RU2817735C1 - Occupancy coding context simulation for point cloud coding - Google Patents

Abstract

FIELD: data encoding and decoding.

SUBSTANCE: technical result is achieved by the fact that the number of contexts associated with the received data, are reduced based on occupancy data corresponding to one or more parent nodes and one or more child nodes in the obtained data. Data corresponding to the point cloud is decoded based on the reduced number of contexts. At that, for parent nodes having a common face with child nodes, the parent level context index is calculated by taking into account all possible combinations, and for parent nodes having a common edge with child nodes, the parent level context index is calculated by simply counting how many parent nodes are occupied.

EFFECT: high efficiency of encoding and decoding point cloud data by reducing the number of contexts associated with point cloud data.

16 cl, 15 dwg

Description

CROSS REFERENCE TO RELATED APPLICATIONS

[1] This application claims benefit from U.S. Patent Application No. 17/231695, "CONTEXT OCCUPATION CODING MODELING FOR POINT CLOUD ENCODING," filed April 15, 2021, claiming priority based on U.S. Provisional Application No. 63/034113, "UPDATES TO CONTEXT MODELING ANIA EMPLOYMENT CODING FOR POINT CLOUD CODING,” filed June 3, 2020, and 63/066099, “UPDATES TO PARENT LEVEL CONTEXT MODELING OF OCCUPATION CODING FOR POINT CLOUD CODING,” filed Aug. 14, 2020, which are incorporated herein in their entirety. via link.

FIELD OF TECHNOLOGY TO WHICH THE INVENTION RELATES

[2] This invention relates generally to the field of data processing and in particular to point clouds.

[3] Point cloud has been widely used in recent years. For example, it is used in autonomous vehicles for object detection and localization; it is also used in Geographic Information Systems (GIS) for mapping and is used in cultural heritage for visualizing and archiving cultural heritage sites and collections, etc. Point clouds contain a collection of multidimensional points, typically three-dimensional (3D), each of which includes 3D position information and additional attributes such as color, reflectance, and so on. They can be captured using multiple cameras and depth or lidar sensors in a variety of settings and can consist of thousands to billions of points to provide a realistic representation of the original scenes. Compression technologies are required to reduce the amount of data required to represent a point cloud, for faster transfer or reduced memory. ISO/IEC MPEG (JTC 1/SC 29/WG 11) created a special group (MPEG-PCC) to standardize compression technologies for static or dynamic point clouds.

DISCLOSURE OF THE INVENTION

[4] Embodiments relate to a method, system, and computer-readable medium for decoding point cloud data. In one aspect, a method for decoding point cloud data is proposed. The method may include obtaining data corresponding to a point cloud. The number of contexts associated with the received data is reduced based on the occupancy data corresponding to one or more parent nodes and one or more child nodes in the received data. The data corresponding to the point cloud is decoded based on the reduced number of contexts.

[5] In another aspect, a computer system for decoding point cloud data is proposed. The computer system may include one or more processors, one or more computer-readable storage devices, one or more computer-readable physical storage devices, and program instructions stored on at least one of the one or more storage devices for execution by at least one of the or more processors through at least one of one or more storage devices, the computer system being capable of executing the method. The method may include obtaining data corresponding to a point cloud. The number of contexts associated with the received data is reduced based on the occupancy data corresponding to one or more parent nodes and one or more child nodes in the received data. The data corresponding to the point cloud is decoded based on the reduced number of contexts.

[6] In yet another aspect, a computer-readable medium for decoding point cloud data is provided. The computer-readable medium may include one or more computer-readable storage devices and program instructions stored on at least one of one or more physical storage devices, wherein the program instructions may be executed by a processor. Software instructions may be executed by a processor to perform a method, which may include obtaining data corresponding to a point cloud. The number of contexts associated with the received data is reduced based on the occupancy data corresponding to one or more parent nodes and one or more child nodes in the received data. The data corresponding to the point cloud is decoded based on the reduced number of contexts.

BRIEF DESCRIPTION OF THE DRAWINGS

[7] These and other objects, features and advantages will become apparent from the following detailed description of the illustrative embodiments, which should be read in conjunction with the accompanying drawings. Various elements of the drawings are not to scale because the illustrations are provided for clarity and ease of understanding by one skilled in the art in conjunction with the detailed description. On the drawings:

FIG. 1 illustrates a networked computer environment according to at least one embodiment; FIG.

FIG. 2A is a diagram of an octree structure for point cloud data according to at least one embodiment; FIG.

FIG. 2B is a diagram of an octree partition for point cloud data according to at least one embodiment; FIG.

FIG. 2C is a block diagram of a parent node's neighbors for point cloud data according to at least one embodiment; FIG.

FIG. 2D is a block diagram of parent node level contexts for point cloud data according to at least one embodiment; FIG.

FIG. 2E is a block diagram of the parent level neighbors of a current coded node for point cloud data according to at least one embodiment; FIG.

FIG. 2F is a block diagram of parent node level contexts for point cloud data according to at least one embodiment; FIG.

FIG. 2G is a block diagram of the parent layer neighbors of a current coded node for point cloud data according to at least one embodiment; FIG.

FIG. 2H is a block diagram of child node level neighbors for point cloud data according to at least one embodiment; FIG.

FIG. 3A illustrates a syntax element for signaling reduction of point cloud data contexts according to at least one embodiment; FIG.

FIG. 3B is a hash table for encoded point cloud geometry information according to at least one embodiment; FIG.

FIG. 4 is a functional block diagram illustrating steps performed by a program decoding point cloud data, according to at least one embodiment; FIG.

FIG. 5 is a block diagram of internal and external components of the computers and servers depicted in FIG. 1, according to at least one embodiment;

FIG. 6 is a block diagram of an example cloud computing environment including the computer system depicted in FIG. 1 according to at least one embodiment; FIG. And

FIG. 7 is a block diagram of functional layers of the exemplary cloud computing environment of FIG. 6 according to at least one embodiment.

IMPLEMENTATION OF THE INVENTION

[8] Detailed embodiments of the claimed structures and methods are disclosed herein; however, it is understood that the disclosed embodiments are merely illustrative of the claimed structures and methods, which may be implemented in various forms. These structures and methods may, however, be implemented in numerous different forms, and should not be construed as being limited to the exemplary embodiments provided herein. Rather, these exemplary embodiments are provided so that this disclosure is exhaustive and complete, and fully conveys the scope to those skilled in the art. Details of well-known features and technologies may be omitted from the description in order to avoid unnecessary difficulty in understanding the presented embodiments.

[9] Embodiments generally relate to the field of data processing and, in particular, to point clouds. The following exemplary embodiments described relate to a system, method, and computer program for, among other things, reducing the number of contexts associated with point cloud data. Therefore, some embodiments may improve the computational domain by providing improved compression and decompression of the point cloud by considering an appropriate subset of neighbors from all neighbors of the current node in the point cloud data.

[10] As described above, point cloud has been widely used in recent years. For example, it is used in autonomous vehicles for object detection and localization; it is also used in Geographic Information Systems (GIS) for mapping and is used in cultural heritage for visualizing and archiving cultural heritage sites and collections, etc. Point clouds contain a collection of multidimensional points, typically three-dimensional (3D), each of which includes 3D position information and additional attributes such as color, reflectance, and so on. They can be captured using multiple cameras and depth or lidar sensors in a variety of settings and can consist of thousands to billions of points to provide a realistic representation of the original scenes. Compression technologies are required to reduce the amount of data required to represent a point cloud, for faster transfer or reduced memory. ISO/IEC MPEG (JTC 1/SC 29/WG 11) created a special group (MPEG-PCC) to standardize compression technologies for static or dynamic point clouds.

[11] However, context modeling of occupancy coding at the parent node level is limited to accessing up to 6 neighboring coded nodes. This may result in a relatively small number of contexts. Accordingly, considering only the 6 nearest neighbors may largely limit the range of the receptive field and thus limit the induced performance gain. In addition, contextual modeling of occupancy coding can result in a large number of contexts. However, in terms of hardware implementation, a very large number of contexts is not preferable because they consume a large amount of memory. Therefore, it may be preferable to improve the performance of the parent node level context while keeping the number of contexts small by reducing the number of presented contexts.

[12] Aspects are described herein with reference to depictions of flowcharts and/or block diagrams of methods, apparatus (systems), and computer-readable media in accordance with various embodiments. It should be understood that each block of block diagram images and/or block diagrams, and combinations of blocks in the block diagram images and/or block diagrams can be implemented using machine-readable program instructions.

[13] The following exemplary embodiments described relate to a system, method, and computer program that compresses and decompresses point cloud data based on reducing an expanded set of contexts associated with the point cloud data. Referring to FIG. 1, a functional block diagram of a networked computer environment is presented illustrating a point cloud compression system 100 (hereinafter "system") for compressing and decompressing point cloud data. It should be understood that FIG. 1 is intended to illustrate one implementation only and is not intended to be any limitation as to the environments in which the various embodiments may be implemented. Numerous modifications can be made to the environments shown based on design and implementation requirements.

[14] System 100 may include a computer 102 and a server computer 114. The computer 102 may communicate with the server computer 114 via a communications network 110 (hereinafter, “network”). Computer 102 may include a processor 104 and a software system program 108 that is stored on storage device 106 and may interact with a user and communicate with server computer 114. As described below with reference to FIG. 5, computer 102 may include internal components 800A and external components 900A, respectively, and the server computer 114 may include internal components 800B and external components 900B, respectively. Computer 102 may be, for example, a mobile device, telephone, personal digital assistant, netbook, laptop, tablet, desktop, or any type of computing device capable of running a program, accessing a network, and accessing a database.

[15] The server computer 114 may also operate in a cloud computing service model, such as software as a service (SaaS), platform as a service (PaaS), or infrastructure as a service (laaS), as described below with reference to FIG. 6 and 7. The server computer 114 may also be located in a cloud computing deployment model, such as a private cloud, a community cloud, a public cloud, or a hybrid cloud.

[16] The server computer 114, which can be used to compress and decompress the point cloud, can run a Point Cloud Compression Program 116 (hereinafter “program”), which can interact with the database 112. The Point Cloud Compression Program method is explained in more detail below with respect to FIG. 4. In one embodiment, computer 102 may operate as an input device, including a user interface, while program 116 may run primarily on server computer 114. In an alternative embodiment, program 116 may operate primarily on one or more computers 102, while the server computer 114 may be used to process and store data used by program 116. It should be noted that program 116 may be a separate program or may be integrated into a larger point cloud compression and decompression program.

[17] It should be noted, however, that processing for program 116 may in some cases be distributed between computers 102 and server computers 114 in any ratio. In another embodiment, program 116 may run on more than one computer, server, or some combination of computers and server computers, such as multiple computers 102 communicating over network 110 with one server computer 114. In another embodiment, for example, program 116 may operate on a plurality of server computers 114 communicating over a network 110 with a plurality of client computers. Alternatively, the program can run on a network server communicating over the network with the server and multiple client computers.

[18] Network 110 may include wired connections, wireless connections, fiber optic connections, or some combination thereof. In general, network 110 may be any combination of connections and protocols that can support communications between computer 102 and server computer 114. Network 110 may include various types of networks, such as, for example, a local area network (LAN), a wide area network (WAN). WAN) such as the Internet, a telecommunications network such as the public switched telephone network (PSTN), a wireless network, a public switched network, a satellite network, a cellular network (for example, a fifth generation (5G) network, a Long Term Evolution (LTE) network ), third generation network (3G), code division multiple access (CDMA), etc.), public land mobile network (PLMN), metropolitan area network (MAN), private network, decentralized network, intranet, fiber optic network, etc., and/or a combination of these or other types of networks.

[19] The number and location of devices and networks shown in Fig. 1 are given as an example. In practice, there may be additional devices and/or networks, fewer devices and/or networks, other devices and/or networks, or devices and/or networks located differently than those shown in FIG. In addition, two or more devices shown in FIG. 1 may be implemented within a single device, or a single device shown in FIG. 1 may be implemented as multiple distributed devices. Additionally or alternatively, a set of devices (e.g., one or more devices) of system 100 may perform one or more functions described as being performed by another set of devices of system 100.

[20] Referring to FIG. 2A, a diagram of an octree structure 200A is shown. In TMC13, if an octree geometry codec is used, the geometry encoding occurs as follows. First, the axis-aligned cubic bounding box B is defined by two points (0,0,0) and , Where determines size B, and is specified in the bit stream. The octree structure 200A is then constructed by recursively dividing B. At each step, the cube is divided into 8 subcubes. An 8-bit code, namely a busy code, is then generated by associating a 1-bit value with each subcube to indicate whether it contains points (i.e. full and has a value of 1) or not (i.e. empty and has a value 0). Only complete subcubes with a size greater than 1 (i.e., not voxels) are further divided.

[21] Referring to FIG. 2B, a 200B octree partitioning diagram is shown. The octree partition 200B may include a two-level octree partition 202 and a corresponding occupancy code 204, where dark cubes and nodes indicate that they are occupied by points. Each node's busy code 204 is then compressed by an arithmetic encoder. The busy code 204 can be denoted as S , which is an 8-bit integer, and each bit in S indicates the busy status of each child node. In TMC13, there are two encoding methods for busy code 204, i.e. bitwise encoding and bitwise encoding methods, and bitwise encoding is enabled by default. In either case, arithmetic encoding with context modeling is performed to encode the busy code 204, in which the context status is initialized at the beginning of the entire encoding process and updated during the encoding process.

[22] For bitwise encoding, the eight bins in S are encoded in a specific order, where each bin is encoded by referring to the occupancy status of neighboring nodes and children of neighboring nodes, where neighboring nodes are at the same level of the current node. For byte-byte encoding, S is encoded by accessing an adaptive lookup table (A-LUT) keeping track of the N (eg, 32) most frequent busy codes, and a cache keeping track of the last different observed M (eg, 16) busy codes.

[23] A binary flag is encoded indicating whether S is in the A-LUT or not. If S is in an A-LUT, the index in the A-LUT is encoded using a binary arithmetic encoder. If S is not in the A-LUT, then a binary flag is encoded indicating whether S is in the cache or not. If S is in the cache, then the binary representation of its index is encoded using a binary arithmetic encoder. Otherwise, if S is not in the cache, then the binary representation of S is encoded using a binary arithmetic encoder. The decoding process begins by parsing the size of bounding block B from the bitstream. The same octree structure is then constructed by partitioning B according to the decoded occupancy codes.

[24] The busy code of the current node is usually 8 bits, where each bit represents whether its child node i is busy or not. By encoding the busy code of the current node, all information from neighboring encoded nodes can be used for context modeling. Context information can be further grouped based on partition level and distance to the current node. Without loss of generality, the context index of child node i in the current node can be obtained as follows,

,

where LUT is the context index lookup table. And denote LUT indices representing neighbor information at the parent node level and at the child node level.

[25] Referring to FIG. 2C, a parent layer node neighbor block diagram 200C is shown. According to one or more embodiments, assume that the octree intersects in width order. The current node's six neighboring nodes can be used to set parent node-level contexts. The block in the center can be the current node, and the other six blocks can be nearest neighbors, where their distance to the current node is 1 block unit. Let denotes the employment status of neighbor i , i.e. , where i =0.1,…,5. Then the parent node level context index can be obtained as follows

Therefore, if all neighbors are occupied, the maximum value is 63, indicating that the number of contexts at the parent node level in this case is 64.

[26] Referring to FIG. 2D, a block diagram of parent node level contexts 200D is shown. In one or more embodiments, the octree is intersected in order of breadth. All nodes in the occupancy of the parent level can be encoded, and they can be used as contexts in the current level. The way context is accessed at the parent node level can be different for each child node with index i . For example, 8 subfigures could depict how to access the parent node level context for child node i ( i =0,1,...,7) of the current coded node. In each subfigure, the current coded node is highlighted in dark gray, and the smaller subblock in the current node is the child node i ( i =0,1,...,7); each child node has a different position relative to the current node. The way the parent node level context is accessed is different for each child node depending on its relative position to the current node.

[27] Referring to FIG. 2E, a block diagram 200E of the parent layer neighbors of the current coded node is shown. In one or more embodiments, when encoding child node i ( i =0,1,...,7), 7 adjacent nodes of child node i at the parent level are used as contexts. The 7 adjacent nodes of a child node i include 3 nodes that share an edge with the child node, and 3 nodes that share an edge, and 1 node that shares a vertex. Since different child nodes belong to different sets of parent nodes, a total of 26 parent-level neighboring nodes are used when encoding the current node. Compared to using only 6 parent-level neighbors, using 26 parent-level neighbors allows much more information to be obtained.

[28] Accordingly, the context index of child node i in the current node can be obtained as:

Where denotes the occupancy of adjacent node j at the parent level of child node i . equals 1 if adjacent node j at the parent level of child node i is occupied, and equals 0 otherwise, where i =0,1,…,7 and j =0,1,…,6.

[29] To reduce the number of contexts at the parent node level, in another embodiment, the index of the parent node level context can be obtained using,

,

in this case the maximum number is reduced to 6, which means that the number of contexts at the parent node level is reduced to 7 from 64.

[30] To further reduce the number of parent node level contexts, in another embodiment, the parent node level context is disabled by setting

.

In this case, neighbor information at the parent node level is not used at all.

[31] Referring to FIG. 2F, a block diagram 200F of parent node level contexts is shown. According to one or more embodiments, the number of parent level neighbors may be changed. For example, each child node can only use the parent's 6 nearest neighbors. The 8 subfigures depict how to access the parent node level context for child node i ( i =0,1,...,7) of the current coded node. In each subfigure, the current coded node is highlighted in dark gray, and the smaller subblock in the current node is the child node i ( i =0,1,...,7); each child node has a different position relative to the current node. The way the parent node level context is accessed is different for each child node depending on its relative position to the current node. When encoding child node i ( i =0,1,...,7), only 6 adjacent nodes of child node i at the parent level are used as contexts. The 6 adjacent nodes of a child node i may include 3 nodes that share an edge with the child node and 3 nodes that share an edge. Compared to FIG. 2D, a node sharing a vertex with a child node cannot be used in this case.

[32] Referring to FIG. 2G, a block diagram is depicted 200G depicting the parent layer neighbors of the current coded node based on the parent layer contexts of FIG. 2F. In one or more embodiments, a node that shares a vertex with a child node may not be used. Accordingly, a total of up to 18 parent level neighbors can be used, decreasing from 26 in FIG. 2D and 2E. Therefore, the context index of child node i in the current node can be obtained as:

Where denotes the occupancy of adjacent node j at the parent level of child node i. equals 1 if adjacent node j at the parent level of child node i is occupied, and equals 0 otherwise, where i =0,1,…,7 and j =0,1,…,5. Note that the number of parent-level neighbors can be further reduced by using only a subset of neighbors.

[33] To further reduce the number of contexts, it can be easier to obtain . In one embodiment, each child node belongs to 6 neighbors of the parent level, as shown in Fig. 6, and the context index of the parent level can be obtained as:

),

Where ( j =0,1,2) denotes the occupancy status of three parent-level nodes that share an edge with child node i , and ( j =3,4,5) denotes the occupancy status of three parent-level nodes that share an edge with child node i. In this case, neighbors sharing an edge have higher importance and the context index is calculated by taking into account all possible combinations of them, while neighbors sharing an edge have lower importance and the context index is calculated by simply counting how many of them are occupied. Thus, the maximum value decreases from 2 ⁶ - 1 to 2 ⁵ - 1, so the total number of contexts is halved. It should be noted that other similar simplification techniques may be used to obtain the parent-level context.

[34] Referring to FIG. 2H, a block diagram 200H of child node level neighbors is shown. In one or more embodiments, the level neighbors of a child node can be determined in a number of ways. For example, child-level neighbor nodes that are already encoded can be classified based on their distance to the central node. Without loss of generality, assuming that the size of each edge of the child nodes is 1, the distance l ₂ to the center is 1. And from left to right respectively.

[35] Let , And denote the occupancy status of neighbor i at the child node level with distance l ₂ equal to 1, And , accordingly, i.e. . It should be noted that the maximum number of coded neighbors of a child layer with a distance of 1. And can be 3, 9 and 7, respectively.

[36] Accordingly, the child node level context index can be obtained as:

Therefore, the maximum number of contexts at the child node level is 2 ¹⁹ , which is too many for applications. To reduce the number of contexts, one or more embodiments may consider partial neighbors at the child node level. This can reduce the maximum number of contexts at the child node level to 2 ⁷ =128.

[37] In another embodiment, the maximum number of contexts can be further simplified as:

where the maximum number of contexts at the child node level is reduced to .

[38] To further reduce the number of contexts, only the three closest nodes with distance l ₂ equal to 1 can be used, and the context index is calculated as:

,

where the maximum number of contexts at the child node level is reduced to 2 ³ =8.

[39] To further reduce the number of contexts, the relationship between parent-level contexts can be broken and index i of the child node. To achieve this, some rules can be defined. In one embodiment, each child node is related to 6 parent-level neighbors. The context index of child node i in the current node can be obtained as:

Where ( j =0,1,2) denotes the occupancy status of three parent-level nodes that share an edge with child node i , and ( j =3,4,5) denotes the occupancy status of three parent-level nodes that share an edge with child node i. It should be noted that the final context index is independent of the child index i. To achieve this, a transformation can be defined for each child node relative to the ordering of its parent-level neighboring nodes, i.e. . The transformation is described with reference to Fig. 3B. The gap reflects the symmetrical geometry between each child node and its 6 neighboring parent level nodes. It should be appreciated that other methods of breaking may be used by defining similar lookup tables in other embodiments.

[40] According to one or more embodiments, there may be no distinction between different child nodes when calculating the context to reduce the number of contexts. Specifically, the context index of child node i in the current node can be obtained as:

,

where i is no longer a dimension in the LUT. In other words, all child nodes are considered the same.

[41] Referring to FIG. 3A, syntax element 300A is shown. To achieve adaptability to different content types and scenarios, different context reduction strategies can be employed for different cases. Accordingly, high-level syntax may be signaled either in the sequence parameter set, or in the geometry parameter set, or in the slice data header, or elsewhere to indicate the level of context reduction. Based on the value of the specified context reduction level, the encoder and decoder can then decide how to use context information from neighboring coded nodes. The level of context reduction may correspond to a combination scheme of context reduction techniques.

[42] In one or more embodiments, syntax element 300A may be signaled in a geometry parameter set indicating the level of reduction of contexts, as shown in the table below. If the context reduction level is 0, which means no context reduction, then the normal context setting is applied. If the contexts reduction level is 1, low-level reduction is applied. If the context reduction level is 2, a mid-level reduction is applied. If the contexts reduction level is 3, high-level reduction is applied. With more context reduction levels, fewer contexts are used. In this case, different context configurations can be selected based on the contents and scenarios. Thus, gps_context_reduction_level can specify the reduction level of contexts in the geometry parameter set.

[43] Referring to FIG. 3B, a hash table 300B is shown. In one or more embodiments, encoded geometry occupancy information must be stored in cache because later encoded nodes need to use this information as context. Hash table 300B may be used to cache busy information. For each octree partition depth d , a hash table is maintained , where the key is the Morton code of the octree node at depth d , i.e. , Where - three-dimensional coordinates of an octree node. Using Morton's code as a key, you can access its busy value in a hash table . The occupancy value of octree node i at depth d can be obtained as:

.

When encoding/decoding the occupancy value of the current node, the occupancy information of neighboring nodes is obtained from the hash table . After encoding/decoding the busy value of the current node, the encoded busy value is then stored in .

[44] Referring to FIG. 4, a functional block diagram is depicted illustrating the steps of a method 400 performed by a program compressing and decompressing point cloud data.

[45] At 402, the method may include obtaining data corresponding to a point cloud.

[46] At 404, method 400 may include reducing the number of contexts associated with the received data based on busy data corresponding to one or more parent nodes and one or more child nodes in the received data.

[47] At 406, method 400 may include decoding data corresponding to a point cloud based on a reduced number of contexts.

[48] It should be understood that FIG. 4 is intended to illustrate one implementation only and is not intended to imply any limitation as to how various embodiments may be implemented. Numerous modifications can be made to the environments shown based on design and implementation requirements.

[49] FIG. 5 is a block diagram 500 of internal and external components of the computers depicted in FIG. 1, according to an example embodiment. It should be understood that FIG. 5 is intended to illustrate one implementation only and is not intended to be any limitation as to the environments in which the various embodiments may be implemented. Numerous modifications can be made to the environments shown based on design and implementation requirements.

[50] Computer 102 (FIG. 1) and server computer 114 (FIG. 1) may include respective sets of internal components 800A, B and external components 900A, B shown in FIG. 5. Each of the sets of internal components 800 includes one or more processors 820, one or more computer-readable RAM 822 and one or more computer-readable ROMs 824 on one or more buses 826, one or more operating systems 828, and one or more computer-readable physical storage devices 830 .

[51] The processor 820 is implemented in hardware, firmware, or a combination of hardware and software. The 820 processor is a central processing unit (CPU), graphics processing unit (GPU), accelerated processing unit (APU), microprocessor, microcontroller, digital signal processor (DSP), field programmable gate array (FPGA), application specific integrated circuit (ASIC), or component other type of processing. In some implementations, processor 820 includes one or more processors that can be programmed to perform a function. Bus 826 includes a component that provides communication between internal components 800A, B.

[52] One or more operating systems 828, software system program 108 (FIG. 1), and Point Cloud Compression Program 116 (FIG. 1) on server computer 114 (FIG. 1) are stored on one or more of respective machine-readable physical devices. storage 830 for execution by one or more respective processors 820 using one or more respective RAMs 822 (typically including cache memory). In the embodiment shown in FIG. 5, each of the computer-readable physical storage devices 830 is an internal hard drive magnetic disk storage device. Alternatively, each of the machine-readable physical storage devices 830 is a semiconductor storage device such as ROM 824, EPROM, flash memory, optical disk, magneto-optical disk, solid state disk, compact disc (CD), digital versatile disk (DVD), floppy disk, cartridge, magnetic tape, and/or other type of non-transitory machine-readable physical storage device that can store a computer program and digital information.

[53] Each set of internal components 800A, B also includes a drive or read/write interface 832 for reading from and writing to one or more portable computer-readable physical storage devices 936, such as a CD-ROM, DVD, memory card, magnetic tape , magnetic disk, optical disk, or semiconductor storage device. A software system program, such as a software system program 108 (FIG. 1) and a Point Cloud Compression Program 116 (FIG. 1), may be stored on one or more suitable portable machine-readable physical storage devices 936 readable using a suitable drive or interface. 832 read/write and bootable to the corresponding 830 hard drive.

[54] Each set of internal components 800A, B also includes network adapters or interfaces 836, such as TCP/IP adapter cards; Wi-Fi wireless interface cards; 3G, 4G or 5G wireless interface cards, or other wired or wireless communication channels. The software system program 108 (FIG. 1) and the Point Cloud Compression Program 116 (FIG. 1) on the server computer 114 (FIG. 1) can be loaded into the computer 102 (FIG. 1) and the server computer 114 from an external computer via the network. (e.g., the Internet, a local area network, or other wide area network) and corresponding network adapters or interfaces 836. From the network adapters or interfaces 836, the software system program 108 and the Point Cloud Compression Program 116 on the server computer 114 are loaded into the corresponding hard drive 830. Network may contain copper wires, optical fibers, wireless transmission, routers, firewalls, switches, gateway computers, and/or edge servers.

[55] Each of the sets of external components 900A, B may include a computer screen monitor 920, a keyboard 930, and a computer mouse 934. The external components 900A, B may also include touch screens, virtual keyboards, touchpads, pointing devices, and others. devices with human interface. Each of the sets of internal components 800A, B also includes device drivers 840 for interfacing with the computer screen monitor 920, keyboard 930, and computer mouse 934. Device drivers 840, read/write drive or interface 832, and network adapter or interface 836 contain hardware and software (stored in storage device 830 and/or ROM 824).

[56] It is understood in advance that although this invention includes a detailed description of cloud computing, the implementation of the ideas mentioned herein is not limited to the cloud computing environment. In contrast, some embodiments may be implemented in combination with any other type of computing environment currently known or later developed.

[57] Cloud computing is a service delivery model for providing convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, storage, storage, applications, virtual machines, and services) that can be quickly granted and withdrawn with minimal management effort or interaction with the service provider. This cloud model may include at least five characteristics, at least three service models, and at least four deployment models.

[58] Characteristics include the following:

On-demand self-service: The cloud user can unilaterally provision computing capabilities such as server time and network storage as needed automatically, without the need for human interaction with the service provider.

Broad network access: Capabilities can be networked and accessed through standard mechanisms, facilitating use across heterogeneous platforms of low-power or full-featured clients (eg, mobile phones, laptops, and PDAs).

Resource Pooling: The provider's computing resources are pooled to serve multiple users using a multi-tenant model, with various physical and virtual resources dynamically assigned and reassigned according to demand. There is a sense of location independence in the sense that the user generally has no control or knowledge of the exact location of the resources being provided, but can specify the location at a higher level of abstraction (e.g., country, state, or data center).

Fast Adaptability: Capabilities can be provisioned quickly and adaptively, in some cases automatically, to quickly scale out with increased performance, and quickly withdrawn to scale out quickly with decreased performance. To the user, the capabilities available to provide often appear unlimited and can be purchased in any quantity at any time.

Metered Service: Cloud systems automatically monitor and optimize resource usage by leveraging the ability to measure at some level of abstraction appropriate to the service type (e.g., storage, processing, throughput, and active user accounts). Resource usage can be tracked, monitored and reported, providing transparency to both the provider and the user of the service being used.

[59] Service models include the following:

Software as a Service (SaaS): The ability provided to the user is to use the provider's applications running on a cloud infrastructure. Applications are accessible from various client devices through a low-power client interface such as a web browser (for example, a web-based email service). The user does not manage or control the underlying cloud infrastructure, including the network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of user-defined limited application configuration options.

Platform as a Service (PaaS): This capability provided to the user is to deploy applications built or purchased by the user, built using programming languages and tools supported by the vendor, onto a cloud infrastructure. The user does not manage or control the underlying cloud infrastructure, including networks, servers, operating systems, or storage, but does have control over deployed applications and possibly application hosting environment configurations.

Infrastructure as a Service (laaS): This capability provided to the user is to provide processing, storage, networks and other core computing resources on which the user can deploy and run arbitrary software, which may include operating systems and applications. The user does not manage or control the underlying cloud infrastructure, but does have control over operating systems, storage, deployed applications, and perhaps limited control over individual network components (such as the host firewall).

[60] The following deployment models are available:

Private Cloud: Cloud infrastructure is used exclusively for the organization. It can be managed by an organization or a third party and can exist either on-premises or off-premises.

Community Cloud: Cloud infrastructure is shared across multiple organizations and supports a dedicated community that shares common interests (e.g., mission, security requirements, policy, and compliance considerations). It can be managed by organizations or a third party and can exist either on-premises or off-premises.

Public cloud: The cloud infrastructure is made available to the public or a large industry group and is owned by the organization that sells the cloud services.

Hybrid cloud: A cloud infrastructure is made up of two or more clouds (private, community, or public) that remain unique entities but are linked together by standardized or proprietary technology to enable data and application portability (e.g., cloud egress for load balancing across clouds) .

[61] The cloud computing environment is service-oriented with an emphasis on structurelessness, loose coupling, modularity, and semantic interoperability. At the heart of cloud computing is an infrastructure containing a network of interconnected nodes.

[62] FIG. 6 depicts an example cloud computing environment 600. As shown, cloud computing environment 600 includes one or more cloud computing nodes 10 with which local computing devices used by cloud users, such as, for example, a personal digital assistant (PDA) or cell phone 54A, desktop computer 54B, laptop computer, can communicate. 54C, and/or automotive computer system 54N. The cloud computing nodes 10 can communicate with each other. They may be grouped (not shown) physically or virtually in one or more networks, such as private, community, public or hybrid clouds as described above, or a combination thereof. This allows the cloud computing environment 600 to offer infrastructure, platforms, and/or software as services for which the cloud user does not need to maintain resources on a local computing device. It will be understood that the types of computing devices 54A-N shown in FIG. 6 are for illustration purposes only, and that cloud computing nodes 10 and cloud computing environment 600 may communicate with any type of computerized device over any type of network and/or network addressable connection ( for example using a web browser).

[63] Referring to FIG. 7, depicts a set of functional abstraction layers 700 provided by a cloud computing environment 600 (FIG. 6). It should be understood in advance that it is intended that the components, layers, and functions shown in FIG. 7 are merely illustrative and the embodiments are not limited to them. As shown, the following levels and corresponding functions are provided:

[64] Hardware and software layer 60 includes hardware and software components. Examples of hardware components include: mainframes 61; servers 62 based on RISC (reduced instruction set computer) architecture; servers 63; blade servers 64; storage devices 65; and networks and network components 66. In some embodiments, the software components include network application server software 67 and database software 68.

[65] Virtualization layer 70 provides an abstraction layer from which the following examples of virtual objects can be represented: virtual servers 71; virtual storage 72; virtual networks 73, including virtual private networks; virtual applications and operating systems 74; and virtual clients 75.

[66] In one example, control layer 80 may provide the functions described below. Resource provisioning 81 enables the dynamic provision of computing resources and other resources used to perform tasks in a cloud computing environment. Measuring and Defining 82 Cost allows you to track costs as resources are used in a cloud computing environment, and generate bills or invoices for the consumption of those resources. In one example, these resources may contain application software licenses. Security provides identity verification for cloud users and tasks, as well as protection for data and other resources. The user portal 83 provides access to the cloud computing environment for users and system administrators. Service level management 84 provides cloud computing resource allocation and management to ensure that required service levels are met. Planning and Execution 85 Service Level Agreement (SLA) involves the pre-arrangement and procurement of cloud computing resources for which future demand is expected according to the SLA.

[67] Workload Level 90 provides examples of functionality for which a cloud computing environment can be used. Examples of workloads and functions that may be present at this layer include: mapping and navigation 91; software development and 92 life cycle management; providing 93 virtual classroom training; analytical processing of 94 data; processing 95 transactions; and Compress 96 Point Clouds. Point Cloud Compression 96 can reduce an extended set of neighbors for the current node to compress and decompress point cloud data.

[68] Some embodiments may relate to a system, method, and/or computer-readable medium at any possible level of technical integration details. A computer-readable medium may include a computer-readable non-transitory storage medium (or media) containing computer-readable program instructions thereon that cause a processor to perform operations.

[69] A computer-readable medium may be a physical device that can store and store instructions for use by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of computer readable storage media includes the following: portable computer diskette, hard disk, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM or flash memory), static storage device random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital versatile disk (DVD), memory card, floppy disk, mechanically encoded device such as punch cards or raised structures in a groove with instructions written on them , and any suitable combination of the above. The computer-readable storage medium used herein should not be construed as representing intrinsically transient signals, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (for example, light pulses passing through a fiber optic cable) or electrical signals transmitted through wires.

[70] The computer-readable program instructions described herein may be downloaded to appropriate computing/processing devices from a computer-readable storage medium or to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and/or a wireless network. . The network may contain copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers, and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage on a computer-readable storage medium in the corresponding computing/processing device.

[71] The machine-readable program code/instructions for performing operations may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, configuration data for an integrated circuit, or source code, or object code, written in any combination of one or more programming languages, including an object-oriented programming language such as Smalltalk, C++, etc., and procedural programming languages such as the C programming language or similar languages programming. The machine-readable software instructions may be executed entirely on the user's computer, partially on the user's computer as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or wide area network (WAN), or the connection can be made to an external computer (for example, over the Internet using an Internet service provider). In some embodiments, electronic circuits, including, for example, programmable logic circuits, field programmable gate arrays (FPGAs), or programmable gate arrays (PLAs), can execute computer-readable program instructions by using state information of the computer-readable program instructions to configure the electronic circuit to execute aspects or operations.

[72] These machine-readable program instructions may be transmitted to a processor of a general purpose computer, special purpose computer, or other programmable data processing device to create a machine, such that the instructions executed by the processor of the computer or other programmable data processing device create the means for implementing the functions/actions indicated in the block diagram and/or block or blocks of the block diagram. These computer-readable program instructions may also be stored on a computer-readable storage medium that can control a computer, programmable data processing device, and/or other devices to operate in a particular manner, such that the computer-readable storage medium containing the instructions stored thereon contains a finished product, including the instructions , implementing aspects of the function/action specified in the block diagram and/or block or blocks of the block diagram.

[73] Machine-readable program instructions may also be loaded onto a computer, other programmable data processing device, or other device to cause a number of operational steps to be executed on the computer, other programmable device, or other device to create a computer-implemented process such that the instructions are executable on the computer , another programmable device, or other device, implement the functions/actions specified in the block diagram and/or block diagram block or blocks.

[74] The block diagram and block diagrams in the drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer-readable media in accordance with various embodiments. In this regard, each block in a block diagram or block diagrams may represent a module, segment, or part of instructions containing one or more executable instructions to implement specified logical function(s). The method, computer system, and computer-readable media may include additional blocks, fewer blocks, different blocks, or blocks arranged differently than those depicted in the drawings. In some alternative implementations, the functions shown in the blocks may not appear in the order shown in the drawings. For example, two blocks shown sequentially may actually execute simultaneously or substantially simultaneously, or the blocks may sometimes execute in reverse order, depending on the functionality involved. It should also be noted that each block of the block diagrams and/or block diagram images, as well as combinations of blocks in the block diagrams and/or block diagram images, can be implemented using special purpose hardware-oriented systems that perform specified functions or actions, or executing combinations of hardware and computer instructions for special purposes.

[75] It is understood that the systems and/or methods described herein may be implemented in various forms of hardware, firmware, or combinations of hardware and software. The actual specialized hardware or software control code used to implement these systems and/or methods does not limit the implementation. Thus, the operation and behavior of the systems and/or methods have been described herein without reference to specific software code - it being understood that software and hardware may be developed to implement the systems and/or methods based on the descriptions herein.

[76] No item, action or instruction used in this document should be considered critical or essential unless expressly described as such. It is also intended that the singular number as used herein includes one or more elements and may be used interchangeably with “one or more.” Additionally, as used herein, the expression “set” is intended to include one or more elements (e.g., related elements, unrelated elements, a combination of related and unrelated elements, etc.), and may be used interchangeably with “one or more". If only one element is intended, the expression "one" or similar expression is used. It is also intended that the expressions “has,” “have,” “having,” and the like, when used herein. are non-limiting expressions. In addition, the expression “based on” is intended to mean “based at least in part on” unless expressly stated otherwise.

[77] Descriptions of various aspects and embodiments have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Although combinations of features are set forth in the claims and/or disclosed in the specification, these combinations are not intended to limit the disclosure of possible implementations. In fact, many of these features may be combined in ways not specifically stated in the claims and/or disclosed in the specification. Although each dependent claim below may depend directly on only one claim, the disclosure of possible implementations includes each dependent claim in combination with each other claim within the claims. Numerous modifications and variations may be apparent to those skilled in the art without departing from the scope of the described embodiments. The terminology used herein has been chosen to better explain the principles of embodiments, practical applications or technical advancements with respect to technologies available on the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims (30)

1. A method for decoding point cloud data performed by a processor, comprising: obtaining data corresponding to the point cloud; reducing the number of contexts associated with the received data based on the busy data corresponding to one or more parent nodes and one or more child nodes in the received data; decoding data corresponding to a point cloud based on a reduced number of contexts, wherein for parent nodes that share an edge with child nodes, the parent-level context index is calculated by taking into account all possible combinations, and for parent nodes that share an edge with child nodes, the parent-level context index is calculated by simply counting how many parent nodes are occupied. 2. The method of claim 1, further comprising caching the busy information based on a hash table. 3. The method of claim 1, further comprising signaling the level of context reduction in the sequence parameter set, geometry parameter set, or slice data header. 4. The method of claim 1, wherein the number of contexts is reduced based on one or more child nodes having the shortest distance from the current node. 5. The method of claim 1, wherein the number of contexts is reduced based on considering only a subset of child nodes of the one or more child nodes. 6. The method of claim 1, wherein the number of contexts is reduced based on non-discrimination between one or more child nodes. 7. The method of claim 1, wherein the number of contexts is reduced based on the elimination of busy data associated with one or more parent nodes. 8. A computer system for decoding point cloud data, the computer system comprising: one or more computer-readable non-transitory storage media configured to store computer program code; And one or more computer processors configured to access said computer program code and operate in accordance with the instructions of said computer program code, wherein said computer program code includes: receiving code configured to cause one or more computer processors to receive data corresponding to the point cloud; reduction code configured to cause one or more computer processors to reduce the number of contexts associated with the received data based on busy data corresponding to one or more parent nodes and one or more child nodes in the received data; decoding code configured to cause one or more computer processors to decode data corresponding to the point cloud based on the reduced number of contexts, wherein for parent nodes that share an edge with child nodes, the parent-level context index is calculated by taking into account all possible combinations, and for parent nodes that share an edge with child nodes, the parent-level context index is calculated by simply counting how many parent nodes are occupied. 9. The computer system of claim 8, further comprising caching code configured to cause one or more computer processors to cache busy information based on a hash table. 10. The computer system of claim 8, further comprising signaling code configured to cause one or more computer processors to signal a reduction level of contexts in the sequence parameter set, geometry parameter set, or slice data header. 11. The computer system of claim 8, wherein the number of contexts is reduced based on one or more child nodes having the shortest distance from the current node. 12. The computer system of claim 8, wherein the number of contexts is reduced by considering only a subset of child nodes of the one or more child nodes. 13. The computer system of claim 8, wherein the number of contexts is reduced based on non-discrimination between one or more child nodes. 14. The computer system of claim 8, wherein the number of contexts is reduced based on the elimination of busy data associated with one or more parent nodes. 15. A method for encoding point cloud data performed by a processor, comprising: determining data corresponding to the point cloud; reducing the number of contexts associated with the data based on the busy data corresponding to one or more parent nodes and one or more child nodes in the data; encoding data corresponding to a point cloud based on a reduced number of contexts, wherein for parent nodes that share an edge with child nodes, the parent-level context index is calculated by taking into account all possible combinations, and for parent nodes that share an edge with child nodes, the parent-level context index is calculated by simply counting how many parent nodes are occupied. 16. A non-transitory computer-readable medium having a computer program stored thereon for decoding/encoding point cloud data, wherein the computer program is configured to cause one or more computer processors to implement the method of any one of claims 1 to 7 and 15.