for the five tiles in the direction of travel into memory and deletes data for the five tiles in the direction opposite to the direction of travel from memory.

[0568] In this way, data for 3x3 tiles is always stored in memory, so the memory size can be limited to data for 3x3 tiles.

[0569] 74 is a diagram showing an example of the configuration of a system according to this embodiment. The system includes a server or an external storage device (a three-dimensional data encoding device) and an on-board computer (a three-dimensional data decoding device) installed in a vehicle or the like.

[0570] The server or external storage device stores all the 3D maps. Upon request from the on-board computer, the desired tile is loaded into the memory of the on-board computer and decoded. For example, the on-board computer requests tiles for an area corresponding to the current position of the vehicle from the server or external storage device.

[0571] Next, an example of how tiles are used will be described. Figures 75 and 76 are diagrams showing examples of tile areas acquired by a car. For example, when a car is traveling on a highway, it is moving fast, so it is necessary to quickly decode information ahead. Therefore, it is desirable to acquire necessary information with a smaller amount of data. For this reason, for example, a car acquires tiles in area A.

[0572] On the other hand, when the vehicle is driving in a city, the vehicle will acquire information from both area A and area B to allow the driver to better understand the surroundings. Also, when the vehicle is driving off-road or on a road the driver is unfamiliar with, the vehicle may acquire data from a wider area, such as 8x8 tiles, to acquire more data.

[0573] As another example, tiles to be decoded may be selected according to the vehicle's speed so as not to impose too much processing load on the decoding process. For example, when a vehicle is traveling at high speed on a highway, information about what is ahead needs to be updated quickly. However, areas to the sides of the vehicle are less important. Therefore, the vehicle selects and decodes tiles in a rectangular area ahead. On the other hand, when the vehicle is traveling at low speed, fewer tiles are needed ahead. Therefore, the area from which tiles ahead are acquired is narrower than when traveling at high speed. Furthermore, when a vehicle is stopped at an intersection, tiles in all surrounding directions are equally important. Therefore, the vehicle acquires tiles in all directions.

[0574] As described with reference to FIG. 72, the size of the tiles in area A and area B may be changed depending on the traveling direction and speed of the car.

[0575] Next, slices will be described. Multiple tiles are classified into slices to be used as semantic information in three-dimensional space. A slice is a group into which tiles are classified according to semantic information (attribute information) in three-dimensional space. In other words, each tile belongs to one of multiple slices. Information about the slice to which a tile belongs is coded in the header or subheader of the coded bitstream of three-dimensional points.

[0576] For example, a car traveling on a road will not use some roads on the map because the route to the destination is almost fixed, so these roads can be ignored. Also, the appearance of trees changes from day to day, so trees are less important than buildings and roads.

[0577] Fig. 77 is a diagram showing an example of three-dimensional data (three-dimensional points). Figs. 78 to 80 are diagrams showing examples of data of three slices obtained by dividing the three-dimensional data shown in Fig. 77. The slice shown in Fig. 78 includes three-dimensional data of a highway. The slice shown in Fig. 79 includes three-dimensional data of trees. The slice shown in Fig. 80 includes three-dimensional data of a side road.

[0578] Furthermore, slice division may be based on the characteristics of tiles or point clouds contained within tiles (color, reflectance, normal vector, or related objects). Slices may be set for each target application, such as classifying tiles containing point clouds used for in-vehicle self-localization as slice A and tiles containing point clouds to be displayed on a navigation screen as slice B. When setting slices for use in a three-dimensional map, slices may be set based on regional information to which the point clouds or tiles belong (such as prefectures or cities, towns, and villages in Japan).

[0579] Next, an example of tiles in an octree will be described. Figure 81 is a diagram showing an example of tiles. Figure 82 is a diagram showing an example of tiles in an octree.

[0580] For example, each tile and each slice can be coded and decoded independently. During coding and decoding, each tile and each slice includes a sub-octree related to the overall octree. For example, the three-dimensional data coding device and the three-dimensional data decoding device initialize the coding table for each tile.

[0581] Also, a decoded tile or slice may be immediately used by an application without waiting for the decoding of other tiles or slices.

[0582] Furthermore, the data for the tiles or slices is arranged in a predetermined order within the bitstream, for example, this order indicates the priority of the data in an application.

[0583] For example, as shown in FIG. 82, tile A and tile B are each entropy coded, and the generated bitstreams of tile A and tile B are included in the overall bitstream.

[0584] An example of the configuration of a bitstream according to this embodiment will be described below. Fig. 83 is a diagram showing an example of a bitstream obtained by entropy encoding a plurality of tiles. As shown in Fig. 83, the bitstream includes a common header 2801, which is header information (first header) common to a plurality of tiles, a tile table 2802, and encoded data 2803.

[0585] The common header 2801 includes an overall header 2804, a fixed size flag 2805, and tile count information 2806. The overall header 2804 is a header for the entire tree structure and includes position information 2807 indicating the position of the entire tree structure. In other words, the position information 2807 is information that specifies the position of the entire tree structure or the position in three-dimensional space (bounding box) corresponding to the tree structure. For example, the position information 2807 indicates the position (e.g., coordinates) of any node within the entire tree structure or any point included in three-dimensional space corresponding to the tree structure. For example, the position information 2807 indicates the position of the root of the tree structure. For example, the position information 2807 may indicate the coordinates of the root of the tree structure relative to world coordinates. The overall header may also include information indicating the number of three-dimensional points included in the entire tree structure.

[0586] The fixed size flag 2805 is a flag that indicates whether the size of a tile is fixed. In other words, the fixed size flag 2805 indicates whether multiple tiles have the same size. For example, a fixed size flag of 0 indicates that the tile size is not fixed, and a fixed size flag of 1 indicates that the tile size is fixed.

[0587] The tile count information 2806 is information indicating the number of tiles, and is used to analyze the tile table 2802. Note that the tile count information 2806 may be included in the tile table 2802. The tile table 2802 includes information on a plurality of tiles.

[0588] The coded data 2803 includes coded data for each tile. The coded data for each tile is, for example, independent.

[0589] 84 is a diagram showing an example of the configuration of a tile table 2802 when the fixed size flag = 0 (not fixed). The tile table 2802 includes tile information 2811, which is header information (second header) for each tile. In other words, multiple pieces of tile information 2811 correspond one-to-one to multiple tiles.

[0590] The tile information 2811 includes tile position information 2812, tile size information 2813, and code amount information 2814. The tile position information 2812 indicates the position of the tile. For example, the tile position information 2812 indicates the root position of the tile. For example, the tile position information 2812 may indicate coordinates based on world coordinates. In this case, the 3D data decoding device can use the coordinates of the decoded 3D points as they are, thereby reducing the amount of processing. Alternatively, the tile position information 2812 may indicate the difference between the position (coordinates) of the entire tree structure indicated by the position information 2807 and the position (coordinates) of the tile.

[0591] The tile size information 2813 indicates the size of the tile. The tile size may be indicated by the size in each of the x, y, and z directions, or may be indicated by the size of one side on the assumption that the x, y, and z sizes are equal. Furthermore, as described above, the tile size corresponds to a hierarchy (level) in the tree structure. Therefore, the tile size may be expressed by a hierarchy (level).

[0592] The code amount information 2814 indicates the code amount (bit size) of the coded data of the corresponding tile.

[0593] Furthermore, the three-dimensional data decoding device may calculate the start position (start bit) of the encoded data of each tile in the bitstream by referencing the code amount information 2814 of each tile contained in the tile table 2802. For example, the three-dimensional data decoding device calculates the start position of tile C shown in FIG. 83 by adding the code amount of tile A and the code amount of tile B. Furthermore, the start position of the encoded data of each tile in the bitstream may be stored in the tile table 2802. In this way, the three-dimensional data decoding device can know the start position of the encoded data of each tile in the bitstream by referencing the tile table 2802, thereby obtaining the encoded data of the necessary tiles and starting decoding early. Note that the code amount information 2814 may indicate the end position of the encoded data in question in the bitstream.

[0594] FIG. 85 is a diagram showing another example configuration of the tile table 2802 when the fixed size flag is 0 (not fixed). The tile information 2811 shown in FIG. 85 includes slice information 2815 in addition to the tile information 2811 shown in FIG. 84. The slice information 2815 indicates information (semantic information) about the slice related to the tile. In other words, the slice information 2815 indicates the slice to which the tile belongs. This information depends on the application used, but indicates, for example, color, object attributes, distance to the vehicle, density of three-dimensional points, etc.

[0595] 86 is a diagram showing an example of the configuration of the tile table 2802 when the fixed size flag is 1 (fixed). In this case, the tile size information 2813 is not included in the tile information 2811 of each tile, but is included in the tile table 2802 as information common to multiple tiles. In other words, the tile size information 2813 is included in the first header common to multiple tiles.

[0596] 87 is a diagram showing another example of the configuration of the tile table 2802 when the fixed size flag is 1 (fixed). Similar to the example shown in FIG. 85, the tile information 2811 may include slice information 2815.

[0597] The operation of the 3D data decoding device is described below. When a vehicle including a 3D data decoding device is connected to a map server, the following operations are performed: The vehicle including the 3D data decoding device sends a tile transmission request to the map server based on the vehicle's current rough position and the application's requirements. The map server references the tile table to select tiles that meet the request, generates a new bitstream including encoded data for the selected tiles, and transmits this bitstream to the vehicle. The vehicle references the tile table included in the received bitstream to obtain information about each tile. The vehicle reconstructs 3D points by decoding the bitstream of multiple tiles using the 3D position of the route of each tile indicated by this information.

[0598] Note that the vehicle may be offline and an external storage device may be used instead of the map server. In this case, the following operations are performed: The vehicle determines the optimal tile from the local tile table based on the vehicle's current rough location and the application requirements. The vehicle references the tile table included in the bitstream to obtain information about each tile. The vehicle restores 3D points by decoding the bitstream of multiple tiles using the 3D position of the route of each tile indicated by the information.

[0599] The three-dimensional data encoding device may store a slice identification number (slice_id) in the slice information 2815. This allows the three-dimensional data decoding device to obtain information about a tile having a required slice_id from the tile table 2802, and thus perform processing such as decoding the tile having the required slice_id.

[0600] The three-dimensional data encoding device may encode the common header 2801 (or overall header 2804), the tile table 2802, and the encoded data 2803 of each tile as separate NAL (Network Abstraction Layer) units. In other words, the three-dimensional data encoding device may generate a stream including multiple second headers for multiple tiles as a stream independent of the encoded data of the multiple tiles. In this way, for example, the server (three-dimensional data encoding device) first transmits the NAL units of the tile table 2802 to the client (three-dimensional data decoding device). The client decodes the tile table 2802 to determine the necessary tiles and requests the server to transmit the necessary tiles. In response to the client's request, the server transmits the NAL units of the requested tiles to the client.

[0601] The tile position information 2812 may indicate coordinates based on a certain world coordinate. This allows the three-dimensional data encoding device to quickly determine the position of the point group included in each tile, based on the world coordinate. The tile position information 2812 may also indicate relative coordinates from the coordinates indicated by the position information 2807 included in the overall header 2804. In this case, the three-dimensional data decoding device may calculate the coordinates of each tile based on the world coordinate by adding the relative coordinates indicated by the tile position information 2812 to the coordinates indicated by the position information 2807. This allows the size of the tile position information 2812 value to be reduced, thereby reducing the size of the tile table 2802.

[0602] The operation of the three-dimensional data encoding device and the operation of the three-dimensional data decoding device will be described below. Figure 88 is a flowchart of three-dimensional data encoding processing by the three-dimensional data encoding device according to this embodiment.

[0603] First, the three-dimensional data encoding device sets a bounding box that includes the input three-dimensional points (S2801). Next, the three-dimensional data encoding device divides the bounding box into eight child nodes (S2802).

[0604] Next, the three-dimensional data encoding device generates an occupancy code for each of the eight child nodes that includes a three-dimensional point (S2803). Next, the three-dimensional data encoding device determines whether the level (hierarchy of the tree structure) of the node to be processed has reached the target tile level (S2804). Here, the target tile level is the level (hierarchy of the tree structure) at which tile division is performed.

[0605] If the level of the node to be processed has not reached the target tile level (No in S2804), the three-dimensional data encoding device divides each child node into eight grandchild nodes (S2805) and performs the processing from step S2803 onwards for each of the grandchild nodes.

[0606] If the level of the node to be processed has reached the target tile level (Yes in S2804), the three-dimensional data encoding device stores the current node position and tile level (tile size) in the tile table (S2806).

[0607] Next, the three-dimensional data encoding device divides each child node into eight grandchild nodes (S2807). Next, the three-dimensional data encoding device repeats the process of generating an occupancy code until the node cannot be divided any more (S2808). Next, the three-dimensional data encoding device encodes the occupancy code of each tile (S2809).

[0608] Finally, the three-dimensional data encoding device combines the generated encoded bit streams (encoded data) of the multiple tiles (S2810). The three-dimensional data encoding device also adds a tile table and the like to the header information of the bit stream.

[0609] Here, tile sizes (tile levels) are stored in the tile table 2802. Therefore, the three-dimensional data decoding device can use these tile sizes to obtain the size of the bounding box of the subtree of each tile. Furthermore, the three-dimensional data decoding device can use the size of the bounding box of the subtree to calculate the size of the bounding box of the entire tree structure.

[0610] The three-dimensional data encoding device may store the size of the bounding box of each tile in the tile table 2802. This allows the three-dimensional data decoding device to obtain the size of the bounding box of each tile by referring to the tile table 2802.

[0611] FIG. 89 is a flowchart of three-dimensional data decoding processing by the three-dimensional data decoding device according to this embodiment.

[0612] First, the 3D data decoding device uses header information included in the bitstream to set a bounding box that includes the 3D points to be output (S2821). Next, the 3D data decoding device uses header information included in the bitstream to set the root position of each tile (subtree) (S2822).

[0613] Next, the three-dimensional data decoding device divides the bounding box into eight child nodes (S2823). Next, the three-dimensional data decoding device decodes the occupancy code of each node and divides the node into eight child nodes based on the decoded occupancy code. The three-dimensional data decoding device also repeats this process until the nodes of each tile (subtree) can no longer be divided (S2824).

[0614] Finally, the 3D data decoding device combines the 3D points of the decoded tiles (S2825).

[0615] 90 is a block diagram showing the configuration of a three-dimensional data encoding device 2820 according to this embodiment. The three-dimensional data encoding device 2820 includes an octree generation unit 2821, a tile division unit 2822, multiple entropy encoding units 2823, and a bitstream generation unit 2824.

[0616] The target tile level is input to the three-dimensional data encoding device 2820. After the processing reaches the target tile level, the three-dimensional data encoding device 2820 stores the occupancy encoding of each tile and generates code data for each tile by individually encoding the occupancy codes of multiple tiles.

[0617] The octree generating unit 2821 sets a bounding box and divides the bounding box into eight child nodes. The octree generating unit 2821 also repeats this division process until the process reaches the target tile level.

[0618] The tile division unit 2822 sets tiles. Specifically, when the above process reaches the target tile level, multiple tiles are set with that level as the root.

[0619] The plurality of entropy coding units 2823 individually code the plurality of tiles, and the bitstream generation unit 2824 generates a bitstream by combining the coded data obtained by coding the plurality of tiles.

[0620] 91 is a block diagram showing the configuration of a three-dimensional data decoding device 2830 according to this embodiment. The three-dimensional data decoding device 2830 includes an octree generation unit 2831, a bitstream division unit 2832, multiple entropy decoding units 2833, and a three-dimensional point connection unit 2834.

[0621] The octree generating unit 2831 sets a bounding box and divides the bounding box into eight child nodes. The octree generating unit 2831 also repeats this division process until the process reaches the target tile level.

[0622] The bitstream dividing unit 2832 divides the bitstream into coded data for each tile using header information included in the bitstream.

[0623] The multiple entropy decoders 2833 individually decode the multiple tiles. The 3D point combiner 2834 combines the 3D points of the decoded tiles. Note that the decoded 3D points may be directly used in an application. In such a case, this combining process is skipped.

[0624] As described above, the three-dimensional data encoding device according to this embodiment performs the processing shown in Fig. 92. The three-dimensional data encoding device generates a bitstream by encoding a plurality of subspaces (e.g., tiles) included in a target space (e.g., a bounding box) that includes a plurality of three-dimensional points. In generating the bitstream, the three-dimensional data encoding device stores first information (e.g., position information 2807) indicating first coordinates, which are coordinates of the target space, in a first header (e.g., common header 2801 or overall header 2804) included in the bitstream and common to a plurality of subspaces (S2831), and stores second information (e.g., tile position information 2812) indicating the difference between the second coordinates, which are coordinates of the corresponding subspace, and the first coordinate, in a second header (e.g., tile information 2811) included in the bitstream for each subspace (S2832).

[0625] According to this, since information indicating the difference between the first coordinate and the second coordinate is stored as the second information, the amount of code in the bitstream can be reduced.

[0626] Note that at least a portion of the first header and multiple second headers may be included in a single header (syntax). For example, as shown in Fig. 86, tile table 2802 may include tile size information 2913 (first header) that is information common to multiple subspaces, and tile information 2811 (second header) that is information for each subspace. Furthermore, the first header may include at least a portion of common header 2801 and a portion of tile table 2802.

[0627] For example, a first subspace and a second subspace included in a plurality of subspaces may partially overlap. For example, as shown in Figures 68, 69, and 70, different tile divisions may be used for the same area.

[0628] For example, when generating the bitstream, the three-dimensional data encoding device stores third information (e.g., tile size information 2813 in Figure 84) indicating the size of the corresponding subspace in the second header (e.g., tile information 2811 in Figure 84).

[0629] For example, when generating the bitstream, the three-dimensional data encoding device stores third information (tile size information 2813 in FIG. 86) indicating the sizes of multiple subspaces in the first header.

[0630] For example, when generating the bitstream, the three-dimensional data encoding device stores fourth information (tile number information 2806) indicating the number of multiple subspaces in the first header (for example, common header 2801 or tile table 2802).

[0631] For example, in generating the bitstream, the three-dimensional data encoding device generates a stream including a plurality of second headers for a plurality of subspaces as a stream independent of the encoded data of the plurality of subspaces. For example, the plurality of second headers for the plurality of subspaces and the encoded data of the plurality of subspaces are encoded as separate NAL Units.

[0632] According to this, for example, a three-dimensional data decoding device can determine a necessary subspace by referring to a stream including the second header, and selectively acquire encoded data of the necessary subspace.

[0633] For example, the three-dimensional data encoding device includes a processor and a memory, and the processor performs the above-mentioned processing using the memory.

[0634] Furthermore, the three-dimensional data decoding device according to this embodiment performs the processing shown in FIG. 93. The three-dimensional data decoding device decodes a bitstream obtained by encoding multiple subspaces included in a target space containing multiple three-dimensional points. In decoding the bitstream, the three-dimensional data decoding device decodes first information (e.g., position information 2807) indicating first coordinates, which are coordinates of the target space, from a first header (e.g., common header 2801 or overall header 2804) included in the bitstream and common to multiple subspaces (S2841), and decodes second information (e.g., tile position information 2812) indicating the difference between the first coordinate and a second coordinate, which is the coordinate of the corresponding subspace, from a second header (e.g., tile information 2811) included in the bitstream for each subspace (S2842). For example, the three-dimensional data decoding device calculates the second coordinate by adding the difference indicated in the second information to the first coordinate indicated in the first information.

[0635] According to this, since information indicating the difference between the first coordinate and the second coordinate is stored as the second information, the amount of code in the bitstream can be reduced.

[0636] For example, a first subspace and a second subspace included in a plurality of subspaces may partially overlap. For example, as shown in Figures 68, 69, and 70, different tile divisions may be used for the same area.

[0637] For example, when decoding the bitstream, a three-dimensional data decoding device decodes third information (e.g., tile size information 2813 in Figure 84) indicating the size of the corresponding subspace from the second header (e.g., tile information 2811 in Figure 84).

[0638] For example, in decoding the bitstream, the three-dimensional data decoding device decodes the third information (tile size information 2813 in FIG. 86) indicating the sizes of multiple subspaces from the first header.

[0639] For example, in decoding the bitstream, the three-dimensional data decoding device decodes the fourth information (tile number information 2806) indicating the number of multiple subspaces from the first header (for example, common header 2801).

[0640] For example, a stream including multiple second headers for multiple subspaces is generated as a stream independent of the decoded data for the multiple subspaces. For example, the multiple second headers for the multiple subspaces and the encoded data for the multiple subspaces are encoded as separate NAL units. For example, a three-dimensional data decoding device determines a required subspace by referring to the stream including the second headers, and selectively obtains the encoded data for the required subspace.

[0641] For example, the three-dimensional data decoding device includes a processor and a memory, and the processor performs the above processing using the memory.

[0642] (Embodiment 10) A 3D point cloud map can be used in a localization process to allow a car or drone to find its location in global coordinates. To provide global coordinates, the 3D point cloud map needs to be georeferenced.

[0643] In autonomous vehicle applications, georeferenced 3D point cloud maps are necessary for autonomous driving processes using geofencing, which is a technology for establishing virtual boundaries (geofences) within geographical regions in real space. For example, in autonomous driving processes, automobiles are restricted to operate within specific geographical regions (geofences) for safety and security reasons.

[0644] Also, in a georeferenced 3D point cloud map, the internal (local) coordinate system of the 3D point cloud can be related to geographic coordinates.

[0645] In this embodiment, global coordinates and local coordinates are used. Global coordinates are expressed, for example, by latitude, longitude, and altitude, and are coordinates that can uniquely identify a position on Earth. Local coordinates are, for example, coordinates based on the position of a sensor (or a mobile body (e.g., an automobile) equipped with a sensor) that generates point cloud data. For example, the origin of the local coordinate system is the position of the sensor or automobile. In other words, global coordinates do not depend on the position of the sensor (or automobile), and indicate the same coordinates regardless of the position of the sensor. On the other hand, local coordinates change depending on the position of the sensor.

[0646] Furthermore, global coordinates are expressed as latitude-longitude coordinates (spherical coordinates) represented by latitude, longitude, and altitude, or as Cartesian coordinates represented by X, Y, and Z. For example, local coordinates are expressed as Cartesian coordinates. Note that the coordinate expression method is not limited to these, and any known expression method may be used.

[0647] In the following, global coordinates expressed in latitude and longitude coordinates will be called geographic coordinates, global coordinates expressed in Cartesian coordinates will be called world coordinates, and local coordinates expressed in Cartesian coordinates will be called geometric coordinates.

[0648] Furthermore, in this embodiment, the coordinates may be information that directly indicates the coordinates themselves, or may be information that indirectly indicates the coordinates or a position.

[0649] Fig. 94 is a diagram showing an example of a 3D point cloud map including a geofence when the geofence is used to restrict movement. For example, the movement of an autonomous vehicle is restricted within the geofence shown in Fig. 94.

[0650] Figure 95 is a diagram showing an example of a three-dimensional point cloud map used to activate services or additional functions within a geofence. For example, when a vehicle enters the geofence shown in Figure 95, some services or functions are activated.

[0651] Additionally, some applications require vehicles to upload 3D point clouds along with geographic coordinates.

[0652] FIG. 96 is a diagram illustrating a process for updating a geo-referenced 3D point cloud (hereinafter referred to as a geo-referenced point cloud). During mapping or a map update or update process, the vehicle updates the geo-referenced point cloud to a map server. For example, the vehicle generates the geo-referenced point cloud using a point cloud acquired by a sensor such as LiDAR and geographic information acquired from a GPS or the like.

[0653] A use case may be considered in which a vehicle downloads a georeferenced point cloud from a server based on the vehicle's location information (global coordinates). The vehicle may acquire its location information using a GPS sensor equipped in the vehicle. The vehicle may also estimate its future location information and download the georeferenced point cloud from a server based on that information. The vehicle may also estimate its future location information from a destination specified by the user using a car navigation system or the like. Alternatively, the vehicle may estimate its future location information from the vehicle's control information (such as speed or acceleration) or surrounding conditions (such as traffic congestion information or accident information).

[0654] The following describes the control of three-dimensional positions using a georeferenced point cloud. Figure 97 is a diagram showing an example of three-dimensional positions in a georeferenced point cloud. In Figure 97, geographical coordinates are expressed as φ, λ, h (latitude, longitude, altitude).

[0655] A 3D point cloud is divided into the same or different volumes (spatial units). In each volume, the local coordinates (X, Y, Z) of one or more 3D points are mapped to geographic coordinates (φ, λ, h). In the same volume, all 3D points have the same or different geographic coordinates.

[0656] The automobile is equipped with a sensor A for detecting its position. For example, the sensor A includes a global positioning system (GPS) for obtaining the geographical position of the automobile. However, the sensor A does not have to include a GPS. If GPS information cannot be obtained or if the accuracy of the GPS information is low, the automobile may detect the global coordinates of the automobile using information obtained by the sensor A (for example, information on point clouds C1, C2, and C3) and a three-dimensional point cloud map. Here, the three-dimensional point cloud map includes the global coordinates of the point clouds C1, C2, and C3.

[0657] There are three cases for the state of the geographic coordinates of a single 3D point (target 3D point). The first case is when the geographic coordinates are obtained from a GPS system. The second case is when the geographic coordinates of the target 3D point are obtained by interpolating the geographic coordinates of multiple adjacent 3D points that have geographic coordinates obtained from a GPS system. The third case is when no information on the geographic coordinates exists.

[0658] The state of the geographic coordinates of the above 3D points is determined by the deployment in the actual application, and since there are different points in the 3D space, the geographic coordinates of multiple 3D points may be different.

[0659] For example, if sensor A is a LiDAR, the automobile estimates its own position (global coordinates) within the three-dimensional point cloud map using point cloud data around the automobile obtained by the LiDAR and a three-dimensional point cloud map obtained from a server or the like. In this case, if global coordinates are added to each point cloud in the three-dimensional point cloud map, the automobile may calculate its own global coordinates based on those global coordinates. This allows the automobile to obtain absolute position information on Earth (latitude, longitude, altitude, etc.). This enables the automobile to receive services using the geofencing described above.

[0660] A method for obtaining a georeferenced point cloud will be described below. Figure 98 is a diagram for explaining a method for obtaining a georeferenced point cloud.

[0661] The georeferenced point cloud is obtained in the following way: The car is equipped with a sensor A and a sensor B. The sensor A obtains the local coordinates (e.g., (X i , Y i , Z i ),...,(X n , Y n , Z n) is acquired. Sensor B acquires, for example, the geographic coordinates (φ0, λ0, h0) of the automobile. In addition, the automobile acquires its local coordinates (X0, Y0, Z0) and associates them with the geographic coordinates (φ0, λ0, h0). For example, the geographic coordinate system of Sensor B is defined based on the World Coordinate System 1984 (WGS84).

[0662] The Cartesian coordinates (X, Y, Z) of a three-dimensional point are calculated from the geographical coordinates (Φ, λ, h) using the equations shown in (Equation 2) to (Equation 6) described later. On the other hand, the geographical coordinates (φ, λ, h) can be derived from the Cartesian coordinates (X, Y, Z) of a three-dimensional point using (Equation 7) to (Equation 10) described later. Note that other conversion methods may also be used.

[0663] In addition, there may be cases where the vehicle is unable to obtain its own location information, in which case the vehicle obtains its location information through a weighted interpolation process using available past or future location information.

[0664] Figure 99 is a diagram showing the flow of the process of acquiring a georeferenced point cloud. First, the automobile receives signals from GPS satellites (S2901). Next, the automobile calculates the current position of the automobile in a geographic coordinate system based on the received signals (S2902). Next, the automobile converts the current position in the geographic coordinate system into Cartesian coordinates (world coordinates) (S2903). This conversion is performed using the equations shown in (Equation 2) to (Equation 6) described below.

[0665] Next, the vehicle acquires the current rotation angle of the vehicle relative to the world coordinates (S2904). For example, the vehicle acquires the rotation angle by estimating the current moving direction of the vehicle using the geographic coordinates within a short time period. For example, as shown in FIG. 100, the vehicle acquires the rotation angle at multiple times (t -3 The direction of travel of the vehicle is estimated using the geographic coordinates of the vehicle (~t0).

[0666] The automobile also calculates the amount of translation (e.g., differences in the x, y, and z directions) between the local coordinate system and the world coordinate system (S2905). The automobile also calculates the rotation angle between the local coordinate system and the world coordinate system (S2906). Here, the local coordinate system is, for example, an orthogonal coordinate system with the position of the automobile as its origin.

[0667] The vehicle also receives a signal from the LiDAR (S2907). Next, the vehicle uses the received signal to calculate the position (local coordinates) of a point cloud in the vicinity (S2908).

[0668] Finally, the vehicle converts the local coordinates of the point cloud into world coordinates (S2909) using the information obtained in steps S2905 and S2906. For example, the vehicle converts the coordinates of the point cloud from the local coordinate system into the world coordinate system using the following (Equation 1).

[0669] X new =RX+t (Formula 1)

[0670] where R is the matrix of rotation angles between the local coordinate system and the world coordinate system, t is the matrix of translation between the local coordinate system and the world coordinate system, X is the coordinate of the point cloud in the local coordinate system, and X new are the coordinates of the points in the world coordinate system.

[0671] Also, the coordinate X in the world coordinate system new can be converted into geographic coordinates using, for example, (Equation 7) to (Equation 10) described later.

[0672] FIG. 101 is a diagram showing a schematic flow of the above process. As shown in FIG. 101, first, the automobile acquires GPS data and calculates the geographical coordinates (φ0, λ0, h0) of the automobile using the acquired GPS data. Next, the automobile converts the geographical coordinates (φ0, λ0, h0) into world coordinates (W x0 , W y0 , W z0 )

[0673] Next, the car converts the coordinates (X, Y, Z) of the point cloud in the local coordinate system into world coordinates (X+W x0 , Y+W y0 , Z+W z0 ) At this time, as described above, the matrix of the rotation angle between the local coordinate system and the world coordinate system is also used.

[0674] Next, the car calculates the world coordinates (X + W x0 , Y+W y0 , Z+W z0 ) into geographic coordinates (φ x , λ y , h z )

[0675] The bit streams generated by the three-dimensional data encoding device in Case A and Case B, and the operation of acquiring the geographic coordinates of the point cloud in the three-dimensional data decoding device will be described below.

[0676] In case A, the data of each 3D point group included in the bitstream generated by the 3D data encoding device is represented by local coordinates (X, Y, Z) and geographic coordinates (φ x , λ y , h z In this case, the three-dimensional data decoding device has both the local coordinates (X, Y, Z) of each three-dimensional point group and the geographic coordinates (φ x , λ y , h z In case A, the three-dimensional data decoding device does not need to perform processing to convert the coordinates of the point cloud, and therefore the processing load of the three-dimensional data decoding device can be reduced.

[0677] In Case B, the data of each 3D point cloud included in the bitstream generated by the 3D data encoding device includes local coordinates (X, Y, Z), geographical coordinates of the origin of the local coordinate system (φ0, λ0, h0), transformation parameters for converting geographical coordinates to world coordinates, and transformation parameters for converting world coordinates to geographical coordinates. In this case, the geographical coordinates of each 3D point cloud are not encoded, so the data volume of the bitstream can be reduced compared to Case A.

[0678] In this case, the three-dimensional data decoding device decodes the local coordinates (X, Y, Z) of each three-dimensional point cloud. Next, the three-dimensional data decoding device converts the geographic coordinates (φ0, λ0, h0) of the origin into world coordinates (W x0 , W y0 , W z0 ) and convert it to world coordinates (W x0 , W y0 , W z0 ) to convert the local coordinates of each 3D point cloud into world coordinates (X+W x0 , Y+W y0 , Z+W z0 ) and the three-dimensional data decoding device converts each three-dimensional point group into world coordinates (X+W x0 , Y+W y0 , Z+W z0 ) into geographic coordinates (φ x , λ y , h z )

[0679] The conversion process between Cartesian coordinates (world coordinates) and geographical coordinates will be described below. FIG. 102 is a diagram for explaining the conversion process between Cartesian coordinates and geographical coordinates. Mils are used as a unit for expressing small angles. Here, 6400 mils = 2π, and radians = 360°. Also, 1 rad = 57°.29578 = 57°17'44".8 = 3437'.75 = 206264".8 = 1018.6 mils. 1° = 0.0174533 rad, 1' = 0.000291 rad = 0.296 mil, and 1" = 0.00000485 rad. 1 mil = 0.000982 rad = 0°.0563 = 3'.37 = 202". Also, FIG. 103 is a diagram showing examples of geometric parameters of an ellipsoid.

[0680] For example, the following method is used for the conversion process: Cartesian coordinates (world coordinates) (X, Y, Z) are calculated from geographic coordinates (φ, λ, h) using (Equation 2) to (Equation 6).

[0681]

number

[0682] where υ is the radius of curvature of the prime vertical plane, and a and f are the geometric parameters of the ellipsoid.

[0683] Furthermore, for example, orthogonal coordinates (world coordinates) (X, Y, Z) can be converted into geographic coordinates (φ, λ, h) using (Equation 7) to (Equation 10).

[0684]

number

[0685] where p is the perpendicular distance.

[0686] Details of the above conversion process are described, for example, in Non-Patent Document 1. Therefore, the method described in Non-Patent Document 1 may be used.

[0687] The following describes the projection of geographic coordinates. Geographic coordinates may be projected into two-dimensional map coordinates for visualization and display. Here, the geographic coordinate system is based on an ellipsoid with a specific orientation and a specific location as its origin. This origin and ellipsoid differ depending on the region or country. Information indicating this origin and ellipsoid is called a spatial reference. For example, Singapore uses EPSG3414 as its spatial reference (SR), as shown in Figure 104, and Japan uses EPSG4947 as shown in Figure 105. The European Petroleum Survey Group (EPSG) is an organization involved in the development of surveys to which geodesy is applied.

[0688] The following describes the first mode for encoding a georeferenced point cloud. Fig. 106 is a diagram showing an example of the structure of encoded data in the first mode. As shown in Fig. 106, the geometric coordinates (X, Y, Z) that are the local coordinates of each point cloud are associated with the geographic coordinates (φ, λ, h).

[0689] FIG. 107 is a diagram showing an example of a point cloud structure in the first mode. FIG. 108 is a diagram showing an example of a point cloud tree structure. In the first mode, each of a plurality of three-dimensional point clouds is associated with a geographical coordinate. For example, the plurality of geographical coordinates are individually encoded. For example, attribute coding or octree coding can be used for this encoding. For example, the octree structure shown in FIG. 108 can be used to encode the geographical information. In this case, the three-dimensional data encoding device may encode the geographical information using information on adjacent nodes, as shown in FIG. 107. Alternatively, the three-dimensional data encoding device may consider the geographical coordinates as attribute information of the geometric coordinates and apply attribute coding to the geographical coordinates.

[0690] The geometric information of the three-dimensional point (geometric coordinates (X, Y, Z)) is data obtained, for example, by LiDAR. The geographic information (geographic coordinates) is data obtained, for example, by GPS. The purpose of encoding the geographic information is to treat the LiDAR data as the coordinates of the vehicle. In this case, there is no information indicating the location of the vehicle. However, the geographic information can be used to determine the current position of the vehicle in a global coordinate system. The determined position can then be used, for example, in applications that require the vehicle's position.

[0691] Next, a second mode for encoding a georeferenced point cloud will be described. Fig. 109 is a diagram showing an example of the structure of encoded data in the second mode. In the second mode, as shown in Fig. 109, a set of multiple 3D point clouds is associated with one geographical coordinate (φ, λ, h).

[0692] FIG. 110 is a diagram showing an example of a point cloud structure in the second mode. In the second mode, a set including multiple 3D point clouds is associated with one piece of geographic information (geographic coordinates). For example, multiple pieces of geographic information are individually coded. For example, attribute coding or octree coding can be used for this coding. For example, the octree structure shown in FIG. 108 can be used to code the geographic information.

[0693] In the second mode, multiple point groups are assigned the same geographic coordinates. That is, the decoded multiple point groups have the same geographic coordinates. For example, the geographic information is added to each independent coding unit that is small enough to have a single geographic coordinate. This unit is, for example, a tile or a slice.

[0694] For example, in the example shown in FIG. 109, the point groups P1 to P n In this case, the three-dimensional data decoding device decodes the geographical coordinates (φ1, λ1, h1) of the point group P1 and the coordinates of the point groups P1 to P n Using the geometric coordinates of other points P2 to P nFor example, the three-dimensional data decoding device can calculate the geographical coordinates of the point group P2 based on the geographical coordinates (φ1, λ1, h1) of the point group P1 and the difference between the geometric coordinates (X1, Y1, Z1) of the point group P1 and the geometric coordinates (X2, Y2, Z2) of the point group P2.

[0695] The geographic coordinates to be added are the points P1 to P n Alternatively, the added geographic coordinates may be the geographic coordinates of a grandparent node. Here, the geographic coordinates of a node are, for example, the geographic coordinates of a predetermined position (for example, the center or a corner) within the node.

[0696] Furthermore, the point to which the geographical information is added may be predetermined or variable. If variable, information indicating the point to which the geographical information is added may be added to the bitstream. Furthermor...

Claims

1. 1. A three-dimensional data encoding method for encoding a plurality of three-dimensional points, comprising: Encoding first coordinate information indicating coordinates at which the plurality of three-dimensional points exist in a first coordinate system; generating first transformation parameters for transforming the first coordinate information into second coordinate information indicating coordinates at which the plurality of three-dimensional points exist in a second coordinate system; generating second transformation parameters for transforming the second coordinate information into third coordinate information indicating coordinates at which the plurality of three-dimensional points exist in a third coordinate system; generating an encoded bitstream including the first coordinate information, the first transformation parameters, and the second transformation parameters; Three-dimensional data encoding method.

2. the first coordinate information indicates a local coordinate; The second coordinate information and the third coordinate information indicate global coordinates.

2. The three-dimensional data encoding method according to claim 1.

3. The first transformation parameters indicate coordinates in a second coordinate system of the origin of the first coordinate system.

2. The three-dimensional data encoding method according to claim 1.

4. The first transformation parameters include information indicating coordinates of the third coordinate system of the origin of the first coordinate system and parameters for transforming the third coordinate system into the second coordinate system.

2. The three-dimensional data encoding method according to claim 1.

5. the first coordinate system and the second coordinate system are Cartesian coordinate systems, The coordinates of the third coordinate system are expressed by latitude, longitude, and altitude. The three-dimensional data encoding method according to any one of claims 1 to 4.

6. The encoded bitstream further includes information indicating whether the coordinates of the third coordinate system are expressed in Cartesian coordinates. The three-dimensional data encoding method according to any one of claims 1 to 4.

7. The encoded bitstream further includes information indicating the spatial reference of the coordinates of the third coordinate system. The three-dimensional data encoding method according to any one of claims 1 to 6.

8. 1. A three-dimensional data decoding method for decoding a plurality of three-dimensional points, comprising: decoding, from the coded bitstream, first coordinate information indicating coordinates at which the plurality of three-dimensional points exist in a first coordinate system; converting the first coordinate information into second coordinate information indicating coordinates at which the plurality of three-dimensional points exist in a second coordinate system using first transformation parameters; Using a second transformation parameter, the second coordinate information is transformed into third coordinate information indicating coordinates at which the plurality of three-dimensional points exist in a third coordinate system. Three-dimensional data decoding method.

9. the first coordinate information indicates a local coordinate; The second coordinate information and the third coordinate information indicate global coordinates.

9. The three-dimensional data decoding method according to claim 8.

10. The first transformation parameters indicate coordinates in a second coordinate system of the origin of the first coordinate system.

9. The three-dimensional data decoding method according to claim 8.

11. The first transformation parameters include information indicating coordinates of the third coordinate system of the origin of the first coordinate system and parameters for transforming the third coordinate system into the second coordinate system.

9. The three-dimensional data decoding method according to claim 8.

12. the first coordinate system and the second coordinate system are Cartesian coordinate systems, The coordinates of the third coordinate system are expressed by latitude, longitude, and altitude. The three-dimensional data decoding method according to any one of claims 8 to 11.

13. The three-dimensional data decoding method further includes: and decoding, from the encoded bitstream, information indicating whether the coordinates of the third coordinate system are expressed in Cartesian coordinates. The three-dimensional data decoding method according to any one of claims 8 to 11.

14. The three-dimensional data decoding method further includes: decoding, from the encoded bitstream, information indicating the spatial reference of the coordinates in the third coordinate system; The three-dimensional data decoding method according to any one of claims 8 to 13.

15. 1. A three-dimensional data encoding device for encoding a plurality of three-dimensional points, comprising: a processor; a memory; The processor uses the memory to: Encoding first coordinate information indicating coordinates at which the plurality of three-dimensional points exist in a first coordinate system; generating first transformation parameters for transforming the first coordinate information into second coordinate information indicating coordinates at which the plurality of three-dimensional points exist in a second coordinate system; generating second transformation parameters for transforming the second coordinate information into third coordinate information indicating coordinates at which the plurality of three-dimensional points exist in a third coordinate system; generating an encoded bitstream including the first coordinate information, the first transformation parameters, and the second transformation parameters; Three-dimensional data encoding device.

16. A three-dimensional data decoding device for decoding a plurality of three-dimensional points, comprising: a processor; a memory; The processor uses the memory to: decoding, from the coded bitstream, first coordinate information indicating coordinates at which the plurality of three-dimensional points exist in a first coordinate system; converting the first coordinate information into second coordinate information indicating coordinates at which the plurality of three-dimensional points exist in a second coordinate system using first transformation parameters; Using a second transformation parameter, the second coordinate information is transformed into third coordinate information indicating coordinates at which the plurality of three-dimensional points exist in a third coordinate system. Three-dimensional data decoding device.

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