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

Abstract

To improve encoding efficiency.SOLUTION: The encoding method includes determining, from a plurality of adjacent occupancy patterns, an adjacent occupancy pattern based on occupancy statuses of adjacent nodes of a target node, determining, from a plurality of groups, a group corresponding to the determined adjacent occupancy pattern, and encoding the target node using information of the determined group, wherein each of the plurality of groups corresponds to one or more adjacent occupancy patterns. The plurality of groups include a first group and a second group, each adjacent occupancy pattern corresponding to the first group indicates a first number of occupied nodes, each adjacent occupancy pattern corresponding to the second group indicates a second number of occupied nodes that is greater than the first number, and the number of adjacent occupancy patterns corresponding to the first group is less than the number of adjacent occupancy patterns corresponding to the second group.SELECTED DRAWING: Figure 104

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 Summary of the Invention [Problem to be solved by the invention]

[0007] It is desirable to be able to reduce the amount of processing required in encoding and decoding three-dimensional data.

[0008] 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. [Means for solving the problem]

[0009] A three-dimensional data encoding method according to one embodiment of the present disclosure generates a tree structure of multiple three-dimensional points included in the three-dimensional data, generates parameters indicating referenceable nodes, determines an adjacent occupation pattern from multiple adjacent occupation patterns based on the occupation state of adjacent nodes of a target node, determines a group from multiple groups corresponding to the determined adjacent occupation pattern, and encodes the target node using information about the determined group, wherein each of the multiple groups corresponds to one or more adjacent occupation patterns, the number of the multiple groups that can be determined varies depending on the value indicated by the parameter, the multiple groups include a first group and a second group, each adjacent occupation pattern corresponding to the first group indicates a first number of occupied nodes, each adjacent occupation pattern corresponding to the second group indicates a second number of occupied nodes greater than the first number, and the number of adjacent occupation patterns corresponding to the first group is less than the number of adjacent occupation patterns corresponding to the second group.

[0010] A three-dimensional data decoding method according to one embodiment of the present disclosure acquires a tree structure of multiple three-dimensional points contained in the three-dimensional data, acquires parameters indicating referenceable nodes, determines an adjacent occupation pattern from multiple adjacent occupation patterns based on the occupation state of adjacent nodes of a target node, determines a group from multiple groups corresponding to the determined adjacent occupation pattern, and decodes the target node using information about the determined group, wherein each of the multiple groups corresponds to one or more adjacent occupation patterns, the number of the multiple groups that can be determined varies depending on the value indicated by the parameter, the multiple groups include a first group and a second group, each adjacent occupation pattern corresponding to the first group indicates a first number of occupied nodes, each adjacent occupation pattern corresponding to the second group indicates a second number of occupied nodes greater than the first number, and the number of adjacent occupation patterns corresponding to the first group is less than the number of adjacent occupation patterns corresponding to the second group. [Effects of the Invention]

[0011] 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. [Brief explanation of the drawings]

[0012] [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 showing 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 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 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 an occupancy code according to the eighth embodiment. [Figure 53] FIG. 53 is a diagram schematically illustrating the operation of the three-dimensional data encoding device according to the eighth embodiment. [Figure 54] FIG. 54 is a diagram illustrating an example of geometric information according to the eighth embodiment. [Figure 55] FIG. 55 is a diagram showing an example of selection of a coding table using geometric information according to the eighth embodiment. [Figure 56]FIG. 56 is a diagram showing an example of selection of a coding table using structure information according to the eighth embodiment. [Figure 57] FIG. 57 is a diagram showing an example of selection of a coding table using attribute information according to the eighth embodiment. [Figure 58] FIG. 58 is a diagram showing an example of selection of a coding table using attribute information according to the eighth embodiment. [Figure 59] FIG. 59 is a diagram showing an example of the structure of a bitstream according to the eighth embodiment. [Figure 60] FIG. 60 is a diagram showing an example of a coding table according to the eighth embodiment. [Figure 61] FIG. 61 is a diagram illustrating an example of a coding table according to the eighth embodiment. [Figure 62] FIG. 62 is a diagram showing an example of the structure of a bitstream according to the eighth embodiment. [Figure 63] FIG. 63 is a diagram illustrating an example of a coding table according to the eighth embodiment. [Figure 64] FIG. 64 is a diagram illustrating an example of a coding table according to the eighth embodiment. [Figure 65] FIG. 65 is a diagram illustrating an example of bit numbers of an occupancy code according to the eighth embodiment. [Figure 66] FIG. 66 is a flowchart of the encoding process using geometric information according to the eighth embodiment. [Figure 67] FIG. 67 is a flowchart of a decoding process using geometric information according to the eighth embodiment. [Figure 68] FIG. 68 is a flowchart of the encoding process using the structure information according to the eighth embodiment. [Figure 69] FIG. 69 is a flowchart of a decoding process using structure information according to the eighth embodiment. [Figure 70] FIG. 70 is a flowchart of the encoding process using attribute information according to the eighth embodiment. [Figure 71] FIG. 71 is a flowchart of a decoding process using attribute information according to the eighth embodiment. [Figure 72] FIG. 72 is a flowchart of a coding table selection process using geometric information according to the eighth embodiment. [Figure 73] FIG. 73 is a flowchart of a coding table selection process using structure information according to the eighth embodiment. [Figure 74] FIG. 74 is a flowchart of a coding table selection process using attribute information according to the eighth embodiment. [Figure 75] FIG. 75 is a block diagram of a three-dimensional data encoding device according to the eighth embodiment. [Figure 76] FIG. 76 is a block diagram of a three-dimensional data decoding device according to the eighth embodiment. [Figure 77] FIG. 77 is a diagram showing reference relationships in an octree structure according to the ninth embodiment. [Figure 78] FIG. 78 is a diagram showing reference relationships in the spatial domain according to the ninth embodiment. [Figure 79] FIG. 79 is a diagram illustrating an example of an adjacent reference node according to the ninth embodiment. [Figure 80] FIG. 80 is a diagram showing the relationship between a parent node and a node according to the ninth embodiment. [Figure 81] FIG. 81 is a diagram showing an example of an occupancy code of a parent node according to the ninth embodiment. [Figure 82] FIG. 82 is a block diagram of a three-dimensional data encoding device according to the ninth embodiment. [Figure 83] FIG. 83 is a block diagram of a three-dimensional data decoding device according to the ninth embodiment. [Figure 84] FIG. 84 is a flowchart of three-dimensional data encoding processing according to the ninth embodiment. [Figure 85] FIG. 85 is a flowchart of three-dimensional data decoding processing according to the ninth embodiment. [Figure 86] FIG. 86 is a diagram illustrating an example of switching of the coding table according to the ninth embodiment. [Figure 87] FIG. 87 shows reference relationships in the spatial domain according to the first modification of the ninth embodiment. [Figure 88] FIG. 88 is a diagram illustrating an example of the syntax of header information according to the first modification of the ninth embodiment. [Figure 89] FIG. 89 is a diagram illustrating an example of the syntax of header information according to the first modification of the ninth embodiment. [Figure 90] FIG. 90 is a diagram illustrating an example of an adjacent reference node according to the second modification of the ninth embodiment. [Figure 91] FIG. 91 is a diagram illustrating an example of a target node and adjacent nodes according to the second modification of the ninth embodiment. [Figure 92] FIG. 92 is a diagram showing reference relationships in an octree structure according to the third modification of the ninth embodiment. [Figure 93] FIG. 93 is a diagram showing reference relationships in the spatial domain according to the third modification of the ninth embodiment. [Figure 94] FIG. 94 is a diagram illustrating an example of translation according to the tenth embodiment. [Figure 95] FIG. 95 is a diagram showing an example of rotation according to the tenth embodiment. [Figure 96] FIG. 96 is a diagram showing an example of horizontal or vertical alignment according to the tenth embodiment. [Figure 97] FIG. 97 is a diagram illustrating an example of adjacent surfaces according to the tenth embodiment. [Figure 98] FIG. 98 is a diagram illustrating an example of translation according to the tenth embodiment. [Figure 99] FIG. 99 is a diagram showing an example of x-axis rotation according to the tenth embodiment. [Figure 100] FIG. 100 is a diagram showing an example of y-axis rotation according to the tenth embodiment. [Figure 101] FIG. 101 is a diagram illustrating an example of z-axis rotation according to the tenth embodiment. [Figure 102] FIG. 102 is a diagram showing an example of horizontal or vertical alignment according to the tenth embodiment. [Figure 103] FIG. 103 is a diagram illustrating an example of adjacent surfaces according to the tenth embodiment. [Figure 104]FIG. 104 is a diagram showing an example of grouping of adjacent occupation patterns according to the tenth embodiment. [Figure 105] FIG. 105 is a diagram showing an example of grouping of adjacent occupation patterns according to the tenth embodiment. [Figure 106] FIG. 106 is a diagram illustrating an example of a conversion table according to the tenth embodiment. [Figure 107] FIG. 107 is a diagram illustrating an example of a conversion table according to the tenth embodiment. [Figure 108] FIG. 108 is a diagram illustrating an outline of the mapping process according to the tenth embodiment. [Figure 109] FIG. 109 is a diagram illustrating an outline of the mapping process according to the tenth embodiment. [Figure 110] FIG. 110 is a block diagram of a three-dimensional data encoding device according to the tenth embodiment. [Figure 111] FIG. 111 is a block diagram of a three-dimensional data decoding device according to the tenth embodiment. [Figure 112] FIG. 112 is a flowchart of three-dimensional data encoding processing according to the tenth embodiment. [Figure 113] FIG. 113 is a flowchart of three-dimensional data decoding processing according to the tenth embodiment. [Figure 114] FIG. 114 is a flowchart of three-dimensional data encoding processing according to the tenth embodiment. [Figure 115] FIG. 115 is a flowchart of three-dimensional data decoding processing according to the tenth embodiment. [Figure 116] FIG. 116 is a flowchart of three-dimensional data encoding processing according to the tenth embodiment. [Figure 117] FIG. 117 is a flowchart of three-dimensional data decoding processing according to the tenth embodiment. [Figure 118] FIG. 118 is a diagram illustrating an example of grouping of adjacent occupation patterns according to the eleventh embodiment. [Figure 119] FIG. 119 is a flowchart of the coding table switching process according to the eleventh embodiment. [Figure 120] FIG. 120 is a flowchart of three-dimensional data encoding processing according to the eleventh embodiment. [Figure 121] FIG. 121 is a flowchart of three-dimensional data decoding processing according to the eleventh embodiment. [Figure 122] FIG. 122 is a diagram illustrating a redundant coding table according to the twelfth embodiment. [Figure 123] FIG. 123 shows static and dynamic coding tables according to the twelfth embodiment. [Figure 124] FIG. 124 is a diagram illustrating an example of a target node according to the twelfth embodiment. [Figure 125] FIG. 125 is a diagram showing the operation in case 1 according to the twelfth embodiment. [Figure 126] FIG. 126 is a diagram showing the operation in Case 2 according to the twelfth embodiment. [Figure 127] FIG. 127 is a diagram illustrating an example of adjacent nodes according to the twelfth embodiment. [Figure 128] FIG. 128 is a diagram showing a specific example of the number of redundant tables according to the twelfth embodiment. [Figure 129] FIG. 129 is a diagram showing a specific example of the number of redundant tables according to the twelfth embodiment. [Figure 130] FIG. 130 is a diagram showing a specific example of a redundant table according to the twelfth embodiment. [Figure 131] FIG. 131 is a diagram showing a specific example of a redundant table according to the twelfth embodiment. [Figure 132] FIG. 132 is a diagram showing an operation in which redundant tables are not deleted according to the twelfth embodiment. [Figure 133] FIG. 133 is a diagram showing an example of a coding table in which redundant tables according to the twelfth embodiment have been deleted. [Figure 134] FIG. 134 is a diagram illustrating an example of the number of coding tables when redundant tables according to the twelfth embodiment are deleted. [Figure 135]FIG. 135 is a diagram illustrating a process of generating a dynamic size coding table according to the twelfth embodiment. [Figure 136] FIG. 136 is a diagram showing an example of the size of a table according to the twelfth embodiment. [Figure 137] FIG. 137 is a flowchart of a process for generating a dynamic size coding table according to the twelfth embodiment. [Figure 138] FIG. 138 is a diagram showing an example of the size of a table according to the twelfth embodiment. [Figure 139] FIG. 139 is a flowchart of the coding table switching process according to the twelfth embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0013] A three-dimensional data encoding method according to one embodiment of the present disclosure encodes a target node included in an N-ary tree structure (N is an integer greater than or equal to 2) of multiple three-dimensional points included in three-dimensional data by encoding a first flag indicating whether the target node and its parent node refer to another node different from each other, and if the first flag indicates that the other node is to be referenced, selects an encoding table from N encoding tables according to the occupancy state of adjacent nodes of the target node, and arithmetically encodes information about the target node using the selected encoding table, and if the first flag indicates that the other node is not to be referenced, selects an encoding table from M encoding tables different from the N according to the occupancy state of adjacent nodes of the target node, and arithmetically encodes information about the target node using the selected encoding table.

[0014] This allows the number of coding tables to be reduced, thereby reducing the amount of processing. Also, by changing the number of coding tables depending on whether the target node and its parent node refer to other nodes different from each other, it is possible to set the coding tables appropriately, thereby reducing the amount of processing while suppressing a decrease in coding efficiency.

[0015] For example, the N number may be greater than the M number.

[0016] For example, when selecting a coding table from the M coding tables, a correspondence table showing the correspondence between L occupancy patterns (more than M) indicating the occupancy status of the adjacent nodes and the M coding tables may be referenced, and a coding table may be selected from the M coding tables according to the occupancy status of the adjacent nodes.

[0017] For example, when selecting a coding table from the M coding tables, a coding table may be selected from the M coding tables according to the occupancy state of the adjacent node by referring to (i) a first correspondence table showing the correspondence between L occupation patterns indicating the occupancy state of the adjacent node and I coding tables, which are less than L, and (ii) a second correspondence table showing the correspondence between the I coding tables and the M coding tables, which are less than I.

[0018] For example, when the first flag indicates that the other nodes are not referenced, the occupancy state of the adjacent node is a plurality of occupancy patterns represented by a combination of the position of the target node within the parent node and the occupancy states of three adjacent nodes within the parent node, and among the plurality of occupancy patterns, one of the three adjacent nodes is in an occupied state, and the same encoding table among the M encoding tables may be assigned to a plurality of occupancy patterns in which the occupied adjacent node is adjacent to the target node in a direction horizontal to the xy plane.

[0019] For example, when the first flag indicates that the other nodes are not referenced, the occupancy state of the adjacent node is a plurality of occupancy patterns represented by a combination of the position of the target node within the parent node and the occupancy states of three adjacent nodes within the parent node, and among the plurality of occupancy patterns, one of the three adjacent nodes is in an occupied state, and the same encoding table among the M encoding tables may be assigned to a plurality of occupancy patterns in which the occupied adjacent node is adjacent to the target node in a direction perpendicular to the xy plane.

[0020] For example, when the first flag indicates that the other nodes are not referenced, the occupancy state of the adjacent node may be a plurality of occupancy patterns represented by a combination of the position of the target node within the parent node and the occupancy states of three adjacent nodes within the parent node, and the same encoding table among the M encoding tables may be assigned to a plurality of occupancy patterns in which two of the three adjacent nodes are occupied and the plane consisting of the two occupied adjacent nodes and the target node is horizontal to the xy plane.

[0021] For example, when the first flag indicates that the other nodes are not referenced, the occupancy state of the adjacent node may be a plurality of occupancy patterns represented by a combination of the position of the target node within the parent node and the occupancy states of three adjacent nodes within the parent node, and the same encoding table among the M encoding tables may be assigned to a plurality of occupancy patterns in which two of the three adjacent nodes are occupied and the plane formed by the two occupied adjacent nodes and the target node is perpendicular to the xy plane.

[0022] A three-dimensional data decoding method according to one embodiment of the present disclosure, in decoding a target node included in an N-ary tree structure of multiple three-dimensional points included in three-dimensional data, decodes a first flag indicating whether the target node and its parent node refer to another node different from each other, and if the first flag indicates that the other node is to be referenced, selects an encoding table from N encoding tables according to the occupancy state of adjacent nodes of the target node, and arithmetically decodes information of the target node using the selected encoding table, and if the first flag indicates that the other node is not to be referenced, selects an encoding table from M encoding tables different from the N according to the occupancy state of adjacent nodes of the target node, and arithmetically decodes information of the target node using the selected encoding table.

[0023] This allows the number of coding tables to be reduced, thereby reducing the amount of processing. Also, by changing the number of coding tables depending on whether the target node and its parent node refer to other nodes different from each other, it is possible to set the coding tables appropriately, thereby reducing the amount of processing while suppressing a decrease in coding efficiency.

[0024] For example, the N number may be greater than the M number.

[0025] For example, when selecting a coding table from the M coding tables, a correspondence table showing the correspondence between L (more than M) occupancy patterns indicating the occupancy status of adjacent nodes and the M coding tables may be referenced, and a coding table may be selected from the M coding tables according to the occupancy status of adjacent nodes of the target node.

[0026] For example, when selecting a coding table from the M coding tables, the coding table may be selected from the M coding tables according to the occupancy status of the adjacent nodes of the target node by referring to (i) a first correspondence table showing the correspondence between L occupation patterns indicating the occupancy status of adjacent nodes and I coding tables, which are less than L, and (ii) a second correspondence table showing the correspondence between the I coding tables and the M coding tables, which are less than I.

[0027] For example, when the first flag indicates that the other nodes are not referenced, the occupancy state of the adjacent node is a plurality of occupancy patterns represented by a combination of the position of the target node within the parent node and the occupancy states of three adjacent nodes within the parent node, and among the plurality of occupancy patterns, one of the three adjacent nodes is in an occupied state, and the same encoding table among the M encoding tables may be assigned to a plurality of occupancy patterns in which the occupied adjacent node is adjacent to the target node in a direction horizontal to the xy plane.

[0028] For example, when the first flag indicates that the other nodes are not referenced, the occupancy state of the adjacent node is a plurality of occupancy patterns represented by a combination of the position of the target node within the parent node and the occupancy states of three adjacent nodes within the parent node, and among the plurality of occupancy patterns, one of the three adjacent nodes is in an occupied state, and the same encoding table among the M encoding tables may be assigned to a plurality of occupancy patterns in which the occupied adjacent node is adjacent to the target node in a direction perpendicular to the xy plane.

[0029] For example, when the first flag indicates that the other nodes are not referenced, the occupancy state of the adjacent node may be a plurality of occupancy patterns represented by a combination of the position of the target node within the parent node and the occupancy states of three adjacent nodes within the parent node, and the same encoding table among the M encoding tables may be assigned to a plurality of occupancy patterns in which two of the three adjacent nodes are occupied and the plane consisting of the two occupied adjacent nodes and the target node is horizontal to the xy plane.

[0030] For example, when the first flag indicates that the other nodes are not referenced, the occupancy state of the adjacent node may be a plurality of occupancy patterns represented by a combination of the position of the target node within the parent node and the occupancy states of three adjacent nodes within the parent node, and the same encoding table among the M encoding tables may be assigned to a plurality of occupancy patterns in which two of the three adjacent nodes are occupied and the plane formed by the two occupied adjacent nodes and the target node is perpendicular to the xy plane.

[0031] Furthermore, a three-dimensional data encoding device according to one embodiment of the present disclosure is a three-dimensional data encoding device that encodes a plurality of three-dimensional points having attribute information, and includes a processor and a memory. The processor may use the memory to encode a first flag indicating whether or not a target node included in an N-ary tree structure of a plurality of three-dimensional points included in the three-dimensional data references another node whose parent node is different from the target node, and when the first flag indicates that the other node references the other node, select an encoding table from N encoding tables according to the occupancy state of adjacent nodes of the target node, and arithmetically encode information of the target node using the selected encoding table. When the first flag indicates that the other node is not referenced, select an encoding table from M encoding tables different from N according to the occupancy state of adjacent nodes of the target node, and arithmetically encode information of the target node using the selected encoding table.

[0032] This allows the number of coding tables to be reduced, thereby reducing the amount of processing. Also, by changing the number of coding tables depending on whether the target node and its parent node refer to other nodes different from each other, it is possible to set the coding tables appropriately, thereby reducing the amount of processing while suppressing a decrease in coding efficiency.

[0033] Furthermore, a three-dimensional data decoding device according to one embodiment of the present disclosure is a three-dimensional data decoding device that decodes a plurality of three-dimensional points having attribute information, and includes a processor and a memory. The processor uses the memory to decode a target node included in an N-ary tree structure of a plurality of three-dimensional points included in the three-dimensional data, by decoding a first flag that indicates whether the target node and its parent node refer to another node different from the target node, and if the first flag indicates that the other node is to be referenced, selects an encoding table from N encoding tables according to the occupancy state of adjacent nodes of the target node, and arithmetically decodes the information of the target node using the selected encoding table. If the first flag indicates that the other node is not to be referenced, selects an encoding table from M encoding tables different from N according to the occupancy state of adjacent nodes of the target node, and arithmetically decodes the information of the target node using the selected encoding table.

[0034] This allows the number of coding tables to be reduced, thereby reducing the amount of processing. Also, by changing the number of coding tables depending on whether the target node and its parent node refer to other nodes different from each other, it is possible to set the coding tables appropriately, thereby reducing the amount of processing while suppressing a decrease in coding efficiency.

[0035] 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.

[0036] 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.

[0037] (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.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] 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.

[0046] 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.

[0047] 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.

[0048] 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).

[0049] 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.

[0050] 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.

[0051] 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.

[0052] 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.

[0053] 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.

[0054] 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.

[0055] 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.

[0056] 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.

[0057] 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.

[0058] Next, we will explain how to handle static and dynamic objects.

[0059] 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.

[0060] 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.

[0061] 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.

[0062] 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.

[0063] 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.

[0064] 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.

[0065] 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.

[0066] The encoding device may also encode static objects and dynamic objects as different streams.

[0067] 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.

[0068] 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.

[0069] 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.

[0070] 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).

[0071] 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.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] As shown in FIG. 7, first, the acquisition unit 101 acquires three-dimensional data 111, which is point cloud data (S101).

[0078] 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.

[0079] 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.

[0080] Next, the encoding unit 104 generates encoded three-dimensional data 112 by sequentially encoding the plurality of SPCs in each GOS (S104).

[0081] 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.

[0082] 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.

[0083] 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).

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] The meta information may also include information indicating the range of the spatial region occupied by the world.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] A modification of this embodiment will now be described.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] Furthermore, when decoding coded data that has been coded hierarchically in space, the decoding device may decode only the lower layers.

[0114] The decoding device may also decode data preferentially from the lowest layer depending on the zoom factor or purpose of the map.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.).

[0136] Also, encoding or decoding is performed in units of GOS, each GOS including one or more spaces.

[0137] 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.

[0138] 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.

[0139] Furthermore, the encoding device and the decoding device transmit or receive the encoded stream in units of worlds each including one or more GOSs.

[0140] 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.

[0141] 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.

[0142] Furthermore, the encoding device and the decoding device encode or decode two or more different spaces or GOSs in parallel.

[0143] The encoding device and decoding device also encode and decode spatial information (coordinates, size, etc.) of the space or GOS.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] (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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The SHOT feature is obtained by dividing the VXL area, 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Next, we will explain how to switch between sending and receiving data in the sparse world (SWLD) and the world (WLD).

[0166] 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 the 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 the WLD from the server (S324). This allows the client to acquire appropriate map information depending on the client's network bandwidth.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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 .

[0191] 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.

[0192] 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).

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] A modification of this embodiment will now be described.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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).

[0205] Furthermore, the following method may be used as a method for updating the WLD or SWLD.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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).

[0213] 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.

[0214] 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.

[0215] 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.

[0216] Furthermore, in the first encoding method, of intra prediction and inter prediction, inter prediction is given priority over the second encoding method.

[0217] 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.

[0218] 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.

[0219] 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).

[0220] 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.

[0221] 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.

[0222] 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 .

[0223] 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 .

[0224] 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.

[0225] This allows the three-dimensional data encoding device 400 to generate encoded three-dimensional data 414 that includes data required by the decoding device.

[0226] 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.

[0227] This allows the three-dimensional data encoding device 400 to transmit appropriate data depending on the state of the client.

[0228] 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.

[0229] 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.

[0230] This allows the three-dimensional data encoding device 400 to transmit appropriate data in response to a client request.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] Furthermore, in the first decoding method, of intra prediction and inter prediction, inter prediction is given priority over the second decoding method.

[0235] 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.

[0236] 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.

[0237] 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).

[0238] 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.

[0239] 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.

[0240] 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.

[0241] This allows the three-dimensional data decoding device 500 to receive appropriate data depending on the state of the client.

[0242] 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.

[0243] 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.

[0244] This allows the three-dimensional data decoding device 500 to receive appropriate data according to the application.

[0245] (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.

[0246] 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.

[0247] 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 .

[0248] 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.

[0249] 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.

[0250] 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.

[0251] Next, a description will be given of the configuration and operation of three-dimensional data transmission device 640 according to this embodiment.

[0252] 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.

[0253] 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 .

[0254] 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.

[0255] 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.

[0256] 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.

[0257] (Fourth embodiment) In this embodiment, an abnormal operation in self-location estimation based on a three-dimensional map will be described.

[0258] 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.

[0259] 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.

[0260] 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.

[0261] 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.

[0262] 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.

[0263] 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.

[0264] (1) Three-dimensional maps cannot be obtained via communication.

[0265] (2) The 3D map does not exist, or the 3D map is obtained but is corrupted.

[0266] (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.

[0267] 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.

[0268] 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.

[0269] 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.

[0270] 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.

[0271] 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.

[0272] 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.

[0273] 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.

[0274] 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.

[0275] On the other hand, if no abnormal case is detected, the three-dimensional information processing apparatus 700 ends the process.

[0276] 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.

[0277] 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.

[0278] 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.

[0279] 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.

[0280] 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.

[0281] (Embodiment 5) In this embodiment, a method of transmitting three-dimensional data to a following vehicle will be described.

[0282] 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.

[0283] 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.

[0284] 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.

[0285] 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.

[0286] 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.

[0287] 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.

[0288] 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.

[0289] 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.

[0290] 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.

[0291] The three-dimensional data storage unit 818 stores the generated three-dimensional data 835 and the like.

[0292] 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.

[0293] 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.

[0294] 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.

[0295] 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.

[0296] 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.

[0297] 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.

[0298] 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.

[0299] 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.

[0300] (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.

[0301] 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.

[0302] 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.

[0303] 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.

[0304] 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.

[0305] 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.

[0306] 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.

[0307] 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.

[0308] 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.

[0309] 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.

[0310] 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.

[0311] 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.

[0312] 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.

[0313] 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.

[0314] 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.

[0315] 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.

[0316] 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.

[0317] 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.

[0318] 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.

[0319] 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.

[0320] 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.

[0321] 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.

[0322] 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.

[0323] The three-dimensional data storage unit 1018 stores a three-dimensional map 1032, three-dimensional data 1034, three-dimensional data 1035, and the like.

[0324] 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.

[0325] 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.

[0326] The transmission control unit 1021 exchanges information such as compatible formats with the communication destination via the communication unit 1020, and establishes communication.

[0327] 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.

[0328] 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.

[0329] 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.

[0330] 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.

[0331] 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 .

[0332] The reception control unit 1113 exchanges information such as compatible formats with the communication destination via the communication unit 1112, and establishes communication.

[0333] 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.

[0334] 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.

[0335] 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.

[0336] The three-dimensional data storage unit 1118 stores a three-dimensional map 1135 and the like.

[0337] 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.

[0338] 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 .

[0339] The transmission control unit 1121 exchanges information such as compatible formats with the communication destination via the communication unit 1120, and establishes communication.

[0340] 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.

[0341] 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.

[0342] 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.

[0343] 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).

[0344] 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).

[0345] 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.

[0346] 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).

[0347] 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.

[0348] A modification of this embodiment will now be described.

[0349] 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.

[0350] 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.

[0351] 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.

[0352] 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.

[0353] 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.

[0354] 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.

[0355] 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.

[0356] 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.

[0357] 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.

[0358] 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.

[0359] 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.

[0360] 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.

[0361] 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.

[0362] The sensor information 1033 also includes information indicating the performance of the sensor.

[0363] 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.

[0364] For example, the client device 902 includes a processor and a memory, and the processor uses the memory to perform the above-described processing.

[0365] 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.

[0366] 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.

[0367] Furthermore, the server 901 further transmits a request to the client device 902 to transmit the sensor information.

[0368] 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.

[0369] 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.

[0370] The sensor information 1037 also includes information indicating the performance of the sensor.

[0371] 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.

[0372] 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.

[0373] 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.

[0374] For example, the server 901 includes a processor and a memory, and the processor uses the memory to perform the above-mentioned processing.

[0375] (Embodiment 7) In this embodiment, a method for encoding and decoding three-dimensional data using inter prediction processing will be described.

[0376] 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.

[0377] 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.

[0378] 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.

[0379] 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).

[0380] 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.

[0381] 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.

[0382] 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.

[0383] 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.

[0384] 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.

[0385] 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.

[0386] 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.

[0387] 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.

[0388] 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.

[0389] 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.

[0390] 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.

[0391] 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.

[0392] 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.

[0393] 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.

[0394] 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.

[0395] 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.

[0396] 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.

[0397] 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.

[0398] 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.

[0399] 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.

[0400] 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.

[0401] 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.

[0402] 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.

[0403] 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.

[0404] 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.

[0405] 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.

[0406] 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.

[0407] 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.

[0408] 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.

[0409] 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.

[0410] 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.

[0411] 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 and the reference space in which the number of voxels or point clouds has been thinned.

[0412] 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.

[0413] 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.

[0414] Furthermore, when the three-dimensional data includes attribute information such as shape or color, the inter prediction unit 1311 searches, for example, in the reference space for a volume having attribute information such as shape or color closest to that of the volume to be coded within the coding space, as a prediction volume for the volume to be coded within the coding space. This reference space is, for example, the reference space after the above-described rotation and translation processes have 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 within 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 performed by the conversion unit 1303 and subsequent processes. 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.

[0415] 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.

[0416] 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.

[0417] 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.

[0418] 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.

[0419] 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.

[0420] 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.

[0421] 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.

[0422] 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.

[0423] 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.

[0424] 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.

[0425] 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.

[0426] 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.

[0427] 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.

[0428] 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.

[0429] These modifications can also be applied to the three-dimensional data decoding device 1400 in the same manner.

[0430] As described above, the three-dimensional data encoding device 1300 according to this embodiment performs the following processes. FIG.

[0431] 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.

[0432] 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.

[0433] 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.

[0434] 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.

[0435] 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.

[0436] 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).

[0437] 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).

[0438] 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).

[0439] 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.

[0440] 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.

[0441] 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.

[0442] 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.

[0443] 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.

[0444] 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.

[0445] FIG. 48 is a flowchart of the inter prediction process performed by the 3D data decoding device 1400.

[0446] 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).

[0447] 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.

[0448] Next, the three-dimensional data decoding device 1400 performs inverse quantization and inverse transformation on the decoded differential attribute information (S1402).

[0449] 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.

[0450] 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.

[0451] 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.

[0452] 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.

[0453] 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.

[0454] 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).

[0455] 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).

[0456] 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).

[0457] 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.

[0458] 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.

[0459] (Embodiment 8) In this embodiment, adaptive entropy coding (arithmetic coding) for occupancy codes of octtrees will be described.

[0460] Fig. 51 is a diagram showing an example of a quadtree tree structure. Fig. 52 is a diagram showing an occupancy code of the tree structure shown in Fig. 51. Fig. 53 is a diagram schematically showing the operation of the three-dimensional data encoding device according to this embodiment.

[0461] The three-dimensional data encoding device according to this embodiment performs entropy encoding of 8-bit occupancy encoding in an octree. The three-dimensional data encoding device updates the encoding table during the entropy encoding process of the occupancy code. The three-dimensional data encoding device does not use a single encoding table, but instead uses adaptive encoding tables to utilize similarity information between three-dimensional points. In other words, the three-dimensional data encoding device uses multiple encoding tables.

[0462] The similarity information is, for example, geometric information of the three-dimensional points, structural information of the octree, or attribute information of the three-dimensional points.

[0463] 51 to 53 show an example of a quadtree, but a similar technique may be applied to an N-ary tree such as a binary tree, an octave tree, a hexadecimal tree, etc. For example, a three-dimensional data encoding device performs entropy encoding using an adaptive table (also called an encoding table) on an 8-bit occupancy code for an octave tree, a 4-bit occupancy code for a quad tree, and a 16-bit occupancy code for a hexadecimal tree.

[0464] The following describes adaptive entropy coding processing using geometry information of three-dimensional points (point cloud).

[0465] For two nodes in a tree structure, if the geometric arrangement of the periphery of each node is similar, there is a possibility that the occupancy state of the child node (i.e., whether or not a three-dimensional point is included) will be similar. Therefore, the three-dimensional data encoding device performs grouping using the geometric arrangement of the periphery of the parent node. This allows the three-dimensional data encoding device to group the occupancy state of the child node and use a different encoding table for each group. This can improve the encoding efficiency of entropy encoding.

[0466] FIG. 54 is a diagram showing an example of geometric information. The geometric information includes information indicating whether each of multiple adjacent nodes of a target node is occupied (i.e., whether it contains a three-dimensional point). For example, a three-dimensional data encoding device calculates the geometric arrangement (Local geometry) around the target node using information on whether adjacent nodes contain a three-dimensional point (occupied or non-occupied). An adjacent node is, for example, a node that exists spatially around the target node, or a node that exists at the same position as the target node at a different time, or a node that exists spatially around the target node.

[0467] In Figure 54, hatched cubes indicate target nodes to be coded. White cubes indicate adjacent nodes that contain three-dimensional points. In Figure 54, the geometric pattern shown in (2) represents a rotated version of the geometric pattern shown in (1). Therefore, the three-dimensional data coding device determines that these geometric patterns have high geometric similarity, and performs entropy coding on these geometric patterns using the same coding table. Furthermore, the three-dimensional data coding device determines that the geometric similarity on the geometric patterns (3) and (4) is low, and performs entropy coding on them using a different coding table.

[0468] Figure 55 is a diagram showing examples of occupancy codes for target nodes in the geometric patterns (1) to (4) shown in Figure 54, and coding tables used for entropy coding. As described above, the three-dimensional data coding device determines that geometric patterns (1) and (2) belong to the same geometric group, and uses the same coding table A. Furthermore, the three-dimensional data coding device uses coding table B and coding table C for geometric patterns (3) and (4), respectively.

[0469] Furthermore, as shown in FIG. 55, there are cases where the occupancy codes of the target nodes of geometric patterns (1) and (2) included in the same geometric group are the same.

[0470] Next, adaptive entropy coding processing using structure information of a tree structure will be described. For example, the structure information includes information indicating the layer to which the target node belongs.

[0471] FIG. 56 is a diagram showing an example of a tree structure. In general, the shape of a local object depends on the search scale. For example, in a tree structure, lower layers tend to be sparser than upper layers. Therefore, a three-dimensional data encoding device can improve the encoding efficiency of entropy encoding by using different encoding tables for the upper and lower layers, as shown in FIG. 56.

[0472] That is, the three-dimensional data encoding device may use a different encoding table for each layer when encoding the occupancy code of each layer. For example, for the tree structure shown in Fig. 56, the three-dimensional data encoding device may perform entropy encoding using the encoding table for layer N when encoding the occupancy code of layer N (N = 0 to 6). This allows the three-dimensional data encoding device to switch encoding tables according to the appearance pattern of the occupancy code for each layer, thereby improving encoding efficiency.

[0473] Furthermore, as shown in Figure 56, the three-dimensional data encoding device may use coding table A for the occupancy codes from layer 0 to layer 2, and coding table B for the occupancy codes from layer 3 to layer 6. This allows the three-dimensional data encoding device to switch coding tables according to the appearance pattern of the occupancy codes for each layer group, thereby improving coding efficiency. Furthermore, the three-dimensional data encoding device may add information about the coding table used in each layer to the header of the bitstream. Alternatively, the coding table used in each layer may be predetermined by a standard or the like.

[0474] Next, we will explain adaptive entropy coding processing using property information of 3D points. For example, the property information includes information about an object including a target node or information about a normal vector held by the target node.

[0475] Attribute information of 3D points can be used to group 3D points with similar geometric arrangements. For example, a normal vector representing the direction of each 3D point can be used as common attribute information of 3D points. By using the normal vector, it is possible to find geometric arrangements associated with similar occupancy codes in the tree structure.

[0476] Furthermore, color or reflectance (reflectance) may be used as attribute information. For example, the three-dimensional data encoding device may group three-dimensional points having similar geometric arrangements using the color or reflectance of the three-dimensional points, and perform processing such as switching encoding tables for each group.

[0477] Fig. 57 is a diagram for explaining switching of encoding tables based on normal vectors. As shown in Fig. 57, when the normal vector of a target node belongs to a different normal vector group, a different encoding table is used. For example, normal vectors included in a predetermined range are classified into one normal vector group.

[0478] Furthermore, if the classification of the object is different, it is highly likely that the occupancy code will also be different. Therefore, the three-dimensional data encoding device may select an encoding table according to the classification of the object to which the target node belongs. Figure 58 is a diagram for explaining switching of encoding tables based on the classification of the object. As shown in Figure 58, if the classification of the object is different, a different encoding table is used.

[0479] An example of the structure of a bitstream according to this embodiment will be described below. Fig. 59 is a diagram showing an example of the structure of a bitstream generated by a three-dimensional data encoding device according to this embodiment. As shown in Fig. 59, the bitstream includes a coding table group, a table index, and a coding occupancy. The coding table group includes a plurality of coding tables.

[0480] The table index is an index indicating the coding table used for entropy coding of the subsequent coding occupancy. The coding occupancy is the occupancy code after entropy coding. As shown in Figure 59, the bitstream includes multiple pairs of table index and coding occupancy.

[0481] For example, in the example shown in Fig. 59, coding occupancy 0 is data that has been entropy coded using a context model (hereinafter also referred to as a context) indicated by table index 0. Also, coding occupancy 1 is data that has been entropy coded using a context indicated by table index 1. Also, a context for coding coding occupancy 0 may be defined in advance in a standard or the like, and the three-dimensional data decoding device may use that context when decoding coding occupancy 0. This eliminates the need to add a table index to the bitstream, thereby reducing overhead.

[0482] The three-dimensional data encoding device may also add information for initializing each context to the header.

[0483] The three-dimensional data encoding device determines an encoding table using geometric information, structural information, or attribute information of the target node, and encodes the occupancy code using the determined encoding table. The three-dimensional data encoding device adds the encoding result and information about the encoding table used for encoding (such as a table index) to a bitstream, and transmits the bitstream to the three-dimensional data decoding device. This allows the three-dimensional data decoding device to decode the occupancy code using the encoding table information added to the header.

[0484] Alternatively, the three-dimensional data encoding device may not add information about the encoding table used for encoding to the bitstream, and the three-dimensional data decoding device may determine the encoding table using the geometric information, structural information, or attribute information of the target node after decoding in the same manner as the three-dimensional data encoding device, and decode the occupancy code using the determined encoding table. This eliminates the need to add information about the encoding table to the bitstream, thereby reducing overhead.

[0485] 60 and 61 are diagrams showing examples of encoding tables. As shown in Fig. 60 and 61, one encoding table indicates, for each value of an 8-bit occupancy code, the context model and context model type corresponding to that value.

[0486] As shown in the coding table of Fig. 60, the same context model (context) may be applied to multiple occupancy codes. Alternatively, a different context model may be assigned to each occupancy code. This allows the context model to be assigned according to the occurrence probability of the occupancy code, thereby improving coding efficiency.

[0487] The context model type indicates, for example, whether the context model is a context model that updates the probability table according to the frequency of appearance of the occupancy code, or a context model that has a fixed probability table.

[0488] Next, another example of a bitstream and a coding table will be shown. Fig. 62 is a diagram showing a configuration example of a modified bitstream. As shown in Fig. 62, the bitstream includes a coding table group and a coding occupancy. The coding table group includes a plurality of coding tables.

[0489] 63 and 64 are diagrams showing examples of coding tables. As shown in Fig. 63 and 64, one coding table indicates, for each bit included in the occupancy code, a context model and a context model type corresponding to that bit.

[0490] FIG. 65 is a diagram showing an example of the relationship between occupancy codes and bit numbers of the occupancy codes.

[0491] In this way, the three-dimensional data encoding device may treat the occupancy code as binary data, assign a different context model to each bit, and perform entropy encoding of the occupancy code. This allows the context model to be assigned according to the occurrence probability of each bit of the occupancy code, thereby improving encoding efficiency.

[0492] Specifically, each bit of the occupancy code corresponds to a sub-block obtained by dividing the spatial block corresponding to the target node. Therefore, coding efficiency can be improved when sub-blocks at the same spatial position within a block have similar trends. For example, if the surface of the ground or road crosses the block, in the octree, the bottom four blocks contain 3D points, while the top four blocks do not. Furthermore, similar patterns appear in multiple blocks arranged horizontally. Therefore, coding efficiency can be improved by switching the context for each bit as described above.

[0493] Alternatively, a context model may be used in which the probability table is updated according to the frequency of occurrence of each bit of the occupancy code, or a context model in which the probability table is fixed.

[0494] Next, the flow of the three-dimensional data encoding process and the three-dimensional data decoding process according to this embodiment will be described.

[0495] FIG. 66 is a flowchart of a three-dimensional data encoding process including an adaptive entropy encoding process using geometric information.

[0496] In the decomposition process, an octree is generated from the initial bounding box of the 3D points. The bounding box is divided according to the positions of the 3D points within the bounding box. Specifically, non-empty subspaces are further divided. Next, information indicating whether the subspace contains a 3D point is encoded into an occupancy code. Note that similar processing is performed in the processes shown in Figures 68 and 70.

[0497] First, the three-dimensional data encoding device acquires the input three-dimensional points (S1901), and then determines whether the decomposition process for the unit length is complete (S1902).

[0498] If the decomposition process of the unit length has not been completed (No in S1902), the three-dimensional data encoding device generates an octree by performing the decomposition process on the target node (S1903).

[0499] Next, the three-dimensional data encoding device acquires geometric information (S1904), and selects an encoding table based on the acquired geometric information (S1905). Here, the geometric information is, for example, information indicating the geometric arrangement of the occupancy state of the surrounding blocks of the target node, as described above.

[0500] Next, the three-dimensional data encoding device entropy encodes the occupancy code of the target node using the selected encoding table (S1906).

[0501] The processes of steps S1903 to S1906 are repeated until the decomposition process of the unit lengths is completed. When the decomposition process of the unit lengths is completed (Yes in S1902), the three-dimensional data encoding device outputs a bit stream including the generated information (S1907).

[0502] The three-dimensional data encoding device determines an encoding table using geometric information, structural information, or attribute information of the target node, and encodes the bit string of the occupancy code using the determined encoding table. The three-dimensional data encoding device adds the encoding result and information about the encoding table used for encoding (such as a table index) to a bit stream, and transmits the bit stream to the three-dimensional data decoding device. This allows the three-dimensional data decoding device to decode the occupancy code using the encoding table information added to the header.

[0503] Alternatively, the three-dimensional data encoding device may not add information about the encoding table used for encoding to the bitstream, and the three-dimensional data decoding device may determine the encoding table using the geometric information, structural information, or attribute information of the target node after decoding in the same manner as the three-dimensional data encoding device, and decode the occupancy code using the determined encoding table. This eliminates the need to add information about the encoding table to the bitstream, thereby reducing overhead.

[0504] FIG. 67 is a flowchart of three-dimensional data decoding processing including adaptive entropy decoding processing using geometric information.

[0505] The decomposition process included in the decoding process is similar to the decomposition process included in the encoding process described above, but differs in the following respects. The three-dimensional data decoding device divides the initial bounding box using the decoded occupancy code. When the three-dimensional data decoding device has completed processing for the unit length, it saves the position of the bounding box as a three-dimensional point and position. Note that similar processing is also performed in the processes shown in Figures 69 and 71.

[0506] First, the three-dimensional data decoding device acquires the input bit stream (S1911), and then determines whether the decomposition process for the unit length is complete (S1912).

[0507] If the decomposition process of the unit length has not been completed (No in S1912), the three-dimensional data decoding device generates an octree by performing the decomposition process on the target node (S1913).

[0508] Next, the three-dimensional data decoding device acquires geometric information (S1914), and selects an encoding table based on the acquired geometric information (S1915). Here, the geometric information is, for example, information indicating the geometric arrangement of the occupancy state of the peripheral blocks of the target node, as described above.

[0509] Next, the three-dimensional data decoding device entropy decodes the occupancy code of the target node using the selected encoding table (S1916).

[0510] The processes of steps S1913 to S1916 are repeated until the decomposition process of the unit length is completed. When the decomposition process of the unit length is completed (Yes in S1912), the three-dimensional data decoding device outputs the three-dimensional point (S1917).

[0511] FIG. 68 is a flowchart of a three-dimensional data encoding process including an adaptive entropy encoding process using structural information.

[0512] First, the three-dimensional data encoding device acquires the input three-dimensional points (S1921), and then determines whether the decomposition process for the unit length is complete (S1922).

[0513] If the decomposition process of the unit length has not been completed (No in S1922), the three-dimensional data encoding device generates an octree by performing the decomposition process on the target node (S1923).

[0514] Next, the three-dimensional data encoding device acquires structural information (S1924) and selects an encoding table based on the acquired structural information (S1925). Here, structural information is, for example, information indicating the layer to which the target node belongs, as described above.

[0515] Next, the three-dimensional data encoding device entropy encodes the occupancy code of the target node using the selected encoding table (S1926).

[0516] The processes of steps S1923 to S1926 are repeated until the decomposition process of the unit lengths is completed. When the decomposition process of the unit lengths is completed (Yes in S1922), the three-dimensional data encoding device outputs a bit stream including the generated information (S1927).

[0517] FIG. 69 is a flowchart of three-dimensional data decoding processing including adaptive entropy decoding processing using structural information.

[0518] First, the three-dimensional data decoding device acquires the input bit stream (S1931). Next, the three-dimensional data decoding device determines whether the decomposition process for the unit length is complete (S1932).

[0519] If the decomposition process of the unit length has not been completed (No in S1932), the three-dimensional data decoding device generates an octree by performing the decomposition process on the target node (S1933).

[0520] Next, the three-dimensional data decoding device acquires structural information (S1934), and selects an encoding table based on the acquired structural information (S1935). Here, structural information is, for example, information indicating the layer to which the target node belongs, as described above.

[0521] Next, the three-dimensional data decoding device entropy decodes the occupancy code of the target node using the selected encoding table (S1936).

[0522] The processes of steps S1933 to S1936 are repeated until the decomposition process of the unit length is completed. When the decomposition process of the unit length is completed (Yes in S1932), the three-dimensional data decoding device outputs the three-dimensional point (S1937).

[0523] FIG. 70 is a flowchart of three-dimensional data encoding processing including adaptive entropy encoding processing using attribute information.

[0524] First, the three-dimensional data encoding device acquires the input three-dimensional points (S1941), and then determines whether the decomposition process for the unit length is complete (S1942).

[0525] If the decomposition process of the unit length has not been completed (No in S1942), the three-dimensional data encoding device generates an octree by performing the decomposition process on the target node (S1943).

[0526] Next, the three-dimensional data encoding device acquires attribute information (S1944) and selects an encoding table based on the acquired attribute information (S1945). Here, the attribute information is, for example, information indicating the normal vector of the target node, as described above.

[0527] Next, the three-dimensional data encoding device entropy encodes the occupancy code of the target node using the selected encoding table (S1946).

[0528] The processes of steps S1943 to S1946 are repeated until the decomposition process of the unit lengths is completed. When the decomposition process of the unit lengths is completed (Yes in S1942), the three-dimensional data encoding device outputs a bit stream including the generated information (S1947).

[0529] FIG. 71 is a flowchart of three-dimensional data decoding processing including adaptive entropy decoding processing using attribute information.

[0530] First, the three-dimensional data decoding device acquires the input bit stream (S1951). Next, the three-dimensional data decoding device determines whether or not the decomposition process for the unit length is completed (S1952).

[0531] If the decomposition process of the unit length has not been completed (No in S1952), the three-dimensional data decoding device generates an octree by performing the decomposition process on the target node (S1953).

[0532] Next, the three-dimensional data decoding device acquires attribute information (S1954), and selects an encoding table based on the acquired attribute information (S1955). Here, the attribute information is, for example, information indicating the normal vector of the target node, as described above.

[0533] Next, the three-dimensional data decoding device entropy decodes the occupancy code of the target node using the selected encoding table (S1956).

[0534] The processes of steps S1953 to S1956 are repeated until the decomposition process of the unit length is completed. When the decomposition process of the unit length is completed (Yes in S1952), the three-dimensional data decoding device outputs the three-dimensional point (S1957).

[0535] FIG. 72 is a flowchart of the process of selecting an encoding table using geometric information (S1905).

[0536] The three-dimensional data encoding device may switch the encoding table used for entropy encoding of the occupancy code by using, for example, information on a tree-structured geometric group as geometric information. Here, the information on the geometric group indicates the geometric group that includes the geometric pattern of the target node.

[0537] 72, if the geometry group indicated by the geometry information is geometry group 0 (Yes in S1961), the three-dimensional data encoding device selects encoding table 0 (S1962). If the geometry group indicated by the geometry information is geometry group 1 (Yes in S1963), the three-dimensional data encoding device selects encoding table 1 (S1964). Otherwise (No in S1963), the three-dimensional data encoding device selects encoding table 2 (S1965).

[0538] The method of selecting the encoding table is not limited to the above. For example, the three-dimensional data encoding device may further switch the encoding table depending on the value of the geometry group, such as using encoding table 2 when the geometry group indicated by the geometry information is geometry group 2.

[0539] For example, a geometric group is determined using occupancy information indicating whether a point group is included in a node adjacent to a target node. Furthermore, geometric patterns that have the same shape when a transformation such as rotation is applied may be included in the same geometric group. Furthermore, the three-dimensional data encoding device may select a geometric group using occupancy information of nodes adjacent to or located around the target node and belonging to the same layer as the target node. Furthermore, the three-dimensional data encoding device may select a geometric group using occupancy information of nodes belonging to a different layer from the target node. For example, the three-dimensional data encoding device may select a geometric group using occupancy information of a parent node, or nodes adjacent to or located around the parent node.

[0540] The process of selecting an encoding table using geometric information in the three-dimensional data decoding device (S1915) is similar to the above.

[0541] FIG. 73 is a flowchart of the process of selecting an encoding table using structure information (S1925).

[0542] The three-dimensional data encoding device may switch the encoding table used for entropy encoding of the occupancy code by using, for example, information about a layer of a tree structure as structural information. Here, the information about the layer indicates, for example, the layer to which the target node belongs.

[0543] 73, if the target node belongs to layer 0 (Yes in S1971), the three-dimensional data encoding device selects encoding table 0 (S1972). If the target node belongs to layer 1 (Yes in S1973), the three-dimensional data encoding device selects encoding table 1 (S1974). Otherwise (No in S1973), the three-dimensional data encoding device selects encoding table 2 (S1975).

[0544] The method for selecting the encoding table is not limited to the above. For example, the three-dimensional data encoding device may further switch the encoding table depending on the layer to which the target node belongs, such as using encoding table 2 when the target node belongs to layer 2.

[0545] The process of selecting an encoding table using structural information in the three-dimensional data decoding device (S1935) is also the same as above.

[0546] FIG. 74 is a flowchart of the process of selecting an encoding table using attribute information (S1945).

[0547] The three-dimensional data encoding device may switch the encoding table used for entropy encoding of the occupancy code using, for example, information about the object to which the target node belongs or information about the normal vector of the target node as attribute information.

[0548] 74, if the normal vector of the target node belongs to normal vector group 0 (Yes in S1981), the three-dimensional data encoding device selects encoding table 0 (S1982). If the normal vector of the target node belongs to normal vector group 1 (Yes in S1983), the three-dimensional data encoding device selects encoding table 1 (S1984). Otherwise (No in S1983), the three-dimensional data encoding device selects encoding table 2 (S1985).

[0549] The method of selecting the encoding table is not limited to the above. For example, if the normal vector of the target node belongs to normal vector group 2, the three-dimensional data encoding device may further switch the encoding table depending on the normal vector group to which the normal vector of the target node belongs. For example, the three-dimensional data encoding device may use encoding table 2.

[0550] For example, the three-dimensional data encoding device selects a group of normal vectors using information on the normal vectors of the target node. For example, the three-dimensional data encoding device determines that normal vectors whose distance between them is equal to or less than a predetermined threshold are the same group of normal vectors.

[0551] Furthermore, the information on the object to which the target node belongs may be information on a person, a car, a building, or the like, for example.

[0552] The following describes the configurations of a three-dimensional data encoding device 1900 and a three-dimensional data decoding device 1910 according to this embodiment. Fig. 75 is a block diagram of three-dimensional data encoding device 1900 according to this embodiment. Three-dimensional data encoding device 1900 shown in Fig. 75 includes an octree generation unit 1901, a similarity information calculation unit 1902, a coding table selection unit 1903, and an entropy encoding unit 1904.

[0553] The octree generation unit 1901 generates, for example, an octree from input three-dimensional points and generates an occupancy code for each node included in the octree. The similarity information calculation unit 1902 acquires similarity information, such as geometric information, structural information, or attribute information of the target node. The coding table selection unit 1903 selects a context to be used for entropy coding of the occupancy code according to the similarity information of the target node. The entropy coding unit 1904 generates a bitstream by entropy coding the occupancy code using the selected context. Note that the entropy coding unit 1904 may add information indicating the selected context to the bitstream.

[0554] 76 is a block diagram of a three-dimensional data decoding device 1910 according to this embodiment. The three-dimensional data decoding device 1910 shown in FIG. 76 includes an octree generation unit 1911, a similarity information calculation unit 1912, a coding table selection unit 1913, and an entropy decoding unit 1914.

[0555] The octree generation unit 1911 generates an octree, for example, from the lower layer to the upper layer, using information obtained from the entropy decoding unit 1914. The similarity information calculation unit 1912 obtains similarity information, which is geometric information, structural information, or attribute information of the target node. The coding table selection unit 1913 selects a context to be used for entropy decoding of the occupancy code according to the similarity information of the target node. The entropy decoding unit 1914 generates a 3D point by entropy decoding the occupancy code using the selected context. Note that the entropy decoding unit 1914 may decode and obtain information of the selected context added to the bitstream, and use the context indicated by the information.

[0556] As shown in Figures 63 to 65, multiple contexts are provided for each bit of the occupancy code. That is, the three-dimensional data encoding device entropy encodes a bit string representing an N-ary tree structure (N is an integer equal to or greater than 2) of multiple three-dimensional points included in the three-dimensional data, using an encoding table selected from multiple encoding tables. The bit string includes N bits of information for each node in the N-ary tree structure. The N-bit information includes N pieces of 1-bit information indicating whether or not a three-dimensional point exists in each of N child nodes of the corresponding node. In each of the multiple encoding tables, a context is provided for each bit of the N-bit information. In the entropy encoding, the three-dimensional data encoding device entropy encodes each bit of the N-bit information using the context provided for that bit in the selected encoding table.

[0557] According to this, the three-dimensional data encoding device can improve encoding efficiency by switching the context for each bit.

[0558] For example, in entropy coding, a three-dimensional data encoding device selects an encoding table to use from a plurality of encoding tables based on whether a three-dimensional point exists in each of a plurality of adjacent nodes adjacent to a target node. In this way, the three-dimensional data encoding device can improve encoding efficiency by switching encoding tables based on whether a three-dimensional point exists in the adjacent nodes.

[0559] For example, in entropy coding, a three-dimensional data encoding device selects a coding table based on an arrangement pattern that indicates the arrangement positions of adjacent nodes where three-dimensional points exist among multiple adjacent nodes, and selects the same coding table for arrangement patterns that become the same arrangement pattern when rotated. This allows the three-dimensional data encoding device to suppress an increase in the number of coding tables.

[0560] For example, in entropy coding, the three-dimensional data encoding device selects a coding table to use from a plurality of coding tables based on the layer to which the target node belongs. In this way, the three-dimensional data encoding device can improve coding efficiency by switching coding tables based on the layer to which the target node belongs.

[0561] For example, in entropy coding, the three-dimensional data encoding device selects an encoding table to use from multiple encoding tables based on the normal vector of the target node. This allows the three-dimensional data encoding device to improve encoding efficiency by switching encoding tables based on the normal vector.

[0562] 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.

[0563] Furthermore, the three-dimensional data decoding device entropy decodes a bit string representing an N-ary tree structure (N is an integer equal to or greater than 2) of multiple three-dimensional points included in the three-dimensional data, using a coding table selected from multiple coding tables. The bit string includes N bits of information for each node in the N-ary tree structure. The N-bit information includes N pieces of 1-bit information indicating whether a three-dimensional point exists in each of N child nodes of the corresponding node. In each of the multiple coding tables, a context is provided for each bit of the N-bit information. In the entropy decoding, the three-dimensional data decoding device entropy decodes each bit of the N-bit information using the context provided for that bit in the selected coding table.

[0564] According to this, the three-dimensional data decoding device can improve the coding efficiency by switching the context for each bit.

[0565] For example, in entropy decoding, the three-dimensional data decoding device selects a coding table to use from a plurality of coding tables based on whether a three-dimensional point exists in each of a plurality of adjacent nodes adjacent to the target node. In this way, the three-dimensional data decoding device can improve coding efficiency by switching coding tables based on whether a three-dimensional point exists in the adjacent nodes.

[0566] For example, in entropy decoding, the three-dimensional data decoding device selects a coding table based on an arrangement pattern that indicates the arrangement positions of adjacent nodes where three-dimensional points exist among multiple adjacent nodes, and selects the same coding table for arrangement patterns that become the same arrangement pattern when rotated. This allows the three-dimensional data decoding device to suppress an increase in the number of coding tables.

[0567] For example, in entropy decoding, the three-dimensional data decoding device selects a coding table to use from a plurality of coding tables based on the layer to which the target node belongs. In this way, the three-dimensional data decoding device can improve coding efficiency by switching coding tables based on the layer to which the target node belongs.

[0568] For example, in entropy decoding, the three-dimensional data decoding device selects a coding table to use from a plurality of coding tables based on the normal vector of the target node. This allows the three-dimensional data decoding device to improve coding efficiency by switching coding tables based on the normal vector.

[0569] For example, the three-dimensional data decoding device includes a processor and a memory, and the processor performs the above processing using the memory.

[0570] (Embodiment 9) In this embodiment, a method for controlling reference when encoding an occupancy code will be described. Note that, although the following mainly describes the operation of a three-dimensional data encoding device, a similar process may also be performed in a three-dimensional data decoding device.

[0571] 77 and 78 are diagrams showing reference relationships according to this embodiment. FIG. 77 is a diagram showing the reference relationships on an octree structure, and FIG. 78 is a diagram showing the reference relationships in the spatial domain.

[0572] In this embodiment, when encoding the encoding information of a node to be encoded (hereinafter referred to as the target node), the three-dimensional data encoding device refers to the encoding information of each node in the parent node to which the target node belongs. However, it does not refer to the encoding information of each node in other nodes (hereinafter referred to as parent adjacent nodes) on the same layer as the parent node. In other words, the three-dimensional data encoding device sets parent adjacent nodes to be unreferenceable or prohibits reference.

[0573] The three-dimensional data encoding device may allow a parent node to refer to the encoding information in the parent node (hereinafter referred to as the grandparent node) to which the parent node belongs. In other words, the three-dimensional data encoding device may encode the encoding information of the target node by referring to the encoding information of the parent node to which the target node belongs and the grandparent node.

[0574] Here, the encoding information is, for example, an occupancy code. When encoding the occupancy code of a target node, the three-dimensional data encoding device refers to information indicating whether or not a point group is included in each node in the parent node to which the target node belongs (hereinafter referred to as occupancy information). In other words, when encoding the occupancy code of the target node, the three-dimensional data encoding device refers to the occupancy code of the parent node. On the other hand, the three-dimensional data encoding device does not refer to the occupancy information of each node in the parent adjacent node. In other words, the three-dimensional data encoding device does not refer to the occupancy code of the parent adjacent node. The three-dimensional data encoding device may also refer to the occupancy information of each node in the grandparent node. In other words, the three-dimensional data encoding device may refer to the occupancy information of the parent node and the parent adjacent node.

[0575] For example, when encoding the occupancy code of a target node, the three-dimensional data encoding device switches the encoding table used for entropy encoding the occupancy code of the target node using the occupancy code of the parent node or grandparent node to which the target node belongs. Details of this will be described later. In this case, the three-dimensional data encoding device does not need to refer to the occupancy code of the parent adjacent node. This allows the three-dimensional data encoding device to appropriately switch the encoding table according to the information of the occupancy code of the parent node or grandparent node when encoding the occupancy code of the target node, thereby improving encoding efficiency. Furthermore, by not referring to the parent adjacent node, the three-dimensional data encoding device can reduce the process of checking the information of the parent adjacent node and the memory capacity required to store it. Furthermore, it becomes easier to scan and encode the occupancy code of each node in the occupancy tree in depth-first order.

[0576] An example of coding table switching using the occupancy code of a parent node will be described below. Fig. 79 is a diagram showing an example of a target node and adjacent reference nodes. Fig. 80 is a diagram showing the relationship between a parent node and nodes. Fig. 81 is a diagram showing an example of an occupancy code of a parent node. Here, an adjacent reference node is a node that is spatially adjacent to the target node and is referenced when encoding the target node. In the example shown in Fig. 79, the adjacent nodes are nodes that belong to the same layer as the target node. Furthermore, node X adjacent to the target block in the x direction, node Y adjacent to the target block in the y direction, and node Z adjacent to the target block in the z direction are used as reference adjacent nodes. That is, one adjacent block is set as the reference adjacent block in each of the x, y, and z directions.

[0577] Note that the node numbers shown in Figure 80 are just an example, and the relationship between the node numbers and the node positions is not limited to this. Also, in Figure 81, node 0 is assigned to the lower bits and node 7 is assigned to the upper bits, but the assignment may be performed in the reverse order. Also, each node may be assigned to any bit.

[0578] The three-dimensional data encoding device determines the encoding table for entropy encoding of the occupancy code of the target node, for example, by the following formula.

[0579] CodingTable=(FlagX<<2)+(FlagY<<1)+(FlagZ)

[0580] Here, CodingTable indicates a coding table for the occupancy code of the target node, and indicates one of the values ​​0 to 7. FlagX is occupancy information of adjacent node X, and indicates 1 if adjacent node X includes (occupies) a point cloud, and indicates 0 if not. FlagY is occupancy information of adjacent node Y, and indicates 1 if adjacent node Y includes (occupies) a point cloud, and indicates 0 if not. FlagZ is occupancy information of adjacent node Z, and indicates 1 if adjacent node Z includes (occupies) a point cloud, and indicates 0 if not.

[0581] In addition, since information indicating whether an adjacent node is occupied or not is included in the occupancy code of the parent node, the three-dimensional data encoding device may select an encoding table using the value indicated in the occupancy code of the parent node.

[0582] As described above, the three-dimensional data encoding device can improve encoding efficiency by switching encoding tables using information indicating whether or not a point group is included in the adjacent nodes of the target node.

[0583] Furthermore, the three-dimensional data encoding device may switch the adjacent reference node depending on the spatial position of the target node within the parent node, as shown in Fig. 79. In other words, the three-dimensional data encoding device may switch the adjacent node to be referenced from among multiple adjacent nodes depending on the spatial position of the target node within the parent node.

[0584] Next, configuration examples of a three-dimensional data encoding device and a three-dimensional data decoding device will be described. Fig. 82 is a block diagram of a three-dimensional data encoding device 2100 according to this embodiment. The three-dimensional data encoding device 2100 shown in Fig. 82 includes an octree generation unit 2101, a geometric information calculation unit 2102, a coding table selection unit 2103, and an entropy encoding unit 2104.

[0585] The octree generation unit 2101 generates, for example, an octree from input three-dimensional points (point cloud) and generates an occupancy code for each node included in the octree. The geometric information calculation unit 2102 acquires occupancy information indicating whether an adjacent reference node of the target node is occupied. For example, the geometric information calculation unit 2102 acquires the occupancy information of the adjacent reference node from the occupancy code of the parent node to which the target node belongs. Note that the geometric information calculation unit 2102 may switch the adjacent reference node depending on the position of the target node within the parent node, as shown in FIG. 79. Furthermore, the geometric information calculation unit 2102 does not refer to the occupancy information of each node within the parent adjacent node.

[0586] The coding table selection unit 2103 selects a coding table to be used for entropy coding of the occupancy code of the target node using the occupancy information of the adjacent reference node calculated by the geometric information calculation unit 2102. The entropy coding unit 2104 generates a bitstream by entropy coding the occupancy code using the selected coding table. Note that the entropy coding unit 2104 may add information indicating the selected coding table to the bitstream.

[0587] 83 is a block diagram of a three-dimensional data decoding device 2110 according to this embodiment. The three-dimensional data decoding device 2110 shown in FIG. 83 includes an octree generation unit 2111, a geometric information calculation unit 2112, a coding table selection unit 2113, and an entropy decoding unit 2114.

[0588] The octree generation unit 2111 generates an octree of a certain space (node) using header information of a bitstream, etc. The octree generation unit 2111 generates a large space (root node) using the sizes of the space in the x-axis, y-axis, and z-axis directions added to the header information, for example, and then divides the space into two in the x-axis, y-axis, and z-axis directions to generate eight small spaces A (nodes A0 to A7), thereby generating an octree. Furthermore, nodes A0 to A7 are set in order as target nodes.

[0589] The geometric information calculation unit 2112 acquires occupancy information indicating whether an adjacent reference node of the target node is occupied. For example, the geometric information calculation unit 2112 acquires the occupancy information of the adjacent reference node from the occupancy code of the parent node to which the target node belongs. Note that the geometric information calculation unit 2112 may switch the adjacent reference node depending on the position of the target node within the parent node, as shown in FIG. 79. Furthermore, the geometric information calculation unit 2112 does not refer to the occupancy information of each node within the parent adjacent node.

[0590] The coding table selection unit 2113 selects a coding table (decoding table) to be used for entropy decoding of the occupancy code of the target node using the occupancy information of the adjacent reference node calculated by the geometric information calculation unit 2112. The entropy decoding unit 2114 generates a 3D point by entropy decoding the occupancy code using the selected coding table. Note that the coding table selection unit 2113 may decode and acquire information of the selected coding table added to the bitstream, and the entropy decoding unit 2114 may use the coding table indicated by the acquired information.

[0591] Each bit of the occupancy code (8 bits) included in the bit stream indicates whether or not a point group is included in each of the eight small spaces A (nodes A0 to A7). Furthermore, the three-dimensional data decoding device divides the small space node A0 into eight small spaces B (nodes B0 to B7) to generate an occupancy tree, and decodes the occupancy code to obtain information indicating whether or not a point group is included in each node of the small space B. In this way, the three-dimensional data decoding device decodes the occupancy code of each node while generating an occupancy tree from the large space to the small spaces.

[0592] The flow of processing by the three-dimensional data encoding device and the three-dimensional data decoding device will be described below. Fig. 84 is a flowchart of three-dimensional data encoding processing in the three-dimensional data encoding device. First, the three-dimensional data encoding device determines (defines) a space (target node) that contains part or all of the input three-dimensional point cloud (S2101). Next, the three-dimensional data encoding device divides the target node into eight to generate eight small spaces (nodes) (S2102). Next, the three-dimensional data encoding device generates an occupancy code for the target node depending on whether or not each node contains a point cloud (S2103).

[0593] Next, the three-dimensional data encoding device calculates (obtains) occupancy information of the adjacent reference node of the target node from the occupancy code of the parent node of the target node (S2104). Next, the three-dimensional data encoding device selects a coding table to be used for entropy encoding based on the occupancy information of the determined adjacent reference node of the target node (S2105). Next, the three-dimensional data encoding device entropy encodes the occupancy code of the target node using the selected coding table (S2106).

[0594] Furthermore, the three-dimensional data encoding device repeats the process of dividing each node into eight parts and encoding the occupancy code of each node until the node cannot be divided any more (S2107). In other words, the processes from steps S2102 to S2106 are repeated recursively.

[0595] 85 is a flowchart of a three-dimensional data decoding method in a three-dimensional data decoding device. First, the three-dimensional data decoding device determines (defines) the space (target node) to be decoded using header information of the bitstream (S2111). Next, the three-dimensional data decoding device divides the target node into eight to generate eight small spaces (nodes) (S2112). Next, the three-dimensional data decoding device calculates (obtains) the occupancy information of the adjacent reference node of the target node from the occupancy code of the parent node of the target node (S2113).

[0596] Next, the three-dimensional data decoding device selects a coding table to be used for entropy decoding based on the occupancy information of the adjacent reference node (S2114). Next, the three-dimensional data decoding device entropy decodes the occupancy code of the target node using the selected coding table (S2115).

[0597] Furthermore, the three-dimensional data decoding device repeats the process of dividing each node into eight parts and decoding the occupancy code of each node until the node cannot be divided any more (S2116). In other words, the processes from steps S2112 to S2115 are repeated recursively.

[0598] Next, an example of switching of the coding table will be described. FIG. 86 is a diagram showing an example of switching of the coding table. For example, as in coding table 0 shown in FIG. 86, the same context model may be applied to a plurality of occupancy codes. Alternatively, a different context model may be assigned to each occupancy code. This allows a context model to be assigned according to the occurrence probability of the occupancy code, thereby improving coding efficiency. Alternatively, a context model in which a probability table is updated according to the occurrence frequency of the occupancy code may be used. Alternatively, a context model in which the probability table is fixed may be used.

[0599] Although FIG. 86 shows an example in which the coding tables shown in FIGS. 60 and 61 are used, the coding tables shown in FIGS. 63 and 64 may also be used.

[0600] A first modification of this embodiment will be described below. Fig. 87 is a diagram showing the reference relationship in this modification. In the above embodiment, the three-dimensional data encoding device does not refer to the occupancy code of the parent adjacent node, but it may switch whether or not to refer to the occupancy coding of the parent adjacent node according to specific conditions.

[0601] For example, when a three-dimensional data encoding device performs encoding while scanning an octet tree in a breadth-first manner, it encodes the occupancy code of the target node by referencing the occupancy information of the nodes in the parent adjacent node. On the other hand, when a three-dimensional data encoding device performs encoding while scanning an octet tree in a depth-first manner, it prohibits referencing the occupancy information of the nodes in the parent adjacent node. In this way, by appropriately switching the nodes that can be referenced depending on the scan order (encoding order) of the nodes in the octet tree, it is possible to improve encoding efficiency and reduce processing load.

[0602] The three-dimensional data encoding device may add information to the header of the bitstream, such as whether the octree was encoded in a breadth-first or depth-first manner. FIG. 88 is a diagram showing an example of the syntax of the header information in this case. octree_scan_order shown in FIG. 88 is encoding order information (encoding order flag) that indicates the encoding order of the octree. For example, octree_scan_order = 0 indicates breadth-first, and 1 indicates depth-first. By referring to octree_scan_order, the three-dimensional data decoding device can thus know whether the bitstream was encoded in a breadth-first or depth-first manner, and can therefore decode the bitstream appropriately.

[0603] Furthermore, the three-dimensional data encoding device may add information indicating whether or not referencing a parent adjacent node is prohibited to the header information of the bitstream. Fig. 89 is a diagram showing an example of the syntax of the header information in this case. limit_refer_flag is prohibition switching information (prohibition switching flag) indicating whether or not referencing a parent adjacent node is prohibited. For example, when limit_refer_flag is 1, this indicates that referencing a parent adjacent node is prohibited, and when it is 0, this indicates that there is no reference restriction (reference to a parent adjacent node is permitted).

[0604] In other words, the three-dimensional data encoding device determines whether to prohibit referencing a parent adjacent node, and switches between prohibiting and allowing referencing a parent adjacent node based on the result of the determination. The three-dimensional data encoding device also generates a bitstream that includes prohibition switching information that indicates whether to prohibit referencing a parent adjacent node based on the result of the determination.

[0605] In addition, the three-dimensional data decoding device acquires prohibition switching information indicating whether or not reference to the parent adjacent node is prohibited from the bitstream, and switches between prohibiting and allowing reference to the parent adjacent node based on the prohibition switching information.

[0606] This allows the three-dimensional data encoding device to generate a bitstream while controlling the reference to the parent adjacent node. Also, the three-dimensional data decoding device can obtain information indicating whether the reference to the parent adjacent node is prohibited from the header of the bitstream.

[0607] In addition, in this embodiment, the encoding process of the occupancy code has been described as an example of an encoding process that prohibits reference to a parent adjacent node, but this is not necessarily limited to this. For example, a similar method can be applied when encoding other information of an octtree node. For example, the method of this embodiment may be applied when encoding other attribute information such as color, normal vector, or reflectance added to a node. Furthermore, a similar method can be applied when encoding a coding table or a predicted value.

[0608] Next, a second modification of this embodiment will be described. In the above description, an example in which three reference adjacent nodes are used as shown in Figure 79 has been shown, but four or more reference adjacent nodes may also be used. Figure 90 is a diagram showing an example of a target node and reference adjacent nodes.

[0609] For example, the three-dimensional data encoding device calculates a coding table for entropy encoding of the occupancy code of the target node shown in FIG. 90, for example, using the following formula.

[0610] CodingTable=(FlagX0<<3)+(FlagX1<<2)+(FlagY<<1)+(FlagZ)

[0611] Here, CodingTable indicates the coding table for the occupancy code of the target node and indicates one of the values ​​0 to 15. FlagXN is occupancy information of adjacent node XN (N=0..1), indicating 1 if adjacent node XN includes (occupies) a point cloud, and 0 if not. FlagY is occupancy information of adjacent node Y, indicating 1 if adjacent node Y includes (occupies) a point cloud, and 0 if not. FlagZ is occupancy information of adjacent node Z, indicating 1 if adjacent node Z includes (occupies) a point cloud, and 0 if not.

[0612] In this case, if an adjacent node, for example, adjacent node X0 in Figure 90, cannot be referenced (reference prohibited), the three-dimensional data encoding device may use a fixed value such as 1 (occupied) or 0 (unoccupied) as an alternative value.

[0613] FIG. 91 is a diagram showing an example of a target node and adjacent nodes. As shown in FIG. 91, if an adjacent node cannot be referenced (reference is prohibited), the occupancy information of the adjacent node may be calculated by referring to the occupancy code of the grandparent node of the target node. For example, the three-dimensional data encoding device may calculate FlagX0 in the above equation using the occupancy information of adjacent node G0 instead of adjacent node X0 shown in FIG. 91, and determine the value of the encoding table using the calculated FlagX0. Note that adjacent node G0 shown in FIG. 91 is an adjacent node whose occupancy can be determined by the occupancy code of the grandparent node. Adjacent node X1 is an adjacent node whose occupancy can be determined by the occupancy code of the parent node.

[0614] Modification 3 of the present embodiment will be described below. Figures 92 and 93 are diagrams showing reference relationships according to this modification, with Figure 92 showing the reference relationships on an octree structure and Figure 93 showing the reference relationships in the spatial domain.

[0615] In this modification, when encoding the encoding information of a node to be encoded (hereinafter referred to as target node 2), the three-dimensional data encoding device refers to the encoding information of each node in the parent node to which the target node 2 belongs. That is, the three-dimensional data encoding device allows reference to information (e.g., occupancy information) of a child node of a first node, among multiple adjacent nodes, whose parent node is the same as the target node. For example, when encoding the occupancy code of the target node 2 shown in FIG. 92, the three-dimensional data encoding device refers to a node existing in the parent node to which the target node 2 belongs, such as the occupancy code of the target node shown in FIG. 92. The occupancy code of the target node shown in FIG. 92 indicates, for example, whether each node in the target node adjacent to the target node 2 is occupied, as shown in FIG. 93. Therefore, the three-dimensional data encoding device can switch the encoding table for the occupancy code of the target node 2 according to the detailed shape of the target node, thereby improving encoding efficiency.

[0616] The three-dimensional data encoding device may calculate a coding table for entropy encoding the occupancy code of the target node 2, for example, using the following formula.

[0617] CodingTable=(FlagX1<<5)+(FlagX2<<4)+(FlagX3<<3)+(FlagX4<<2)+(FlagY<<1)+(FlagZ)

[0618] Here, CodingTable indicates a coding table for the occupancy code of the target node 2, and indicates one of the values ​​0 to 63. FlagXN is occupancy information of adjacent node XN (N=1..4), and indicates 1 if adjacent node XN includes (occupies) a point cloud, and indicates 0 if not. FlagY is occupancy information of adjacent node Y, and indicates 1 if adjacent node Y includes (occupies) a point cloud, and indicates 0 if not. FlagZ is occupancy information of adjacent node Y, and indicates 1 if adjacent node Z includes (occupies) a point cloud, and indicates 0 if not.

[0619] The three-dimensional data encoding device may change the method of calculating the encoding table depending on the node position of the target node 2 within the parent node.

[0620] Furthermore, if reference to a parent adjacent node is not prohibited, the three-dimensional data encoding device may refer to the encoding information of each node in the parent adjacent node. For example, if reference to a parent adjacent node is not prohibited, reference to information (e.g., occupancy information) of a child node of a third node whose parent node is different from that of the target node is permitted. For example, in the example shown in FIG. 91, the three-dimensional data encoding device refers to the occupancy code of an adjacent node X0 whose parent node is different from that of the target node, and acquires occupancy information of the child node of the adjacent node X0. The three-dimensional data encoding device switches the encoding table used for entropy encoding of the occupancy code of the target node based on the acquired occupancy information of the child node of the adjacent node X0.

[0621] As described above, the three-dimensional data encoding device according to this embodiment encodes information (e.g., occupancy code) of a target node included in an N-ary tree structure (N is an integer equal to or greater than 2) of multiple three-dimensional points included in three-dimensional data. As shown in Figures 77 and 78, in the encoding process, the three-dimensional data encoding device allows reference to information (e.g., occupancy information) of a first node of multiple adjacent nodes spatially adjacent to the target node, whose parent node is the same as the target node, and prohibits reference to information (e.g., occupancy information) of a second node of which the parent node is different from the target node. In other words, in the encoding process, the three-dimensional data encoding device allows reference to information (e.g., occupancy code) of the parent node, and prohibits reference to information (e.g., occupancy code) of other nodes (parent adjacent nodes) on the same layer as the parent node.

[0622] According to this, the three-dimensional data encoding device can improve encoding efficiency by referencing information on a first node, among multiple adjacent nodes spatially adjacent to the target node, whose parent node is the same as the target node. Furthermore, the three-dimensional data encoding device can reduce the amount of processing by not referencing information on a second node, among multiple adjacent nodes, whose parent node is different from the target node. In this way, the three-dimensional data encoding device can improve encoding efficiency and reduce the amount of processing.

[0623] For example, the three-dimensional data encoding device further determines whether to prohibit reference to the information of the second node, and in the encoding, switches between prohibiting and allowing reference to the information of the second node based on the result of the determination. The three-dimensional data encoding device further generates a bitstream including prohibition switching information (for example, limit_refer_flag shown in FIG. 89) that is the result of the determination and indicates whether to prohibit reference to the information of the second node.

[0624] This allows the three-dimensional data encoding device to switch whether or not to prohibit reference to information of the second node, and the three-dimensional data decoding device to perform decoding processing appropriately using the prohibition switching information.

[0625] For example, the information of the target node is information (e.g., occupancy code) indicating whether or not a three-dimensional point exists in each of the child nodes belonging to the target node, the information of the first node is information indicating whether or not a three-dimensional point exists in the first node (occupancy information of the first node), and the information of the second node is information indicating whether or not a three-dimensional point exists in the second node (occupancy information of the second node).

[0626] For example, in the above encoding, the three-dimensional data encoding device selects an encoding table based on whether or not a three-dimensional point exists at the first node, and uses the selected encoding table to entropy encode information of the target node (e.g., occupancy code).

[0627] For example, in the above encoding, the three-dimensional data encoding device allows reference to information (for example, occupancy information) of a child node of the first node among a plurality of adjacent nodes, as shown in FIGS.

[0628] This allows the three-dimensional data encoding device to refer to more detailed information about adjacent nodes, thereby improving encoding efficiency.

[0629] For example, as shown in FIG. 79, in the above encoding, the three-dimensional data encoding device switches the adjacent node to be referenced from among a plurality of adjacent nodes depending on the spatial position of the target node within the parent node.

[0630] This allows the three-dimensional data encoding device to refer to an appropriate adjacent node depending on the spatial position of the target node within the parent node.

[0631] 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.

[0632] Furthermore, the three-dimensional data decoding device according to this embodiment decodes information (e.g., occupancy code) of a target node included in an N-ary tree structure (N is an integer equal to or greater than 2) of multiple three-dimensional points included in the three-dimensional data. As shown in Figures 77 and 78, in the decoding, the three-dimensional data decoding device permits reference to information (e.g., occupancy information) of a first node of multiple adjacent nodes spatially adjacent to the target node, whose parent node is the same as the target node, and prohibits reference to information (e.g., occupancy information) of a second node whose parent node is different from the target node. In other words, in the decoding, the three-dimensional data decoding device permits reference to information (e.g., occupancy code) of the parent node, and prohibits reference to information (e.g., occupancy code) of other nodes (parent adjacent nodes) on the same layer as the parent node.

[0633] According to this, the three-dimensional data decoding device can improve coding efficiency by referencing information on a first node, among multiple adjacent nodes spatially adjacent to the target node, whose parent node is the same as the target node. Furthermore, the three-dimensional data decoding device can reduce the amount of processing by not referencing information on a second node, among multiple adjacent nodes, whose parent node is different from the target node. In this way, the three-dimensional data decoding device can improve coding efficiency and reduce the amount of processing.

[0634] For example, the three-dimensional data decoding device further acquires prohibition switching information (e.g., limit_refer_flag shown in FIG. 89) from the bitstream indicating whether to prohibit reference to information of the second node, and in the above decoding, switches between prohibiting and allowing reference to information of the second node based on the prohibition switching information.

[0635] This allows the three-dimensional data decoding device to perform decoding processing appropriately using the inhibition switching information.

[0636] For example, the information of the target node is information (e.g., occupancy code) indicating whether or not a three-dimensional point exists in each of the child nodes belonging to the target node, the information of the first node is information indicating whether or not a three-dimensional point exists in the first node (occupancy information of the first node), and the information of the second node is information indicating whether or not a three-dimensional point exists in the second node (occupancy information of the second node).

[0637] For example, in the above decoding, the three-dimensional data decoding device selects an encoding table based on whether or not a three-dimensional point exists at the first node, and entropy decodes the information of the target node (e.g., occupancy code) using the selected encoding table.

[0638] For example, in the decoding, the three-dimensional data decoding device allows reference to information (for example, occupancy information) of a child node of the first node among a plurality of adjacent nodes, as shown in FIGS.

[0639] This allows the three-dimensional data decoding device to refer to more detailed information about adjacent nodes, thereby improving coding efficiency.

[0640] For example, as shown in FIG. 79, in the decoding, the three-dimensional data decoding device switches the adjacent node to be referenced from among a plurality of adjacent nodes depending on the spatial position of the target node within the parent node.

[0641] This allows the three-dimensional data decoding device to refer to an appropriate adjacent node depending on the spatial position of the target node within the parent node.

[0642] For example, the three-dimensional data decoding device includes a processor and a memory, and the processor performs the above processing using the memory.

[0643] (Embodiment 10) In this embodiment, a method for reducing the number of encoding tables will be described.

[0644] If an encoding table is provided for each combination of the position of the target node within the parent node (8 patterns) and the occupation state patterns of the three adjacent nodes of the target node (8 patterns), 8 x 8 = 64 encoding tables are required. Note that, below, this combination is also referred to as an adjacent occupation pattern. Also, an occupied node is also referred to as an occupied node. An adjacent node in an occupied state is also referred to as an adjacent occupied node.

[0645] In this embodiment, the total number of coding tables is reduced by assigning the same coding table to similar adjacently occupied patterns. Specifically, a plurality of adjacently occupied patterns are grouped by performing a conversion process on the adjacently occupied patterns. More specifically, adjacently occupied patterns that become the same pattern after the conversion process are grouped into the same group. Furthermore, one coding table is assigned to each group.

[0646] For example, the transformation process may involve translation along the x-axis, y-axis, or z-axis as shown in Fig. 94. Alternatively, as shown in Fig. 95, rotation along the x-axis, y-axis, or z-axis (with the x-axis, y-axis, or z-axis as the axis) may be used.

[0647] Furthermore, the grouped adjacent occupancy patterns may be classified using the following rules. For example, as shown in Figure 96, the plane on which the occupied node and the target node exist may be horizontal or vertical to the coordinate plane (xy plane, yz plane, or xz plane). Alternatively, as shown in Figure 97, the adjacent plane in which the adjacent occupied node exists relative to the target node may be used.

[0648] Fig. 98 is a diagram showing an example of translation along the x-axis, y-axis, or z-axis. Fig. 99 is a diagram showing an example of rotation along the x-axis. Fig. 100 is a diagram showing an example of rotation along the y-axis. Fig. 101 is a diagram showing an example of rotation along the z-axis. Fig. 102 is a diagram showing an example of horizontal or vertical coordinate planes. Fig. 103 is a diagram showing an example of patterns of adjacent faces.

[0649] Figure 104 shows an example in which 64 adjacent occupation patterns are divided into six groups. That is, in this example, six coding tables are used. In this example, the coding tables are reduced by rotation along the z-axis.

[0650] Specifically, as shown in FIG. 104, an adjacent occupancy pattern whose occupancy number, which is the number of adjacent nodes that are occupied among three adjacent nodes, is 0 is classified into group 0. An adjacent occupancy pattern whose occupancy number is 1 and is horizontal to the xy plane is classified into group 1. An adjacent occupancy pattern whose occupancy number is 1 and is perpendicular to the xy plane is classified into group 2. An adjacent occupancy pattern whose occupancy number is 2 and is perpendicular to the xy plane is classified into group 3. An adjacent occupancy pattern whose occupancy number is 2 and is horizontal to the xy plane is classified into group 4. An adjacent occupancy pattern whose occupancy number is 3 is classified into group 5.

[0651] One coding table is used for each group. Each group contains adjacent occupied patterns that become identical when rotated along the z-axis. Note that for group 4, translation along the z-axis is also taken into account.

[0652] For example, in a three-dimensional map with the xy plane as the ground plane, there may be multiple buildings with similar shapes on the xy plane. In such a case, for example, rotating building A along the z-axis may result in it overlapping with another building B. In this case, using the coding table for the occupancy code updated by encoding building A when encoding the occupancy code for building B may improve the coding efficiency when encoding building B. Therefore, by treating coding tables with shapes rotated along the z-axis as the same group, the coding tables can be updated without being affected by rotation along the z-axis, thereby improving coding efficiency. Also, for example, translating building C parallel to the xy plane may result in it overlapping with another building D. In this case, using the coding table for the occupancy code updated by encoding building C when encoding the occupancy code for building D may improve the coding efficiency when encoding building D. Therefore, by treating coding tables related to movement parallel to the xy plane as the same group, the coding tables can be updated without being affected by movement parallel to the xy plane, thereby improving coding efficiency.

[0653] Figure 105 shows an example in which 64 adjacent occupation patterns are divided into eight groups. That is, in this example, eight coding tables are used. In this example, the coding tables are reduced by rotation along the z axis and adjacent faces.

[0654] Specifically, as shown in FIG. 105, an adjacent occupation pattern whose occupation number, which is the number of occupied nodes among three adjacent nodes, is 0 is classified into group 0. An adjacent occupation pattern whose occupation number is 1 and is horizontal to the xy plane is classified into group 1. An adjacent occupation pattern whose occupation number is 1 and is perpendicular to the xy plane is classified into group 2. An adjacent occupation pattern whose occupation number is 2, is perpendicular to the xy plane, and has an adjacent surface in the z direction (i.e., an adjacent occupied node exists in the z direction of the target node) is classified into group 3. An adjacent occupation pattern whose occupation number is 2, is perpendicular to the xy plane, and has an adjacent surface in the -z direction (i.e., an adjacent occupied node exists in the -z direction of the target node) is classified into group 4.

[0655] Adjacent occupation patterns with an occupation number of 2 and horizontal to the xy plane are classified into group 5. Adjacent occupation patterns with an occupation number of 3 and adjacent faces in the z direction are classified into group 5. Adjacent occupation patterns with an occupation number of 3 and adjacent faces in the -z direction are classified into group 5.

[0656] One coding table is used for each group. Each group contains adjacent occupied patterns that become identical when rotated along the z-axis. Note that for group 5, translation along the z-axis is also taken into account.

[0657] A mapping rule can be generated using the examples shown in Figures 104 and 105. Figure 106 is a diagram showing an example of a mapping rule (conversion table) when three coding tables are used for 64 adjacently occupied patterns. In the example shown in Figure 106, one of the indexes of the three coding tables (tables 0 to 2) is assigned to each of the 64 adjacently occupied patterns (patterns 0 to 63). This rule is represented by, for example, a look-up table (LUT).

[0658] Furthermore, mapping rules may be generated by adding a new rule to a given mapping rule or deleting part of the rule. That is, adjacently occupied patterns grouped by a first rule may be further grouped by a second rule. In other words, a plurality of adjacently occupied patterns may be assigned to a plurality of first coding tables by a first conversion table, and the plurality of first coding tables may be assigned to a plurality of second coding tables by a second conversion table, and arithmetic coding or arithmetic decoding may be performed using the second coding tables. For example, after the classification shown in FIG. 105 is performed, some of the classified groups may be further integrated to perform the classification shown in FIG. 104.

[0659] Figure 107 shows an example of a conversion table for this classification. Encoding table 1 shown in Figure 107 is an index of an encoding table derived by a given mapping rule, and encoding table 2 is an index of an encoding table representing a new mapping rule.

[0660] For example, coding table 1 is an index of a coding table obtained by the classification shown in Fig. 105, and indicates one of the indexes of eight coding tables (tables 0 to 7). Also, coding table 2 indicates one of the indexes of six coding tables (tables 0 to 5) corresponding to the classification shown in Fig. 104.

[0661] Specifically, since Group 4 and Group 5 shown in Figure 105 correspond to Group 4 shown in Figure 104, Table 4 and Table 5 of Encoding Table 1 are assigned to Table 4 of Encoding Table 2, as shown in Figure 107. Similarly, Table 6 and Table 7 of Encoding Table 1 are assigned to Table 5 of Encoding Table 2.

[0662] The following is an overview of the mapping process: A mapping rule is used to find a unique index into the encoding table.

[0663] FIG. 108 is a diagram showing an overview of the mapping process for determining an index of a coding table from 64 adjacent occupation patterns. As shown in FIG. 108, an adjacent occupation pattern including the position of a target node is input to a mapping rule, and a table index (index of the coding table) is output. The number of patterns is reduced by the mapping rule. For example, the mapping rule shown in FIG. 106 is used as this mapping rule. As shown in FIG. 106, the same table index is assigned to different adjacent occupation patterns.

[0664] Next, entropy coding is performed using the coding table assigned to the obtained table index.

[0665] FIG. 109 is a diagram showing an overview of mapping processing when a table index is given. As shown in FIG. 109, table index 1 is input to the mapping rule, and table index 2 is output. The number of patterns is reduced by the mapping rule. For example, the mapping rule shown in FIG. 107 is used as this mapping rule. As shown in FIG. 107, the same table index 2 is assigned to different table indexes 1.

[0666] Next, entropy coding is performed using the coding table assigned to the obtained table index 2.

[0667] Next, the configurations of a three-dimensional data encoding device and a three-dimensional data decoding device according to this embodiment will be described. Fig. 110 is a block diagram showing the configuration of a three-dimensional data encoding device 3600 according to this embodiment. The three-dimensional data encoding device 3600 shown in Fig. 110 includes an octree generation unit 3601, a geometric information calculation unit 3602, an index generation unit 3603, a coding table selection unit 3604, and an entropy encoding unit 3605.

[0668] The octree generation unit 3601 generates, for example, an octree from input three-dimensional points (point cloud) and generates an occupancy code for each node included in the octree. The geometric information calculation unit 3602 acquires occupancy information indicating whether an adjacent reference node of the target node is occupied. For example, the geometric information calculation unit 3602 calculates the occupancy information of the adjacent reference node from the occupancy code of the parent node to which the target node belongs. Note that the geometric information calculation unit 3602 may switch the adjacent reference node depending on the position of the target node within the parent node. Also, the geometric information calculation unit 3602 does not need to refer to the occupancy information of each node in the adjacent parent node.

[0669] The index generating unit 3603 generates an index for the coding table using adjacent information (for example, adjacent occupation pattern).

[0670] The coding table selection unit 3604 uses the index of the coding table generated by the index generation unit 3603 to select a coding table to be used for entropy coding of the occupancy code of the target node.

[0671] The occupancy code is coded as a decimal or binary number. For example, when binary coding is used, the index of the coding table generated by the index generation unit 3603 is used to select a binary context to be used for entropy coding by the entropy coding unit 3605. When decimal or M-ary coding is used, the index of the coding table selects an M-ary context.

[0672] The entropy coding unit 3605 generates a bitstream by entropy coding the occupancy code using the selected coding table. The entropy coding unit 3605 may also add information indicating the selected coding table to the bitstream.

[0673] 111 is a block diagram of a three-dimensional data decoding device 3610 according to this embodiment. The three-dimensional data decoding device 3610 shown in FIG. 111 includes an octree generation unit 3611, a geometric information calculation unit 3612, an index generation unit 3613, a coding table selection unit 3614, and an entropy decoding unit 3615.

[0674] The octree generation unit 3611 generates an octree of a certain space (node) using header information of a bitstream, etc. The octree generation unit 3611 generates a large space (root node) using the sizes of the x-axis, y-axis, and z-axis directions of the certain space added to the header information, for example, and divides the space into two in the x-axis, y-axis, and z-axis directions to generate eight small spaces A (nodes A0 to A7) and generate an octree. Furthermore, nodes A0 to A7 are set in order as target nodes.

[0675] The geometric information calculation unit 3612 acquires occupancy information indicating whether an adjacent reference node of the target node is occupied. For example, the geometric information calculation unit 3612 acquires the occupancy information of the adjacent reference node from the occupancy code of the parent node to which the target node belongs. Note that the geometric information calculation unit 3612 may switch the adjacent reference node depending on the position of the target node within the parent node. Also, the geometric information calculation unit 3612 may not need to refer to the occupancy information of each node within the adjacent parent node.

[0676] The index generating unit 3613 generates an index for the coding table using adjacent information (for example, adjacent occupation pattern).

[0677] The coding table selection unit 3614 uses the index of the coding table generated by the index generation unit 3613 to select a coding table to be used for entropy decoding of the occupancy code of the target node.

[0678] The occupancy code is decoded as a decimal or binary number. For example, if binary coding is used, the index of the coding table mapped to the previous block is used to select the binary context to be used for entropy decoding of the next block. Also, if decimal or M-ary coding is used, the index of the coding table selects the M-ary context.

[0679] The entropy decoding unit 3615 generates three-dimensional points (point cloud) by entropy decoding the occupancy code using the selected coding table. Note that the entropy decoding unit 3615 may decode and acquire information about the selected coding table added to the bitstream, and use the coding table indicated by the acquired information.

[0680] Each bit of the occupancy code (8 bits) included in the bit stream indicates whether or not a point group is included in each of the eight small spaces A (nodes A0 to A7). Furthermore, the three-dimensional data decoding device divides the small space node A0 into eight small spaces B (nodes B0 to B7) to generate an occupancy tree, and decodes the occupancy code to obtain information indicating whether or not a point group is included in each node of the small space B. In this way, the three-dimensional data decoding device decodes the occupa...

Claims

1. Generate a tree structure of a plurality of three-dimensional points included in the three-dimensional data; Generate a parameter indicating the node that can be referenced, determining an adjacent occupancy pattern from among a plurality of adjacent occupancy patterns based on the occupancy state of adjacent nodes of the target node; determining a group from among a plurality of groups that corresponds to the determined adjacent occupation pattern; Encoding the target node using the determined group information; each of the plurality of groups corresponds to one or more adjacent occupancy patterns; the number of the plurality of groups that can be determined varies depending on the value indicated by the parameter; the plurality of groups includes a first group and a second group; each adjacent occupancy pattern corresponding to the first group indicates a first number of occupied nodes; each adjacent occupation pattern corresponding to the second group indicates a second number of occupied nodes greater than the first number; the number of adjacent occupied patterns corresponding to the first group is smaller than the number of adjacent occupied patterns corresponding to the second group; Three-dimensional data encoding method.

2. Each adjacent occupancy pattern corresponding to the same group shows the same number of occupied nodes.

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

3. The number of the plurality of adjacent occupation patterns is 64.

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

4. Each of the plurality of adjacent occupancy patterns indicates the occupancy status of six adjacent nodes adjacent to the target node.

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

5. The six adjacent nodes adjacent to the target node include three adjacent nodes whose parent nodes are different from those of the target node.

5. The three-dimensional data encoding method according to claim 4.

6. When the parameter indicates a first value, the number of the plurality of groups is N, and when the parameter indicates a second value, the number of the plurality of groups is M, and M is different from N. The three-dimensional data encoding method according to any one of claims 1 to 5.

7. Obtaining a tree structure of multiple 3D points included in the 3D data; Gets a parameter indicating the node that can be referenced, determining an adjacent occupancy pattern from among a plurality of adjacent occupancy patterns based on the occupancy state of adjacent nodes of the target node; determining a group from among a plurality of groups that corresponds to the determined adjacent occupation pattern; Decrypting the target node using the determined group information; each of the plurality of groups corresponds to one or more adjacent occupancy patterns; the number of the plurality of groups that can be determined varies depending on the value indicated by the parameter; the plurality of groups includes a first group and a second group; each adjacent occupancy pattern corresponding to the first group indicates a first number of occupied nodes; each adjacent occupation pattern corresponding to the second group indicates a second number of occupied nodes greater than the first number; the number of adjacent occupied patterns corresponding to the first group is smaller than the number of adjacent occupied patterns corresponding to the second group; Three-dimensional data decoding method.

8. Each adjacent occupancy pattern corresponding to the same group shows the same number of occupied nodes. The three-dimensional data decoding method according to claim 7.

9. The number of the plurality of adjacent occupation patterns is 64. The three-dimensional data decoding method according to claim 7.

10. Each of the plurality of adjacent occupancy patterns indicates the occupancy status of six adjacent nodes adjacent to the target node. The three-dimensional data decoding method according to claim 7.

11. The six adjacent nodes adjacent to the target node include three adjacent nodes whose parent nodes are different from those of the target node. The three-dimensional data decoding method according to claim 10.

12. When the parameter indicates a first value, the number of the plurality of groups is N, and when the parameter indicates a second value, the number of the plurality of groups is M, and M is different from N. The three-dimensional data decoding method according to any one of claims 7 to 11.

13. a processor; a memory; The processor uses the memory to: Generate a tree structure of a plurality of three-dimensional points included in the three-dimensional data; Generate a parameter indicating the node that can be referenced, determining an adjacent occupancy pattern from among a plurality of adjacent occupancy patterns based on the occupancy state of adjacent nodes of the target node; determining a group from among a plurality of groups that corresponds to the determined adjacent occupation pattern; Encoding the target node using the determined group information; each of the plurality of groups corresponds to one or more adjacent occupancy patterns; the number of the plurality of groups that can be determined varies depending on the value indicated by the parameter; the plurality of groups includes a first group and a second group; each adjacent occupancy pattern corresponding to the first group indicates a first number of occupied nodes; each adjacent occupation pattern corresponding to the second group indicates a second number of occupied nodes greater than the first number; the number of adjacent occupied patterns corresponding to the first group is smaller than the number of adjacent occupied patterns corresponding to the second group; Three-dimensional data encoding device.

14. a processor; a memory; The processor uses the memory to: Obtaining a tree structure of multiple 3D points included in the 3D data; Gets a parameter indicating the node that can be referenced, determining an adjacent occupancy pattern from among a plurality of adjacent occupancy patterns based on the occupancy state of adjacent nodes of the target node; determining a group from among a plurality of groups that corresponds to the determined adjacent occupation pattern; Decrypting the target node using the determined group information; each of the plurality of groups corresponds to one or more adjacent occupancy patterns; the number of the plurality of groups that can be determined varies depending on the value indicated by the parameter; the plurality of groups includes a first group and a second group; each adjacent occupancy pattern corresponding to the first group indicates a first number of occupied nodes; each adjacent occupation pattern corresponding to the second group indicates a second number of occupied nodes greater than the first number; the number of adjacent occupied patterns corresponding to the first group is smaller than the number of adjacent occupied patterns corresponding to the second group; Three-dimensional data decoding device.

Images