Point cloud data transmission device, point cloud data transmission method, point cloud data reception device, and point cloud data reception method

Abstract

A decoding method according to embodiments comprises the steps of: decoding geometry information in point cloud data; and decoding attribute information in the point cloud data, wherein the step of decoding geometry information can comprise the steps of: dividing the geometry information into a plurality of prediction units on the basis of information related to scene configuration; and configuring a scene on the basis of the plurality of prediction units.

Description

Point cloud data transmission device, point cloud data transmission method, point cloud data reception device, and point cloud data reception method

[0001] Embodiments relate to a method and apparatus for processing point cloud content.

[0002] Point cloud content is content expressed as a point cloud, a collection of points belonging to a coordinate system that represents three-dimensional space (space or volume). Point cloud content can express three-dimensional media and is used to provide various services such as VR (Virtual Reality), AR (Augmented Reality), MR (Mixed Reality), XR (Extended Reality), and autonomous driving services. However, expressing point cloud content requires tens to hundreds of thousands of point data. Therefore, a method for efficiently processing massive amounts of point data is required.

[0003] That is, transmitting and receiving point cloud data requires a significant amount of processing power. Therefore, encoding for compression and decoding for decompression are performed during the process of transmitting and receiving point cloud data. However, the large size of point cloud data makes the computations complex and time-consuming.

[0004] The technical problem according to the embodiments is to provide a device and method for efficiently transmitting and receiving a point cloud in order to solve the problems described above.

[0005] The technical problem according to the embodiments is to provide a device and method for resolving latency and encoding / decoding complexity.

[0006] The technical problem according to the embodiments is to provide a device and method for improving the compression performance of a point cloud by improving the encoding technology of attribute information of geometry-based point cloud compression (G-PCC).

[0007] A technical problem according to embodiments is to provide a device and method for improving compression performance of geometry information by configuring a scene in specific units.

[0008] However, the scope of the embodiments is not limited to the aforementioned technical tasks, and the scope of the embodiments may be expanded to other technical tasks that can be inferred by a person skilled in the art based on the entire contents of this document.

[0009] To achieve the above-described purpose and other advantages, a decoding method according to embodiments may include a step of receiving a bitstream including point cloud data, and a step of decoding the point cloud data.

[0010] According to embodiments, the step of decoding the point cloud data may include the step of decoding geometry information within the point cloud data, and the step of decoding attribute information within the point cloud data.

[0011] According to embodiments, the step of decoding the geometry information may include the step of dividing the geometry information into a plurality of prediction units based on information related to scene composition, and the step of composing a scene based on the plurality of prediction units.

[0012] According to embodiments, the scene has a minimum k containing the point cloud data2 k It consists of a bounding box area, and each of the plurality of prediction units can have origin information and size information.

[0013] According to embodiments, the bitstream includes information related to the scene configuration, the information related to the scene configuration includes information indicating whether a scene is configured and scene configuration information, and the scene configuration information may include identification information, origin information, and size information for each of the plurality of prediction units.

[0014] According to embodiments, the scene configuration information may further include threshold information for the scene configuration.

[0015] According to embodiments, a decoding device may include a memory and at least one processor connected to the memory, wherein the at least one processor may be configured to receive a bitstream including point cloud data and decode the point cloud data.

[0016] According to embodiments, the at least one processor may include a geometry decoder that decodes geometry information within the point cloud data, and an attribute decoder that decodes attribute information within the point cloud data.

[0017] According to embodiments, the geometry decoder can divide the geometry information into a plurality of prediction units based on information related to scene composition, and compose a scene based on the plurality of prediction units.

[0018] According to embodiments, the scene has a minimum k containing the point cloud data2 k It consists of a bounding box area, and each of the plurality of prediction units can have origin information and size information.

[0019] According to embodiments, the bitstream includes information related to the scene configuration, the information related to the scene configuration includes information indicating whether a scene is configured and scene configuration information, and the scene configuration information may include identification information, origin information, and size information for each of the plurality of prediction units.

[0020] According to embodiments, the scene configuration information may further include threshold information for the scene configuration.

[0021] According to embodiments, the encoding method may include the steps of encoding point cloud data, and transmitting a bitstream including the point cloud data.

[0022] According to embodiments, the step of encoding the point cloud data may include a step of encoding geometry information within the point cloud data and a step of encoding attribute information within the point cloud data.

[0023] According to embodiments, the step of encoding the geometry information may include the step of dividing the geometry information into a plurality of prediction units based on information related to scene composition, and the step of composing a scene based on the plurality of prediction units.

[0024] According to embodiments, the scene has a minimum k containing the point cloud data2 k It consists of a bounding box area, and each of the plurality of prediction units can have origin information and size information.

[0025] According to embodiments, the bitstream includes information related to the scene configuration, the information related to the scene configuration includes information indicating whether a scene is configured and scene configuration information, and the scene configuration information may include identification information, origin information, and size information for each of the plurality of prediction units.

[0026] According to embodiments, the scene configuration information may further include threshold information for the scene configuration.

[0027] The device and method according to the embodiments can provide a quality point cloud service.

[0028] The devices and methods according to the embodiments can achieve various video codec schemes.

[0029] The device and method according to the embodiments can provide general-purpose point cloud content such as autonomous driving services.

[0030] The device and method according to the embodiments can provide improved parallel processing and scalability by performing spatially adaptive segmentation of point cloud data for independent encoding and decoding of point cloud data.

[0031] The device and method according to the embodiments can improve the encoding and decoding performance of a point cloud by spatially dividing point cloud data into tiles and / or slices to perform encoding and decoding and signaling data required for this.

[0032] The device and method according to the embodiments can efficiently apply coding tools, interface tools, etc. by combining a scene description or scene graph with a compression unit when compressing captured content, thereby reducing the bitstream configuration and size of the captured content, thereby efficiently supporting compression of the captured content.

[0033] The device and method according to the embodiments can more efficiently compress geometry information by composing a scene while compressing a point cloud in the case of content used in real time.

[0034] The drawings are included to further understand the embodiments, and the drawings illustrate the embodiments together with the description related to the embodiments.

[0035] Figure 1 illustrates an example of a point cloud content provision system according to embodiments.

[0036] FIG. 2 is a block diagram illustrating a point cloud content provision operation according to embodiments.

[0037] FIG. 3 illustrates an example of a point cloud encoder according to embodiments.

[0038] Figure 4 illustrates examples of octree and occupancy codes according to embodiments.

[0039] Figure 5 shows an example of a point configuration by LOD according to embodiments.

[0040] Figure 6 shows an example of a point configuration by LOD according to embodiments.

[0041] Fig. 7 illustrates an example of a point cloud decoder according to embodiments.

[0042] Figure 8 is an example of a transmission device according to embodiments.

[0043] Fig. 9 is an example of a receiving device according to embodiments.

[0044] Fig. 10 shows an example of a structure that can be linked with a point cloud data transmission / reception method / device according to embodiments.

[0045] FIG. 11 is a diagram showing an example of a scene graph according to embodiments.

[0046] Figure 12 is a diagram showing an example of a process unit that constitutes a scene according to embodiments.

[0047] FIG. 13 is a drawing showing another example of a point cloud transmission device according to embodiments.

[0048] FIG. 14 is a diagram showing an example of detailed operation of a geometry encoder and an attribute encoder according to embodiments.

[0049] FIG. 15 is a drawing showing another example of a point cloud receiving device according to embodiments.

[0050] FIG. 16 is a diagram showing an example of detailed operation of a geometry decoder and an attribute decoder according to embodiments.

[0051] Fig. 17 is a diagram showing the connection relationship between components within a bitstream of point cloud data according to embodiments.

[0052] FIG. 18 is a diagram showing an example of a syntax structure of a sequence parameter set according to embodiments.

[0053] FIG. 19 is a diagram showing an example of a syntax structure of a geometry parameter set according to embodiments.

[0054] FIG. 20 is a diagram showing an example of a syntax structure of a tile parameter set according to embodiments.

[0055] FIG. 21 is a diagram showing an example of a syntax structure of a geometry slice header according to embodiments.

[0056] Fig. 22 shows a flowchart of a point cloud data transmission method according to embodiments.

[0057] Fig. 23 shows a flowchart of a method for receiving point cloud data according to embodiments.

[0058] Hereinafter, embodiments disclosed in this specification will be described in detail with reference to the attached drawings. Regardless of the reference numerals used in the drawings, identical or similar components will be assigned the same reference numerals, and redundant descriptions thereof will be omitted. The following embodiments are intended to concretize the present disclosure and do not limit or restrict the scope of the present disclosure. Anything that a specialist in the technical field to which the present disclosure pertains can easily infer from the detailed description and embodiments of the present disclosure is interpreted as falling within the scope of the present disclosure.

[0059] The detailed description herein is not to be construed in any way as limiting, but rather as illustrative. The scope of this disclosure should be determined by a reasonable interpretation of the appended claims, and all changes within the equivalent scope of this disclosure are intended to be embraced therein.

[0060] Preferred embodiments are described in detail, examples of which are illustrated in the accompanying drawings. The following detailed description with reference to the accompanying drawings is intended to illustrate preferred embodiments rather than merely show possible embodiments. The following description includes details to provide a thorough understanding of the present disclosure. However, it will be apparent to those skilled in the art that the present disclosure may be practiced without such details. Most of the terms used in this specification are selected from those commonly used in the relevant field, but some terms are arbitrarily selected by the applicant, and their meanings are described in detail in the following description as needed. Therefore, the present disclosure should be understood based on the intended meaning of the terms, not the simple name or meaning of the terms. In addition, the drawings and detailed description below should not be interpreted as being limited to the specifically described embodiments, but should be interpreted to include equivalents or alternatives to the embodiments described in the drawings and detailed description.

[0061] FIG. 1 illustrates an example of a point cloud content provision system according to embodiments.

[0062] The point cloud content provision system illustrated in FIG. 1 may include a transmission device (10000) and a reception device (10004). The transmission device (10000) and the reception device (10004) are capable of wired and wireless communication to transmit / receive point cloud data.

[0063] A transmission device (10000) according to embodiments can secure, process, and transmit a point cloud video (or point cloud content). According to embodiments, the transmission device (10000) can include a fixed station, a base transceiver system (BTS), a network, an Artificial Intelligence (AI) device and / or system, a robot, an AR / VR / XR device and / or a server, etc. In addition, according to embodiments, the transmission device (10000) can include a device that performs communication with a base station and / or other wireless devices using a wireless access technology (e.g., 5G NR (New RAT), LTE (Long Term Evolution)), a robot, a vehicle, an AR / VR / XR device, a portable device, a home appliance, an IoT (Internet of Things) device, an AI device / server, etc.

[0064] A transmission device (10000) according to embodiments includes a Point Cloud Video Acquisition unit (10001), a Point Cloud Video Encoder (10002), and / or a Transmitter (or Communication module), 10003.

[0065] A point cloud video acquisition unit (10001) according to embodiments acquires a point cloud video through a processing process such as capture, synthesis, or generation. The point cloud video is point cloud content expressed as a point cloud, which is a collection of points located in a three-dimensional space, and may be referred to as point cloud video data, etc. The point cloud video according to embodiments may include one or more frames. One frame represents a still image / picture. Therefore, the point cloud video may include a point cloud image / frame / picture, and may be referred to as any one of a point cloud image, a frame, and a picture.

[0066] A point cloud video encoder (10002) according to embodiments encodes acquired point cloud video data. The point cloud video encoder (10002) may encode point cloud video data based on point cloud compression coding. The point cloud compression coding according to embodiments may include G-PCC (Geometry-based Point Cloud Compression) coding and / or V-PCC (Video-based Point Cloud Compression) coding or next-generation coding. In addition, the point cloud compression coding according to embodiments is not limited to the above-described embodiment. The point cloud video encoder (10002) may output a bitstream including encoded point cloud video data. The bitstream may include not only encoded point cloud video data but also signaling information related to encoding of the point cloud video data.

[0067] A transmitter (10003) according to embodiments transmits a bitstream including encoded point cloud video data. The bitstream according to embodiments is encapsulated into a file or segment (e.g., streaming segment) and transmitted through various networks such as a broadcast network and / or a broadband network. Although not shown in the drawing, the transmission device (10000) may include an encapsulation unit (or an encapsulation module) that performs an encapsulation operation. In addition, the encapsulation unit may be included in the transmitter (10003) according to embodiments. According to embodiments, the file or segment may be transmitted to a receiving device (10004) through a network or may be stored in a digital storage medium (e.g., USB, SD, CD, DVD, Blu-ray, HDD, SSD, etc.). The transmitter (10003) according to embodiments may communicate with the receiving device (10004) (or receiver (10005)) via a network such as 4G, 5G, or 6G via wired / wireless communication. Additionally, the transmitter (10003) can perform data processing operations required according to a network system (e.g., a communication network system such as 4G, 5G, or 6G). Additionally, the transmission device (10000) can transmit encapsulated data in an on-demand manner.

[0068] A receiving device (10004) according to embodiments includes a receiver (10005), a point cloud video decoder (10006), and / or a renderer (10007). According to embodiments, the receiving device (10004) may include a device, robot, vehicle, AR / VR / XR device, mobile device, home appliance, IoT (Internet of Things) device, AI device / server, etc. that performs communication with a base station and / or other wireless devices using a wireless access technology (e.g., 5G NR (New RAT), LTE (Long Term Evolution)).

[0069] A receiver (10005) according to embodiments receives a bitstream containing point cloud video data or a file / segment in which the bitstream is encapsulated, from a network or a storage medium. The receiver (10005) may perform data processing operations required according to a network system (e.g., a communication network system such as 4G, 5G, or 6G). The receiver (10005) according to embodiments may decapsulate the received file / segment and output a bitstream. In addition, the receiver (10005) according to embodiments may include a decapsulation unit (or decapsulation module) for performing the decapsulation operation. In addition, the decapsulation unit may be implemented as a separate element (or component) from the receiver (10005).

[0070] A point cloud video decoder (10006) decodes a bitstream containing point cloud video data. The point cloud video decoder (10006) can decode the point cloud video data according to how it is encoded (e.g., the reverse process of the operation of the point cloud video encoder (10002)). Accordingly, the point cloud video decoder (10006) can decode the point cloud video data by performing point cloud decompression coding, which is the reverse process of point cloud compression. The point cloud decompression coding includes G-PCC coding.

[0071] The renderer (10007) renders the decoded point cloud video data. In one embodiment, the renderer (10007) may render the decoded point cloud video data according to a viewport, etc. The renderer (10007) may render not only the point cloud video data but also audio data to output point cloud content. According to embodiments, the renderer (10007) may include a display for displaying the point cloud content. According to embodiments, the display may not be included in the renderer (10007) but may be implemented as a separate device or component.

[0072] The dotted arrows in the drawing indicate the transmission path of feedback information acquired from the receiving device (10004). The feedback information is information for reflecting the interaction with the user consuming the point cloud content, and includes information about the user (e.g., head orientation information, viewport information, etc.). In particular, when the point cloud content is content for a service requiring interaction with the user (e.g., autonomous driving service, etc.), the feedback information may be transmitted to the content transmitter (e.g., the transmitting device (10000)) and / or the service provider. Depending on the embodiments, the feedback information may be used not only by the transmitting device (10000) but also by the receiving device (10004), or may not be provided.

[0073] Head orientation information according to embodiments may refer to information about the position, direction, angle, movement, etc. of the user's head. The receiving device (10004) according to embodiments may calculate viewport information based on the head orientation information. The viewport information is information about the area of ​​the point cloud video that the user is looking at (i.e., the area that the user is currently viewing). In other words, the viewport information is information about the area that the user is currently viewing within the point cloud video. In other words, the viewport or the viewport area may refer to the area that the user is viewing within the point cloud video. In addition, the viewpoint is the point that the user is viewing within the point cloud video, and may refer to the exact center point of the viewport area. In other words, the viewport is an area centered on the viewpoint, and the size, shape, etc. that the area occupies may be determined by the FOV (Field Of View). Therefore, the receiving device (10004) may extract viewport information based on the vertical or horizontal FOV supported by the device in addition to the head orientation information. In addition, the receiving device (10004) may perform gaze analysis, etc. based on head orientation information and / or viewport information to check the user's point cloud video consumption method, the point cloud video area the user gazes at, the gaze time, etc. According to embodiments, the receiving device (10004) may transmit feedback information including the gaze analysis result to the transmitting device (10000). According to embodiments, a device such as a VR / XR / AR / MR display may extract a viewport area based on the user's head position / direction, a vertical or horizontal FOV supported by the device, etc. According to embodiments, head orientation information and viewport information may be referred to as feedback information, signaling information, or metadata.

[0074] Feedback information according to embodiments may be acquired during the rendering and / or display process. The feedback information according to embodiments may be acquired by one or more sensors included in the receiving device (10004). Additionally, the feedback information according to embodiments may be acquired by the renderer (10007) or a separate external element (or device, component, etc.). The dotted line in Fig. 1 represents the transmission process of the feedback information acquired by the renderer (10007). The feedback information may not only be transmitted to the transmitting side, but may also be consumed by the receiving side. That is, the point cloud content providing system may process (encode / decode / render) point cloud data based on the feedback information. For example, the point cloud video decoder (10006) and the renderer (10007) may use the feedback information, i.e., head orientation information and / or viewport information, to preferentially decode and render only the point cloud video for the area currently being viewed by the user.

[0075] Additionally, the receiving device (10004) can transmit feedback information to the transmitting device (10000). The transmitting device (10000) (or point cloud video encoder (10002)) can perform an encoding operation based on the feedback information. Therefore, the point cloud content providing system can efficiently process necessary data (e.g., point cloud data corresponding to the user's head position) based on the feedback information without processing (encoding / decoding) all point cloud data, and provide point cloud content to the user.

[0076] According to embodiments, the transmitting device (10000) may be referred to as an encoder, a transmitting device, a transmitter, a transmitting system, etc., and the receiving device (10004) may be referred to as a decoder, a receiving device, a receiver, a receiving system, etc.

[0077] Point cloud data processed (processed through a series of processes of acquisition / encoding / transmission / decoding / rendering) in the point cloud content providing system of FIG. 1 according to embodiments may be referred to as point cloud content data or point cloud video data. According to embodiments, point cloud content data may be used as a concept including metadata or signaling information related to point cloud data.

[0078] The elements of the point cloud content provision system illustrated in FIG. 1 may be implemented by hardware, software, a processor, and / or a combination thereof.

[0079] FIG. 2 is a block diagram illustrating a point cloud content provision operation according to embodiments.

[0080] The block diagram of Fig. 2 illustrates the operation of the point cloud content provision system described in Fig. 1. As described above, the point cloud content provision system can process point cloud data based on point cloud compression coding (e.g., G-PCC).

[0081] A point cloud content providing system according to embodiments (e.g., a point cloud transmission device (10000) or a point cloud video acquisition unit (10001)) can acquire a point cloud video (20000). The point cloud video is expressed as a point cloud belonging to a coordinate system representing a three-dimensional space. The point cloud video according to embodiments can include a Ply (Polygon File format or the Stanford Triangle format) file. If the point cloud video has one or more frames, the acquired point cloud video can include one or more Ply files. The Ply file includes point cloud data such as the geometry and / or attributes of points. The geometry includes the positions of points. The position of each point can be expressed as parameters (e.g., values ​​of each of the X-axis, Y-axis, and Z-axis) representing a three-dimensional coordinate system (e.g., a coordinate system composed of XYZ axes). Attributes include attributes of points (e.g., texture information of each point, color (YCbCr or RGB), reflectance (r), transparency, etc.). One point has one or more attributes (or properties). For example, one point may have one attribute of color, or two attributes of color and reflectance. According to embodiments, geometry may be referred to as positions, geometry information, geometry data, etc., and attributes may be referred to as attributes, attribute information, attribute data, etc. In addition, a point cloud content providing system (e.g., a point cloud transmission device (10000) or a point cloud video acquisition unit (10001)) may obtain point cloud data from information related to the acquisition process of a point cloud video (e.g., depth information, color information, etc.).

[0082] A point cloud content providing system according to embodiments (e.g., a transmission device (10000) or a point cloud video encoder (10002)) can encode point cloud data (20001). The point cloud content providing system can encode point cloud data based on point cloud compression coding. As described above, point cloud data can include geometry and attributes of points. Therefore, the point cloud content providing system can perform geometry encoding to encode geometry and output a geometry bitstream. The point cloud content providing system can perform attribute encoding to encode attributes and output an attribute bitstream. According to embodiments, the point cloud content providing system can perform attribute encoding based on geometry encoding. The geometry bitstream and the attribute bitstream according to embodiments can be multiplexed and output as a single bitstream. A bitstream according to embodiments may further include signaling information related to geometry encoding and attribute encoding.

[0083] A point cloud content providing system according to embodiments (e.g., a transmission device (10000) or a transmitter (10003)) can transmit encoded point cloud data (20002). As described in FIG. 1, the encoded point cloud data can be expressed as a geometry bitstream and an attribute bitstream. In addition, the encoded point cloud data can be transmitted in the form of a bitstream together with signaling information related to encoding of the point cloud data (e.g., signaling information related to geometry encoding and attribute encoding). In addition, the point cloud content providing system can encapsulate a bitstream that transmits the encoded point cloud data and transmit it in the form of a file or segment.

[0084] A point cloud content providing system according to embodiments (e.g., a receiving device (10004) or a receiver (10005)) can receive a bitstream including encoded point cloud data. In addition, the point cloud content providing system (e.g., a receiving device (10004) or a receiver (10005)) can demultiplex the bitstream.

[0085] A point cloud content providing system (e.g., a receiving device (10004) or a point cloud video decoder (10005)) can decode encoded point cloud data (e.g., a geometry bitstream, an attribute bitstream) transmitted as a bitstream. The point cloud content providing system (e.g., a receiving device (10004) or a point cloud video decoder (10005)) can decode the point cloud video data based on signaling information related to encoding of the point cloud video data included in the bitstream. The point cloud content providing system (e.g., a receiving device (10004) or a point cloud video decoder (10005)) can decode the geometry bitstream to restore positions (geometry) of points. The point cloud content providing system can decode the attribute bitstream based on the restored geometry to restore attributes of points. A point cloud content provision system (e.g., a receiving device (10004) or a point cloud video decoder (10005)) can reconstruct a point cloud video based on positions and decoded attributes according to the reconstructed geometry.

[0086] A point cloud content providing system (e.g., a receiving device (10004) or a renderer (10007)) according to embodiments can render decoded point cloud data (20004). The point cloud content providing system (e.g., a receiving device (10004) or a renderer (10007)) can render the geometry and attributes decoded through the decoding process according to various rendering methods. Points of the point cloud content may be rendered as vertices having a certain thickness, cubes having a certain minimum size centered on the vertex position, or circles centered on the vertex position. All or a portion of the rendered point cloud content is provided to a user through a display (e.g., a VR / AR display, a general display, etc.).

[0087] A point cloud content provision system according to embodiments (e.g., a receiving device (10004)) can obtain feedback information (20005). The point cloud content provision system can encode and / or decode point cloud data based on the feedback information. The feedback information and the operation of the point cloud content provision system according to embodiments are identical to the feedback information and operation described in FIG. 1, and therefore, a detailed description thereof will be omitted.

[0088] FIG. 3 illustrates an example of a point cloud encoder according to embodiments.

[0089] FIG. 3 illustrates an example of a point cloud video encoder (10002) of FIG. 1. The point cloud encoder reconstructs point cloud data (e.g., positions and / or attributes of points) and performs an encoding operation to adjust the quality of point cloud content (e.g., lossless, lossy, near-lossless) depending on network conditions or applications. If the total size of the point cloud content is large (e.g., point cloud content of 60 Gbps at 30 fps), the point cloud content provision system may not be able to stream the content in real time. Therefore, the point cloud content provision system can reconstruct the point cloud content based on the maximum target bitrate in order to provide it according to the network environment, etc.

[0090] As described in FIGS. 1 and 2, the point cloud encoder can perform geometry encoding and attribute encoding. Geometry encoding is performed before attribute encoding.

[0091] The point cloud encoder according to the embodiments includes a coordinate system transformation unit (Transformation Coordinates, 30000), a quantization unit (Quantize and Remove Points (Voxelize), 30001), an octree analysis unit (Analyze Octree, 30002), a surface approximation analysis unit (Analyze Surface Approximation, 30003), an arithmetic encoder (Arithmetic Encode, 30004), a geometry reconstruction unit (Reconstruct Geometry, 30005), a color transformation unit (Transform Colors, 30006), an attribute transformation unit (Transfer Attributes, 30007), a RAHT transformation unit (30008), a LOD generation unit (Generated LOD, 30009), a lifting transformation unit (Lifting) (30010), and a coefficient quantization unit (Quantize Coefficients, 30011) and / or an arithmetic encoder (30012). In the point cloud encoder of FIG. 3, the coordinate system transformation unit (30000), the quantization unit (30001), the octree analysis unit (30002), the surface approximation analysis unit (30003), the arithmetic encoder (30004), and the geometry reconstruction unit (30005) can be grouped and referred to as a geometry encoder. In addition, the color conversion unit (30006), attribute conversion unit (30007), RAHT conversion unit (30008), LOD generation unit (30009), lifting conversion unit (30010), coefficient quantization unit (30011) and / or arithmetic encoder (30012) can be grouped and referred to as an attribute encoder.

[0092] The coordinate system transformation unit (30000), the quantization unit (30001), the octree analysis unit (30002), the surface approximation analysis unit (30003), the arithmetic encoder (30004), and the geometry reconstruction unit (30005) can perform geometry encoding. The geometry encoding according to the embodiments can include octree geometry coding, direct coding, trisoup geometry encoding, and entropy encoding. Direct coding and trisoup geometry encoding are applied selectively or in combination. In addition, the geometry encoding is not limited to the above examples.

[0093] As illustrated in the drawing, a coordinate system conversion unit (30000) according to embodiments receives positions and converts them into coordinates. For example, the positions may be converted into location information of a three-dimensional space (e.g., a three-dimensional space expressed in an XYZ coordinate system, etc.). The location information of the three-dimensional space according to embodiments may be referred to as geometry information.

[0094] A quantization unit (30001) according to embodiments quantizes geometry. For example, the quantization unit (30001) may quantize points based on the minimum position value of all points (e.g., the minimum value on each axis for the X-axis, Y-axis, and Z-axis). The quantization unit (30001) performs a quantization operation of multiplying the difference between the minimum position value and the position value of each point by a preset quantization scale value, and then rounding down or up to find the closest integer value. Accordingly, one or more points may have the same quantized position (or position value). The quantization unit (30001) according to embodiments performs voxelization based on the quantized positions to reconstruct the quantized points. The minimum unit containing two-dimensional image / video information is a pixel, and points of point cloud content (or three-dimensional point cloud video) according to embodiments may be included in one or more voxels. A voxel is a combination of a volume and a pixel, and refers to a three-dimensional cubic space generated when a three-dimensional space is divided into units (unit=1.0) based on axes representing the three-dimensional space (e.g., X-axis, Y-axis, Z-axis). The quantization unit (40001) may match groups of points in the three-dimensional space to voxels. According to embodiments, one voxel may include only one point. According to embodiments, one voxel may include one or more points. In addition, in order to express one voxel as one point, the position of the center of the voxel may be set based on the positions of one or more points included in one voxel. In this case, the attributes of all positions contained in one voxel can be combined and assigned to the voxel.

[0095] The octree analysis unit (30002) according to the embodiments performs octree geometry coding (or octree coding) to represent voxels in an octree structure. The octree structure represents points matched to voxels based on an octree structure.

[0096] The surface approximation analysis unit (30003) according to the embodiments can analyze and approximate an octree. The octree analysis and approximation according to the embodiments is a process of analyzing and voxelizing an area including a large number of points to efficiently provide an octree and voxelization.

[0097] An arithmetic encoder (30004) according to embodiments entropy encodes an octree and / or an approximated octree. For example, the encoding method includes an arithmetic encoding method. The encoding results in a geometry bitstream.

[0098] The color conversion unit (30006), the attribute conversion unit (30007), the RAHT conversion unit (30008), the LOD generation unit (30009), the lifting conversion unit (30010), the coefficient quantization unit (30011) and / or the arithmetic encoder (30012) perform attribute encoding. As described above, one point may have one or more attributes. Attribute encoding according to embodiments is applied equally to the attributes of one point. However, when one attribute (e.g., color) includes one or more elements, independent attribute encoding is applied to each element. Attribute encoding according to embodiments may include color transform coding, attribute transform coding, RAHT (Region Adaptive Hierarchial Transform) coding, Interpolarization-based hierarchical nearest-neighbor prediction-Prediction Transform) coding, and lifting transform (interpolation-based hierarchical nearest-neighbor prediction with an update / lifting step (Lifting Transform)) coding. Depending on the point cloud content, the above-described RAHT coding, prediction transform coding, and lifting transform coding may be selectively used, or a combination of one or more codings may be used. In addition, attribute encoding according to embodiments is not limited to the above-described examples.

[0099] The color conversion unit (30006) according to the embodiments performs color conversion coding to convert color values ​​(or textures) included in attributes. For example, the color conversion unit (30006) may convert the format of color information (e.g., convert from RGB to YCbCr). The operation of the color conversion unit (30006) according to the embodiments may be optionally applied depending on the color values ​​included in the attributes.

[0100] The geometry reconstruction unit (30005) according to the embodiments reconstructs (decompresses) an octree and / or an approximated octree. The geometry reconstruction unit (30005) reconstructs an octree / voxel based on the results of analyzing the distribution of points. The reconstructed octree / voxel may be referred to as a reconstructed geometry (or restored geometry).

[0101] The attribute conversion unit (30007) according to the embodiments performs attribute conversion that converts attributes based on positions for which geometry encoding has not been performed and / or reconstructed geometry. As described above, since the attributes are dependent on the geometry, the attribute conversion unit (30007) can convert the attributes based on the reconstructed geometry information. For example, the attribute conversion unit (30007) can convert the attribute of a point at a position based on the position value of the point included in the voxel. As described above, when the position of the center point of a voxel is set based on the positions of one or more points included in the voxel, the attribute conversion unit (30007) converts the attributes of one or more points. When try-soup geometry encoding is performed, the attribute conversion unit (30007) can convert attributes based on the try-soup geometry encoding.

[0102] The attribute transformation unit (30007) can perform attribute transformation by calculating the average value of the attributes or attribute values ​​(e.g., the color or reflectance of each point) of neighboring points within a specific position / radius from the position (or position value) of the center point of each voxel. The attribute transformation unit (30007) can apply a weight according to the distance from the center point to each point when calculating the average value. Accordingly, each voxel has a position and a calculated attribute (or attribute value).

[0103] The attribute transformation unit (30007) can search for neighboring points within a specific position / radius from the position of the center point of each voxel based on the KD tree or the Moulton code. The KD tree is a binary search tree that supports a data structure that can manage points based on their positions to enable fast nearest neighbor search (NNS). The Moulton code represents the coordinate values ​​(e.g. (x, y, z)) representing the 3D positions of all points as bit values ​​and is generated by mixing the bits. For example, if the coordinate values ​​representing the position of a point are (5, 9, 1), the bit values ​​of the coordinate values ​​are (0101, 1001, 0001). If the bit values ​​are mixed in the order of z, y, and x according to the bit index, it is 010001000111. If this value is expressed in decimal, it is 1095. That is, the Moulton code value of the point with coordinate values ​​(5, 9, 1) is 1095. The attribute transformation unit (30007) can sort points based on the Moulton code value and perform nearest neighbor search (NNS) through a depth-first traversal process. After the attribute transformation operation, if nearest neighbor search (NNS) is also required in other transformation processes for attribute coding, a KD tree or Moulton code is utilized.

[0104] As shown in the drawing, the converted attributes are input to the RAHT conversion unit (30008) and / or the LOD generation unit (30009).

[0105] The RAHT transform unit (30008) according to the embodiments performs RAHT coding to predict attribute information based on reconstructed geometry information. For example, the RAHT transform unit (30008) can predict attribute information of a node at an upper level of an octree based on attribute information associated with a node at a lower level of the octree.

[0106] The LOD generation unit (30009) according to the embodiments generates a LOD (Level of Detail) to perform predictive transformation coding. The LOD according to the embodiments represents the level of detail of point cloud content. A smaller LOD value indicates lower detail of point cloud content, and a larger LOD value indicates higher detail of point cloud content. Points can be classified according to LOD.

[0107] The lifting transformation unit (30010) according to the embodiments performs lifting transformation coding that transforms attributes of a point cloud based on weights. As described above, lifting transformation coding may be applied selectively.

[0108] The coefficient quantization unit (30011) according to the embodiments quantizes attribute-coded attributes based on coefficients.

[0109] An arithmetic encoder (30012) according to embodiments encodes quantized attributes based on arithmetic coding.

[0110] The elements of the point cloud encoder of FIG. 3 may be implemented by hardware, software, firmware, or a combination thereof, including one or more processors or integrated circuits configured to communicate with one or more memories included in the point cloud providing device, although not shown in the drawing. The one or more processors may perform at least one or more of the operations and / or functions of the elements of the point cloud encoder of FIG. 3 described above. Furthermore, the one or more processors may operate or execute a set of software programs and / or instructions for performing the operations and / or functions of the elements of the point cloud encoder of FIG. 3. The one or more memories according to embodiments may include high-speed random access memory, or may include non-volatile memory (e.g., one or more magnetic disk storage devices, flash memory devices, or other non-volatile solid-state memory devices).

[0111] Figure 4 illustrates examples of octree and occupancy codes according to embodiments.

[0112] As described in FIGS. 1 to 3, the point cloud content provision system (point cloud video encoder (10002)) or point cloud encoder (e.g., octree analysis unit (30002)) performs octree geometry coding (or octree coding) based on an octree structure to efficiently manage the area and / or position of a voxel.

[0113] The top of Fig. 4 shows the octree structure. The three-dimensional space of the point cloud content according to the embodiments is expressed by the axes of the coordinate system (e.g., X-axis, Y-axis, Z-axis). The octree structure has two poles (0,0,0) and (2 d , 2 d , 2 d ) is generated by recursively subdividing the cubical axis-aligned bounding box defined by . 2d can be set to a value that constitutes the smallest bounding box that encloses all points of the point cloud content (or point cloud video). d represents the depth of the octree. The value of d is determined by the following equation. In the equation below, (x int n , y int n , z int n ) represents the positions (or position values) of quantized points.

[0114]

[0115] As shown in the middle of the upper part of Fig. 4, the entire three-dimensional space can be divided into eight spaces according to the division. Each divided space is expressed as a cube with six faces. As shown in the upper right of Fig. 4, each of the eight spaces is again divided based on the axes of the coordinate system (e.g., X-axis, Y-axis, Z-axis). Therefore, each space is again divided into eight smaller spaces. The divided smaller spaces are also expressed as cubes with six faces. This division method is applied until the leaf nodes of the octree become voxels.

[0116] The bottom of Fig. 4 shows the occupancy code of the octree. The occupancy code of the octree is generated to indicate whether each of the eight partitioned spaces generated by partitioning one space contains at least one point. Therefore, one occupancy code is expressed by eight child nodes. Each child node represents the occupancy of the partitioned space, and each child node has a value of 1 bit. Therefore, the occupancy code is expressed as an 8-bit code. That is, if the space corresponding to the child node contains at least one point, the node has a value of 1. If the space corresponding to the child node does not contain a point (empty), the node has a value of 0. Since the occupancy code illustrated in Fig. 4 is 00100001, it indicates that the spaces corresponding to the third and eighth child nodes among the eight child nodes each contain at least one point. As shown in the drawing, the third child node and the eighth child node each have eight child nodes, and each child node is expressed by an 8-bit occupancy code. The drawing shows that the occupancy code of the third child node is 10000111, and the occupancy code of the eighth child node is 01001111. A point cloud encoder according to embodiments (e.g., an arithmetic encoder (30004)) can entropy encode the occupancy code. In addition, the point cloud encoder can intra / inter code the occupancy code to increase compression efficiency. A receiving device according to embodiments (e.g., a receiving device (10004) or a point cloud video decoder (10006)) reconstructs an octree based on the occupancy code.

[0117] A point cloud encoder according to embodiments (e.g., the point cloud encoder of FIG. 3, or the octree analysis unit (30002)) can perform voxelization and octree coding to store the positions of points. However, points within a 3D space are not always evenly distributed, and thus, there may be specific areas where there are not many points. Therefore, performing voxelization on the entire 3D space is inefficient. For example, if there are few points in a specific area, there is no need to perform voxelization up to that area.

[0118] Therefore, the point cloud encoder according to the embodiments can perform direct coding that directly codes the positions of points included in the specific region (or nodes excluding leaf nodes of the octree) without performing voxelization for the specific region described above. The coordinates of the direct coded points according to the embodiments are referred to as a direct coding mode (DCM). In addition, the point cloud encoder according to the embodiments can perform trisoup geometry encoding that reconstructs the positions of points within the specific region (or node) on a voxel basis based on a surface model. Trisoup geometry encoding is a geometry encoding that expresses the representation of an object as a series of triangle meshes. Therefore, the point cloud decoder can generate a point cloud from the mesh surface. Direct coding and trisoup geometry encoding according to the embodiments can be selectively performed. Additionally, direct coding and tri-subtractive geometry encoding according to embodiments may be performed in combination with octree geometry coding (or octree coding).

[0119] In order to perform direct coding, the option to use direct mode for applying direct coding must be activated, the node to which direct coding is to be applied must not be a leaf node, and there must be points below a threshold within a specific node. In addition, the total number of points subject to direct coding must not exceed a preset threshold. If the above conditions are satisfied, the point cloud encoder (or arithmetic encoder (30004)) according to the embodiments can entropy code the positions (or position values) of the points.

[0120] A point cloud encoder according to embodiments (e.g., surface approximation analysis unit (30003)) can determine a specific level of an octree (when the level is smaller than the depth d of the octree) and, starting from that level, perform tri-subject geometry encoding to reconstruct the positions of points within a node region on a voxel basis using a surface model (tri-subject mode). A point cloud encoder according to embodiments can specify a level to which tri-subject geometry encoding is to be applied. For example, when the specified level is equal to the depth of the octree, the point cloud encoder does not operate in tri-subject mode. That is, a point cloud encoder according to embodiments can operate in tri-subject mode only when the specified level is smaller than the depth value of the octree. A three-dimensional cubic area of ​​nodes at a specified level according to embodiments is called a block. One block may include one or more voxels. A block or a voxel may correspond to a brick. Within each block, geometry is represented by a surface. According to embodiments, a surface may intersect each edge of the block at most once.

[0121] Since one block has 12 edges, there are at least 12 intersections within one block. Each intersection is called a vertex. A vertex existing along an edge is detected if there is at least one occupied voxel adjacent to the edge among all blocks sharing the edge. An occupied voxel according to embodiments means a voxel containing a point. The position of a vertex detected along an edge is the average position along the edge of all voxels adjacent to the edge among all blocks sharing the edge.

[0122] When a vertex is detected, the point cloud encoder according to the embodiments calculates the starting point of the edge (x, y, z), the direction vector of the edge ( x, y, z), vertex position values ​​(relative position values ​​within an edge) can be entropy-coded. When tri-subspace geometry encoding is applied, the point cloud encoder according to the embodiments (e.g., geometry reconstruction unit (30005)) can perform triangle reconstruction, up-sampling, and voxelization processes to generate restored geometry (reconstructed geometry).

[0123] The vertices located at the edge of a block determine the surface passing through the block. According to the embodiments, the surface is a non-planar polygon. The triangle reconstruction process reconstructs the surface represented by a triangle based on the starting point of the edge, the direction vector of the edge, and the position value of the vertex. The triangle reconstruction process is as follows. ① Calculate the centroid value of each vertex, ② Subtract the centroid value from each vertex value, and ③ Square the values, and then add up all the values ​​to obtain the value.

[0124]

[0125] Then, the minimum of the added values ​​is found, and the projection process is performed according to the axis with the minimum value. For example, if the x element is minimum, each vertex is projected to the x-axis based on the center of the block, and projected onto the (y, z) plane. If the value produced when projected onto the (y, z) plane is (ai, bi), the θ value is found through atan2(bi, ai), and the vertices are sorted based on the θ value. The table below shows the combination of vertices to create a triangle depending on the number of vertices. The vertices are sorted in order from 1 to n. Table 1 below shows that two triangles can be formed depending on the combination of vertices for four vertices. The first triangle may be composed of the 1st, 2nd, and 3rd vertices among the sorted vertices, and the second triangle may be composed of the 3rd, 4th, and 1st vertices among the sorted vertices.

[0126] [Table 1] Triangles formed from vertices ordered 1,… , nnTriangles3(1,2,3)4(1,2,3), (3,4,1)5(1,2,3), (3,4,5), (5,1,3)6(1,2,3), (3,4,5), (5,6,1), (1,3,5)7(1,2,3), (3,4,5), (5,6,7), (7,1,3), (3,5,7)8(1,2,3), (3,4,5), (5,6,7), (7,8,1), (1,3,5), (5,7,1)9(1,2,3), (3,4,5), (5,6,7), (7,8,9), (9,1,3), (3,5,7), (7,9,3)10(1,2,3), (3,4,5), (5,6,7), (7,8,9), (9,10,1), (1,3,5), (5,7,9), (9,1,5)11(1,2,3), (3,4,5), (5,6,7), (7,8,9), (9,10,11), (11,1,3), (3,5,7), (7,9,11), (11,3,7)12(1,2,3), (3,4,5), (5,6,7), (7,8,9), (9,10,11), (11,12,1), (1,3,5), (5,7,9), (9,11,1), (1,5,9)

[0127] The upsampling process is performed to voxelize the triangle by adding points in the middle along the edges. Additional points are generated based on the upsampling factor and the width of the block. The additional points are called refined vertices. A point cloud encoder according to embodiments can voxelize the refined vertices. The point cloud encoder can also perform attribute encoding based on the voxelized positions (or position values).

[0128] Figure 5 shows an example of a point configuration by LOD according to embodiments.

[0129] As described in FIGS. 1 to 4, the encoded geometry is reconstructed (decompressed) before attribute encoding is performed. When direct coding is applied, the geometry reconstruction operation may include changing the arrangement of direct-coded points (e.g., placing the direct-coded points at the front of the point cloud data). When trysoup geometry encoding is applied, the geometry reconstruction process includes triangle reconstruction, upsampling, and voxelization. Since attributes depend on the geometry, attribute encoding is performed based on the reconstructed geometry.

[0130] A point cloud encoder (e.g., LOD generation unit (30009)) can reorganize points by LOD. The drawing shows point cloud content corresponding to LOD. The left side of the drawing shows the original point cloud content. The second figure from the left in the drawing shows the distribution of points of the lowest LOD, and the rightmost figure in the drawing shows the distribution of points of the highest LOD. That is, points of the lowest LOD are sparsely distributed, and points of the highest LOD are densely distributed. That is, as LOD increases in the direction of the arrow indicated at the bottom of the drawing, the interval (or distance) between points becomes shorter.

[0131] Figure 6 shows an example of a point configuration by LOD according to embodiments.

[0132] As described in FIGS. 1 to 5, a point cloud content providing system, or a point cloud encoder (e.g., a point cloud video encoder (10002), the point cloud encoder of FIG. 3, or a LOD generation unit (30009)) can generate a LOD. The LOD is generated by reorganizing points into a set of refinement levels according to a set LOD distance value (or a set of Euclidean distances). The LOD generation process is performed not only in the point cloud encoder but also in the point cloud decoder.

[0133] The upper part of Fig. 6 shows examples of points (P0 to P9) of point cloud content distributed in 3D space. The original order in Fig. 6 represents the order of points P0 to P9 before LOD generation. The LOD-based order in Fig. 6 represents the order of points according to LOD generation. The points are rearranged by LOD. Additionally, a higher LOD includes points belonging to a lower LOD. As shown in Fig. 6, LOD0 includes P0, P5, P4, and P2. LOD1 includes points of LOD0 and P1, P6, and P3. LOD2 includes points of LOD0, points of LOD1, and P9, P8, and P7.

[0134] As described in FIG. 3, the point cloud encoder according to the embodiments can selectively or in combination perform predictive transform coding, lifting transform coding, and RAHT transform coding.

[0135] A point cloud encoder according to embodiments can perform predictive transformation coding to generate a predictor for points and set a predicted attribute (or predicted attribute value) for each point. That is, N predictors can be generated for N points. The predictor according to embodiments can calculate a weight (= 1 / distance) value based on the LOD value of each point, indexing information for neighboring points existing within a distance set for each LOD, and distance values ​​to the neighboring points.

[0136] According to the embodiments, the predicted attribute (or attribute value) is set as the average value of the product of the attributes (or attribute values, for example, color, reflectance, etc.) of neighboring points set in the predictor of each point and the weight (or weight value) calculated based on the distance to each neighboring point. The point cloud encoder according to the embodiments (for example, the coefficient quantization unit (30011)) can quantize and inverse quantize the residual values ​​(which may be referred to as residual attribute, residual attribute value, attribute prediction residual value, etc.) obtained by subtracting the predicted attribute (attribute value) from the attribute (attribute value) of each point. The quantization process is as shown in Tables 2 and 3 below.

[0137] int PCCQuantization(int value, int quantStep) {if( value >=0) {return floor(value / quantStep + 1.0 / 3.0);} else {return -floor(-value / quantStep + 1.0 / 3.0);}}

[0138] int PCCInverseQuantization(int value, int quantStep) {if( quantStep ==0) {return value;} else {return value * quantStep;}}

[0139] A point cloud encoder according to embodiments (e.g., an arithmetic encoder (30012)) can entropy code the quantized and dequantized residuals as described above when there are neighboring points to the predictor of each point. A point cloud encoder according to embodiments (e.g., an arithmetic encoder (30012)) can entropy code the attributes of the point without performing the above-described process when there are no neighboring points to the predictor of each point.

[0140] A point cloud encoder according to embodiments (e.g., lifting transformation unit (30010)) can perform lifting transformation coding by generating a predictor for each point, setting the LOD calculated in the predictor, registering neighboring points, and setting weights according to the distance to the neighboring points. Lifting transformation coding according to embodiments is similar to the above-described predictive transformation coding, but differs in that weights are cumulatively applied to attribute values. The process of cumulatively applying weights to attribute values ​​according to embodiments is as follows.

[0141] 1) Create an array QW (QuantizationWieght) that stores the weight values ​​of each point. The initial value of all elements in QW is 1.0. Add the value obtained by multiplying the weight of the current point's predictor by the QW value of the predictor index of the neighboring node registered in the predictor.

[0142] 2) Lift prediction process: To calculate the predicted attribute value, the weighted value of the point's attribute value is multiplied and subtracted from the existing attribute value.

[0143] 3) Create temporary arrays called updateweight and update and initialize them to 0.

[0144] 4) For each predictor, the calculated weights are multiplied by the weights stored in the QW corresponding to the predictor index, and the resulting weights are cumulatively added to the update weight array as the index of the neighboring node. The update array accumulates the values ​​obtained by multiplying the calculated weights by the attribute values ​​of the indexes of the neighboring nodes.

[0145] 5) Lift update process: For each predictor, the attribute values ​​in the update array are divided by the weight values ​​in the update weight array of the predictor index, and the existing attribute values ​​are added to the divided value.

[0146] 6) For all predictors, the predicted attribute values ​​are calculated by additionally multiplying the updated attribute values ​​through the lift update process by the weights (stored in QW) updated through the lift prediction process. The point cloud encoder according to the embodiments (e.g., coefficient quantization unit (30011)) quantizes the predicted attribute values. In addition, the point cloud encoder (e.g., arithmetic encoder (30012)) entropy-codes the quantized attribute values.

[0147] A point cloud encoder according to embodiments (e.g., RAHT transform unit (30008)) can perform RAHT transform coding that predicts attributes of upper-level nodes using attributes associated with nodes at lower levels of an octree. RAHT transform coding is an example of attribute intra coding through octree backward scan. A point cloud encoder according to embodiments scans from a voxel to the entire area, and repeats the merging process up to a root node while merging voxels into larger blocks at each step. The merging process according to embodiments is performed only for occupied nodes. The merging process is not performed for empty nodes, and the merging process is performed for the node immediately above the empty node.

[0148] The following equation represents the RAHT transformation matrix. g lx,y,z represents the average attribute value of voxels at level l. g lx,y,z can be calculated from gl+1 2x,y,z and gl+1 2x+1,y,z. g l 2x,y,z and the weights of gl 2x+1,y,z are w1=w l 2x,y,z And w2=wl 2x+1,y,z.

[0149]

[0150] g l-1 x,y,z is a low-pass value, used in the merging process at the next higher level. h l-1 x,y,z are high-pass coefficients, and the high-pass coefficients at each step are quantized and entropy coded (e.g., encoding of an arithmetic encoder (30012)). The weights are w l-1 x,y,z = w l 2x,y,z + wl is calculated as 2x+1,y,z. The root node is the last g 1 0,0,0 and g 1 0,0,1 It is generated through:

[0151]

[0152] The gDC values ​​are also quantized and entropy coded, like the high-pass coefficients.

[0153] Fig. 7 illustrates an example of a point cloud decoder according to embodiments.

[0154] The point cloud decoder illustrated in FIG. 7 is an example of a point cloud decoder and can perform a decoding operation, which is the reverse process of the encoding operation of the point cloud encoder described in FIGS. 1 to 6.

[0155] As described in Figure 1, the point cloud decoder can perform geometry decoding and attribute decoding. Geometry decoding is performed before attribute decoding.

[0156] A point cloud decoder according to embodiments includes an arithmetic decoder (7000), an octree synthesizer (7001), a surface approximation synthesizer (7002), a geometry reconstructor (7003), an inverse transform coordinates (7004), an arithmetic decoder (7005), an inverse quantize (7006), a RAHT transform (7007), a LOD generator (7008), an inverse lifting (7009), and / or an inverse transform colors (7010).

[0157] The arithmetic decoder (7000), the octree synthesis unit (7001), the surface oproximation synthesis unit (7002), the geometry reconstruction unit (7003), and the coordinate system inversion unit (7004) can perform geometry decoding. Geometry decoding according to embodiments can include direct coding and trisoup geometry decoding. Direct coding and trisoup geometry decoding are applied selectively. In addition, geometry decoding is not limited to the above examples, and is performed by the reverse process of the geometry encoding described in FIGS. 1 to 6.

[0158] An arithmetic decoder (7000) according to embodiments decodes a received geometry bitstream based on arithmetic coding. The operation of the arithmetic decoder (7000) corresponds to the reverse process of the arithmetic encoder (30004).

[0159] The octree synthesis unit (7001) according to the embodiments can generate an octree by obtaining an occupancy code from a decoded geometry bitstream (or information about the geometry obtained as a result of decoding). A specific description of the occupancy code is as described in FIGS. 1 to 6.

[0160] The surface off-axis synthesis unit (7002) according to the embodiments can synthesize a surface based on the decoded geometry and / or the generated octree when the tri-sub geometry encoding is applied.

[0161] The geometry reconstruction unit (7003) according to the embodiments can regenerate geometry based on the surface and / or decoded geometry. As described in FIGS. 1 to 6, direct coding and try-soup geometry encoding are selectively applied. Therefore, the geometry reconstruction unit (7003) directly retrieves and adds position information of points to which direct coding is applied. In addition, when try-soup geometry encoding is applied, the geometry reconstruction unit (7003) can restore geometry by performing a reconstruction operation of the geometry reconstruction unit (30005), such as triangle reconstruction, up-sampling, and voxelization operations. The specific details are the same as described in FIG. 4 and are therefore omitted. The restored geometry may include a point cloud picture or frame that does not include attributes.

[0162] The coordinate system inverse transformation unit (7004) according to the embodiments can obtain the positions of points by transforming the coordinate system based on the restored geometry.

[0163] The arithmetic decoder (7005), the inverse quantization unit (7006), the RAHT transform unit (7007), the LOD generation unit (7008), the inverse lifting unit (7009), and / or the color inverse transform unit (7010) can perform attribute decoding. The attribute decoding according to the embodiments can include RAHT (Region Adaptive Hierarchical Transform) decoding, prediction transform (Interpolaration-based hierarchical nearest-neighbor prediction-Prediction Transform) decoding, and lifting transform (interpolation-based hierarchical nearest-neighbor prediction with an update / lifting step (Lifting Transform)) decoding. The three decodings described above can be used selectively, or a combination of one or more decodings can be used. In addition, the attribute decoding according to the embodiments is not limited to the above-described examples.

[0164] An arithmetic decoder (7005) according to embodiments decodes an attribute bitstream using arithmetic coding.

[0165] The inverse quantization unit (7006) according to the embodiments inverse quantizes information about the decoded attribute bitstream or the attributes obtained as a result of the decoding and outputs the inverse quantized attributes (or attribute values). The inverse quantization may be selectively applied based on the attribute encoding of the point cloud encoder.

[0166] According to embodiments, the RAHT transform unit (7007), the LOD generator (7008), and / or the inverse lifting unit (7009) may process the reconstructed geometry and the inverse quantized attributes. As described above, the RAHT transform unit (7007), the LOD generator (7008), and / or the inverse lifting unit (7009) may selectively perform a corresponding decoding operation according to the encoding of the point cloud encoder.

[0167] The color inverse transform unit (7010) according to the embodiments performs inverse transform coding to inversely transform the color values ​​(or textures) included in the decoded attributes. The operation of the color inverse transform unit (7010) may be selectively performed based on the operation of the color transform unit (30006) of the point cloud encoder.

[0168] The elements of the point cloud decoder of FIG. 7 may be implemented by hardware, software, firmware, or a combination thereof, including one or more processors or integrated circuits configured to communicate with one or more memories included in a point cloud providing device, although not shown in the drawing. The one or more processors may perform at least one or more of the operations and / or functions of the elements of the point cloud decoder of FIG. 7 described above. Furthermore, the one or more processors may operate or execute a set of software programs and / or instructions for performing the operations and / or functions of the elements of the point cloud decoder of FIG. 7.

[0169] Figure 8 is an example of a transmission device according to embodiments.

[0170] The transmission device illustrated in FIG. 8 is an example of the transmission device (10000) of FIG. 1 (or the point cloud encoder of FIG. 3). The transmission device illustrated in FIG. 8 can perform at least one or more of the same or similar operations and encoding methods as the operations and encoding methods of the point cloud encoder described in FIGS. 1 to 6. A transmission device according to embodiments may include a data input unit (8000), a quantization processing unit (8001), a voxelization processing unit (8002), an octree occupancy code generation unit (8003), a surface model processing unit (8004), an intra / inter coding processing unit (8005), an arithmetic coder (8006), a metadata processing unit (8007), a color conversion processing unit (8008), an attribute conversion processing unit (or a property conversion processing unit) (8009), a prediction / lifting / RAHT conversion processing unit (8010), an arithmetic coder (8011), and / or a transmission processing unit (8012).

[0171] The data input unit (8000) according to the embodiments receives or acquires point cloud data. The data input unit (8000) may perform operations and / or acquisition methods identical or similar to those of the point cloud video acquisition unit (10001) (or the acquisition process (20000) described in FIG. 2).

[0172] The data input unit (8000), quantization processing unit (8001), voxelization processing unit (8002), octree occupancy code generation unit (8003), surface model processing unit (8004), intra / inter coding processing unit (8005), and arithmetic coder (8006) perform geometry encoding. Since the geometry encoding according to the embodiments is the same or similar to the geometry encoding described in FIGS. 1 to 6, a detailed description thereof will be omitted.

[0173] The quantization processing unit (8001) according to the embodiments quantizes geometry (e.g., position values ​​of points or position values). The operation and / or quantization of the quantization processing unit (8001) is identical to or similar to the operation and / or quantization of the quantization unit (30001) described in FIG. 3. The specific description is the same as that described in FIGS. 1 to 6.

[0174] The voxelization processing unit (8002) according to the embodiments voxels the position values ​​of quantized points. The voxelization processing unit (80002) may perform operations and / or processes identical or similar to the operations and / or voxelization processes of the quantization unit (30001) described in FIG. 3. Specific descriptions are identical to those described in FIGS. 1 to 6.

[0175] The octree occupancy code generation unit (8003) according to the embodiments performs octree coding on the positions of voxelized points based on the octree structure. The octree occupancy code generation unit (8003) can generate an occupancy code. The octree occupancy code generation unit (8003) can perform operations and / or methods identical or similar to those of the point cloud encoder (or octree analysis unit (30002)) described in FIGS. 3 and 4. The specific description is the same as that described in FIGS. 1 to 6.

[0176] The surface model processing unit (8004) according to the embodiments can perform tri-subject geometry encoding to reconstruct the positions of points within a specific area (or node) on a voxel basis based on the surface model. The surface model processing unit (8004) can perform operations and / or methods identical or similar to those of the point cloud encoder (e.g., surface approximation analysis unit (30003)) described in FIG. 3. The specific description is the same as that described with reference to FIGS. 1 to 6.

[0177] The intra / inter coding processing unit (8005) according to embodiments may perform intra / inter coding on point cloud data. The intra / inter coding processing unit (8005) may perform coding identical to or similar to intra / inter coding. According to embodiments, the intra / inter coding processing unit (8005) may be included in an arithmetic coder (8006).

[0178] An arithmetic coder (8006) according to embodiments entropy encodes an octree and / or an approximated octree of point cloud data. For example, the encoding method includes an arithmetic encoding method. The arithmetic coder (8006) performs operations and / or methods identical or similar to those of the arithmetic encoder (30004).

[0179] The metadata processing unit (8007) according to the embodiments processes metadata regarding point cloud data, such as setting values, and provides the metadata to a necessary processing step, such as geometry encoding and / or attribute encoding. In addition, the metadata processing unit (8007) according to the embodiments may generate and / or process signaling information related to geometry encoding and / or attribute encoding. The signaling information according to the embodiments may be encoded and processed separately from geometry encoding and / or attribute encoding. In addition, the signaling information according to the embodiments may be interleaved.

[0180] The color conversion processing unit (8008), the attribute conversion processing unit (8009), the prediction / lifting / RAHT conversion processing unit (8010), and the arithmetic coder (8011) perform attribute encoding. Since the attribute encoding according to the embodiments is the same as or similar to the attribute encoding described in FIGS. 1 to 6, a detailed description thereof will be omitted.

[0181] The color conversion processing unit (8008) according to the embodiments performs color conversion coding to convert the color values ​​included in the attributes. The color conversion processing unit (8008) can perform color conversion coding based on the reconstructed geometry. The description of the reconstructed geometry is the same as that described with reference to FIGS. 1 to 6. In addition, the color conversion processing unit (8008) performs the same or similar operation and / or method as that of the color conversion unit (30006) described with reference to FIG. 3. A detailed description thereof will be omitted.

[0182] The attribute transformation processing unit (8009) according to embodiments performs attribute transformation to transform attributes based on positions for which geometry encoding has not been performed and / or reconstructed geometry. The attribute transformation processing unit (8009) performs operations and / or methods that are the same as or similar to those of the attribute transformation unit (30007) described in FIG. 3. A detailed description thereof will be omitted. The prediction / lifting / RAHT transformation processing unit (8010) according to embodiments can code transformed attributes by using any one or a combination of RAHT coding, prediction transformation coding, and lifting transformation coding. The prediction / lifting / RAHT transformation processing unit (8010) performs at least one or more of operations that are the same as or similar to those of the RAHT transformation unit (30008), LOD generation unit (30009), and lifting transformation unit (30010) described in FIG. 3. In addition, the description of the prediction transformation coding, lifting transformation coding, and RAHT transformation coding is the same as that described in FIGS. 1 to 6, so a detailed description is omitted.

[0183] An arithmetic coder (8011) according to embodiments can encode coded attributes based on arithmetic coding. The arithmetic coder (8011) performs operations and / or methods identical or similar to those of the arithmetic encoder (300012).

[0184] The transmission processing unit (8012) according to embodiments may transmit each bitstream including encoded geometry and / or encoded attribute, metadata information, or may transmit the encoded geometry and / or encoded attribute, and metadata information as one bitstream. When the encoded geometry and / or encoded attribute, and metadata information according to embodiments are configured as one bitstream, the bitstream may include one or more sub-bitstreams. The bitstream according to embodiments may include signaling information including a Sequence Parameter Set (SPS) for sequence-level signaling, a Geometry Parameter Set (GPS) for signaling geometry information coding, an Attribute Parameter Set (APS) for signaling attribute information coding, and a Tile Parameter Set (TPS) for tile-level signaling, and slice data. The slice data may include information about one or more slices. One slice according to embodiments may include one geometry bitstream (Geom0). 0 ) and one or more attribute bitstreams (Attr0 0 , Attr1 0 ) may be included.

[0185] A slice is a series of syntax elements that represent all or part of a coded point cloud frame.

[0186] A TPS according to embodiments may include information about each tile (e.g., coordinate value information of a bounding box and height / size information, etc.) for one or more tiles. A geometry bitstream may include a header and a payload. The header of a geometry bitstream according to embodiments may include identification information of a parameter set included in GPS (geom_ parameter_set_id), a tile identifier (geom_tile_id), a slice identifier (geom_slice_id), and information about data included in the payload. As described above, a metadata processing unit (8007) according to embodiments may generate and / or process signaling information and transmit it to a transmission processing unit (8012). According to embodiments, elements that perform geometry encoding and elements that perform attribute encoding may share data / information with each other as indicated by a dotted line. The transmission processing unit (8012) according to the embodiments may perform operations and / or transmission methods identical or similar to those of the transmitter (10003). A detailed description thereof is omitted as it is the same as that described in FIGS. 1 and 2.

[0187] Fig. 9 is an example of a receiving device according to embodiments.

[0188] The receiving device illustrated in FIG. 9 is an example of the receiving device (10004) of FIG. 1. The receiving device illustrated in FIG. 9 can perform at least one of the same or similar operations and methods as the operations and decoding methods of the point cloud decoder described in FIGS. 1 to 8.

[0189] A receiving device according to embodiments may include a receiving unit (9000), a receiving processing unit (9001), an arithmetic decoder (9002), an occupancy code-based octree reconstruction processing unit (9003), a surface model processing unit (triangle reconstruction, up-sampling, voxelization) (9004), an inverse quantization processing unit (9005), a metadata parser (9006), an arithmetic decoder (9007), an inverse quantization processing unit (9008), a prediction / lifting / RAHT inverse transform processing unit (9009), a color inverse transform processing unit (9010), and / or a renderer (9011). Each component of the decoding according to embodiments may perform the reverse process of the component of the encoding according to embodiments.

[0190] The receiving unit (9000) according to the embodiments receives point cloud data. The receiving unit (9000) may perform operations and / or receiving methods identical or similar to those of the receiver (10005) of FIG. 1. A detailed description thereof will be omitted.

[0191] The receiving processing unit (9001) according to the embodiments can obtain a geometry bitstream and / or an attribute bitstream from the received data. The receiving processing unit (9001) can be included in the receiving unit (9000).

[0192] The arithmetic decoder (9002), the occupancy code-based octree reconstruction processing unit (9003), the surface model processing unit (9004), and the inverse quantization processing unit (9005) can perform geometry decoding. Since the geometry decoding according to the embodiments is identical or similar to the geometry decoding described in at least one of FIGS. 1 to 8, a detailed description thereof will be omitted.

[0193] An arithmetic decoder (9002) according to embodiments can decode a geometry bitstream based on arithmetic coding. The arithmetic decoder (9002) performs operations and / or coding identical to or similar to those of the arithmetic decoder (7000).

[0194] The occupancy code-based octree reconstruction processing unit (9003) according to embodiments can reconstruct an octree by obtaining an occupancy code from a decoded geometry bitstream (or information about the geometry obtained as a result of decoding). The occupancy code-based octree reconstruction processing unit (9003) performs the same or similar operations and / or methods as those of the octree synthesis unit (7001) and / or the octree generation method. The surface model processing unit (9004) according to embodiments can perform tri-sub geometry decoding and related geometry reconstructing (e.g., triangle reconstruction, up-sampling, voxelization) based on the surface model method when tri-sub geometry encoding is applied. The surface model processing unit (9004) performs the same or similar operations as those of the surface off-ratio synthesis unit (7002) and / or the geometry reconstructing unit (7003).

[0195] The inverse quantization processing unit (9005) according to the embodiments can inverse quantize the decoded geometry.

[0196] The metadata parser (9006) according to the embodiments can parse metadata, such as setting values, contained in the received point cloud data. The metadata parser (9006) can pass the metadata to geometry decoding and / or attribute decoding. A detailed description of the metadata is omitted as it is the same as described in FIG. 8.

[0197] The arithmetic decoder (9007), the inverse quantization processing unit (9008), the prediction / lifting / RAHT inverse transform processing unit (9009), and the color inverse transform processing unit (9010) perform attribute decoding. Since attribute decoding is identical or similar to the attribute decoding described in at least one of FIGS. 1 to 8, a detailed description thereof will be omitted.

[0198] An arithmetic decoder (9007) according to embodiments can decode an attribute bitstream using arithmetic coding. The arithmetic decoder (9007) can decode the attribute bitstream based on the reconstructed geometry. The arithmetic decoder (9007) performs operations and / or coding identical or similar to those of the arithmetic decoder (7005).

[0199] The inverse quantization processing unit (9008) according to the embodiments can inverse quantize the decoded attribute bitstream. The inverse quantization processing unit (9008) performs operations and / or methods identical or similar to the operations and / or inverse quantization methods of the inverse quantization unit (7006).

[0200] The prediction / lifting / RAHT inverse transform processing unit (9009) according to embodiments can process reconstructed geometry and inverse quantized attributes. The prediction / lifting / RAHT inverse transform processing unit (9009) performs at least one or more of the same or similar operations and / or decodings as the operations and / or decodings of the RAHT transformation unit (7007), the LOD generation unit (7008), and / or the inverse lifting unit (7009) of FIG. 7. The color inverse transform processing unit (9010) according to embodiments performs inverse transform coding for inverse transforming the color value (or texture) included in the decoded attributes. The color inverse transform processing unit (9010) performs the same or similar operations and / or inverse transform coding as the operations and / or inverse transform coding of the color inverse transform unit (7010) of FIG. 7. A renderer (9011) according to embodiments can render point cloud data.

[0201] Fig. 10 shows an example of a structure that can be linked with a point cloud data transmission / reception method / device according to embodiments.

[0202] The structure of FIG. 10 represents a configuration in which at least one of a server (1060), a robot (1010), an autonomous vehicle (1020), an XR device (1030), a smartphone (1040), a home appliance (1050), and / or an HMD (1070) is connected to a cloud network (1010). The robot (1010), the autonomous vehicle (1020), the XR device (1030), the smartphone (1040), or the home appliance (1050) are referred to as devices. In addition, the XR device (1030) may correspond to or be linked with a point cloud data (PCC) device according to embodiments.

[0203] A cloud network (1000) may refer to a network that constitutes part of a cloud computing infrastructure or exists within the cloud computing infrastructure. Here, the cloud network (1000) may be configured using a 3G network, a 4G or LTE (Long Term Evolution) network, or a 5G network.

[0204] The server (1060) is connected to at least one of a robot (1010), an autonomous vehicle (1020), an XR device (1030), a smartphone (1040), a home appliance (1050), and / or an HMD (1070) through a cloud network (1000), and can assist in at least part of the processing of the connected devices (1010 to 1070).

[0205] The HMD (Head-Mount Display) (1070) represents one of the types in which the XR device and / or the PCC device according to the embodiments can be implemented. The HMD type device according to the embodiments includes a communication unit, a control unit, a memory unit, an I / O unit, a sensor unit, and a power supply unit.

[0206] Below, various embodiments of devices (1010 to 1050) to which the above-described technology is applied are described. Here, the devices (1010 to 1050) illustrated in FIG. 10 can be linked / combined with the point cloud data transmission / reception devices according to the above-described embodiments.

[0207] <PCC+XR>

[0208] The XR / PCC device (1030) may be implemented as a HMD (Head-Mount Display), a HUD (Head-Up Display) equipped in a vehicle, a television, a mobile phone, a smart phone, a computer, a wearable device, a home appliance, digital signage, a vehicle, a fixed robot, a mobile robot, etc., by applying PCC and / or XR (AR+VR) technology.

[0209] The XR / PCC device (1030) can obtain information about surrounding space or real objects by analyzing 3D point cloud data or image data acquired through various sensors or from external devices to generate location data and attribute data for 3D points, and can render and output an XR object to be output. For example, the XR / PCC device (1030) can output an XR object including additional information about a recognized object in correspondence with the recognized object.

[0210] <PCC+XR+모바일폰>

[0211] The XR / PCC device (1030) can be implemented as a mobile phone (1040) or the like by applying PCC technology.

[0212] The mobile phone (1040) can decode and display point cloud content based on PCC technology.

[0213] <PCC+자율주행+XR>

[0214] Autonomous vehicles (1020) can be implemented as mobile robots, vehicles, unmanned aerial vehicles, etc. by applying PCC technology and XR technology.

[0215] An autonomous vehicle (1020) to which XR / PCC technology is applied may refer to an autonomous vehicle equipped with a means for providing XR images, or an autonomous vehicle that is the subject of control / interaction within an XR image. In particular, an autonomous vehicle (1020) that is the subject of control / interaction within an XR image is distinct from an XR device (1030) and can be linked with each other.

[0216] An autonomous vehicle (1020) equipped with a means for providing XR / PCC images can obtain sensor information from sensors including cameras and output XR / PCC images generated based on the obtained sensor information. For example, the autonomous vehicle (1020) can be equipped with a HUD to output XR / PCC images, thereby providing passengers with XR / PCC objects corresponding to real objects or objects on a screen.

[0217] At this time, when the XR / PCC object is output to the HUD, at least a part of the XR / PCC object may be output so as to overlap with an actual object toward which the passenger's gaze is directed. On the other hand, when the XR / PCC object is output to a display provided inside the autonomous vehicle, at least a part of the XR / PCC object may be output so as to overlap with an object on the screen. For example, the autonomous vehicle (1220) may output XR / PCC objects corresponding to objects such as a lane, another vehicle, a traffic light, a traffic sign, a two-wheeled vehicle, a pedestrian, a building, etc.

[0218] VR (Virtual Reality) technology, AR (Augmented Reality) technology, MR (Mixed Reality) technology and / or PCC (Point Cloud Compression) technology according to the embodiments can be applied to various devices.

[0219] In other words, VR technology is a display technology that provides only CG images of real-world objects or backgrounds. On the other hand, AR technology refers to a technology that shows a virtually created CG image on top of an image of an actual object. Furthermore, MR technology is similar to the aforementioned AR technology in that it mixes and combines virtual objects in the real world. However, in AR technology, the distinction between real objects and virtual objects created with CG images is clear, and virtual objects are used in a form that complements real objects, whereas in MR technology, virtual objects are considered to have the same characteristics as real objects. A more specific example is the hologram service, which is an application of the aforementioned MR technology.

[0220] However, recently, rather than clearly distinguishing between VR, AR, and MR technologies, they are often referred to as XR (extended reality) technologies. Therefore, the embodiments of the present disclosure are applicable to all VR, AR, MR, and XR technologies. These technologies can be applied to encoding / decoding based on PCC, V-PCC, and G-PCC technologies.

[0221] The PCC method / device according to the embodiments can be applied to a vehicle providing an autonomous driving service.

[0222] Vehicles providing autonomous driving services are connected to PCC devices to enable wired / wireless communication.

[0223] A point cloud data (PCC) transmission / reception device according to embodiments, when connected to a vehicle to enable wired / wireless communication, can receive / process content data related to AR / VR / PCC services that can be provided together with autonomous driving services and transmit the same to the vehicle. In addition, when the point cloud data transmission / reception device is mounted on a vehicle, the point cloud transmission / reception device can receive / process content data related to AR / VR / PCC services and provide the same to a user according to a user input signal input through a user interface device. A vehicle or a user interface device according to embodiments can receive a user input signal. The user input signal according to embodiments can include a signal instructing an autonomous driving service.

[0224] Meanwhile, as described above, a point cloud (or point cloud data) is composed of a set of points, and each point can have geometry information and attribute information. Geometry information is 3D position (XYZ) information, and attribute information is color (RGB, YUV, etc.), reflectance (or reflectance), etc. That is, the attribute information of each point can include at least one of color, reflectance, opacity, frame index, frame number, material identifier, and normal vector. For example, one point can have one attribute of color, or two attributes of color and reflectance.

[0225] In this document, "geometry information" is used interchangeably with "geometry data" and "geometry." Furthermore, "attribute information" is used interchangeably with "attribute data" and "attribute."

[0226] In the present disclosure, the point cloud content providing system may use one or more cameras (e.g., an infrared camera capable of obtaining depth information, an RGB camera capable of extracting color information corresponding to depth information, etc.), a projector (e.g., an infrared pattern projector for obtaining depth information, etc.), LiDAR (Light Detection and Ranging), etc., to generate point cloud content (or point cloud data).

[0227] Among these, LiDAR is a device that measures distance by measuring the time it takes for the irradiated light to reflect off a subject and return, and provides precise 3D information of the real world in the form of point cloud data over a wide area and long distance. This large-capacity point cloud data can be widely utilized in various fields that utilize computer vision technology, such as autonomous vehicles, robots, and 3D map creation. That is, LiDAR equipment uses a radar system that emits a laser pulse and measures the time it takes for the pulse to reflect off a subject (i.e., a reflector) and return, thereby measuring the position coordinates of the reflector, to generate point cloud content. According to embodiments, depth information can be extracted through LiDAR equipment. In addition, point cloud content generated through LiDAR equipment may be composed of multiple frames, and multiple frames may be integrated into a single content.

[0228] Furthermore, scenes captured as point clouds using equipment used for autonomous driving, such as lidar, can contain various street features, such as roads, buildings, utility poles, cars, bicycles, people, and trash cans. For example, a scene can also contain various states, such as moving people, moving cars, stationary trash cans, buildings, and utility poles. In other words, a scene represents a state where various objects and elements are gathered in a specific environment. These objects can be moving, such as cars, or stationary, such as roads or buildings.

[0229] In the graphics field, which provides 3D rendering such as games, a scene graph or scene description is created to describe these various scenes.

[0230] Fig. 11 is a diagram showing an example of a scene graph according to embodiments. As shown in Fig. 11, the scene graph has a hierarchical structure (i.e., a tree structure). For example, the scene graph of Fig. 11 can create (or can create) a road child node (or road node), a building child node (or building node), and a car child node (or car node) from a root node. In other words, the scene graph can create one or more child nodes from the root node. At this time, each child node can be set as a static node that does not move or a dynamic node that moves. For example, the road node and the building node can be set as static nodes, and the car node can be set as a moving node, i.e., a dynamic node. In addition, for detailed animation of the car, other child nodes (e.g., a stationary person riding in the car, a moving person riding in the car, a car model, a body, wheels, etc.) can be created with the car node as a parent node. In one embodiment, by placing the body and wheels as child nodes of the car node and setting the wheel node as a node that can specify movement on its own, an animation can be configured in which the wheels rotate around a specific axis with the center of the wheel as the origin when the car moves.

[0231] At this time, there are standards that can represent scene descriptions or scene graphs. For example, there is gltf created by Khronos. Gltf enables efficient rendering of elements included in a scene. For example, with gltf, a final node can be linked to a single 3D model file. That is, each component (element) that constitutes a node that composes a scene can be linked to a separate 3D model file. For example, in Figure 11, the static node "road" can be linked to "road.ply," and the car model can be linked to the file "car.obj." In other words, a final node is a leaf node that no longer has child nodes, and the final node is linked to the 3D model file that is actually rendered or displayed on the screen.

[0232] However, for point cloud content for autonomous driving applications that require low latency, it does not make sense to compose a scene with multiple files as in Fig. 11. In other words, a scene graph structure such as Fig. 11 is not suitable for a real-time environment such as autonomous driving. Therefore, for content of a street captured from a lidar device of a dynamically moving vehicle, especially for content used in real time, it is more efficient to provide a function that can compose a scene while compressing the point cloud rather than composing a scene description or scene graph used in graphics.

[0233] Furthermore, in autonomous driving, various types of equipment can be used to support each other. For example, if a LiDAR device can construct 3D points and identify the location of each object through 2D image information, the present disclosure can input object location information into a point cloud (or point cloud data) constructed through the LiDAR device and, by extending the concepts of the Largest Prediction Unit (LPU) and the Prediction Unit (PU), add information that composes the scene. In this case, G-PCC compression can be efficiently performed, and additional information can be provided to the renderer, allowing for additional rendering changes to be made. Furthermore, it can be efficiently utilized for application-specific use cases.

[0234] In other words, the present disclosure provides a method for representing scenes with compression, using LPUs and PUs, to efficiently support the compression of point cloud content captured by lidar equipment on a moving vehicle. In other words, the present disclosure proposes a method for composing scenes using LPUs and PUs to enhance the compression efficiency of G-PCC.

[0235] The following describes how to extend LPU and PU to compose a scene and how to signal it.

[0236] The present disclosure allows for modifications and combinations between embodiments. Furthermore, the terms used in this document can be understood based on their intended meaning, within the scope widely used in the relevant field. Furthermore, scene representation through LPU and PU is performed in the geometry encoder of a transmitting device, transmitted to the geometry decoder of a receiving device, and the scene can be reconstructed through a decoding process in the geometry decoder.

[0237] This document divides points in point cloud data into LPUs and / or PUs to represent a scene.

[0238] More specifically, this document can separate a road (or ground) and an object from point cloud data (or point cloud content or content) captured (or acquired) by a lidar device, configure an LPU or PU with the points of the separated road, and configure an LPU or PU with the points of the separated object(s). At this time, the point cloud data (or point cloud content) can be in units of frames, tiles, or slices. In other words, point cloud content, frames, tiles, slices, etc. can be referred to as point cloud data. For example, a road and an object can be separated from point cloud data in units of slices, configure an LPU (or PU) with the points of the road, and configure an LPU (or PU) with the points of the object. At this time, the LPU composed of the points of the objects can be further divided into at least one LPU and / or at least one PU. At this time, the division process can be continued until no more LPU and / or PU division is necessary or until a specified level is reached.

[0239] Scene composition unit

[0240] The present disclosure may define LPUs and PUs to represent compression and scenes together in an encoder (e.g., a geometry encoder) of a transmitting device.

[0241] In this disclosure, a scene is a 2 having a minimum k that contains the entire content. k It can be defined as a bounding box area. At this time, 2 containing the content k The bounding box k is the minimum value of k. And, the scene can be composed of one or more LPUs and one or more PUs.

[0242] In this disclosure, an LPU can be defined as a large group of points with similar characteristics or points with different characteristics, allowing the use of the same coding tool. Furthermore, an LPU can be defined as a group of PUs.

[0243] In the present disclosure, a PU can be defined as a group of minimum points having the same characteristics. That is, a road PU or an object PU can correspond to this.

[0244] Fig. 12 is a diagram showing an example of a process unit that constitutes a scene according to embodiments. That is, Fig. 12 shows an example of a scene constituted by LPUs and PUs.

[0245] In the scene of Fig. 12, assuming that the route is point cloud data (or point cloud content or content) captured (or acquired) by a lidar device, the route can be composed of a road PU and an object LPU. That is, the road and the object can be separated from the point cloud data (or point cloud content or content), the road PU can be composed of the points of the separated road, and the object LPU can be composed of the points of the separated object(s). Then, the object LPU can be divided again into a car PU and a mixed LPU, and the mixed LPU can be divided again into a building PU and a utility pole PU. That is, the object LPU corresponds to the parent node (or upper node) of the car PU and the mixed LPU, and the mixed LPU corresponds to the parent node (or upper node) of the building PU and the utility pole PU. Conversely, the car PU and the mixed LPU correspond to child nodes of the object LPU, and the building PU and the utility pole PU correspond to child nodes of the mixed LPU.

[0246] In the present disclosure, the LPU and PU may each have origin information and box size information.

[0247] According to embodiments, the origin information of the root can be defined as the Molton code value 0 of (0,0,0).

[0248] According to embodiments, the origin information of an LPU or PU can be defined as a molton code value of a left / right / front point. That is, it can be defined as a molton code value based on the origin value of a parent node of the scene.

[0249] According to embodiments, the size (or box size or bounding box size) information of an LPU or PU can be defined as a level of an occupancy tree.

[0250] Taking Fig. 12 as an example, the origin information of the road PU is 0 (Molton code), the size information is 2 (occupancy level), and the origin information of the object LPU is 16 (Molton code), the size information is 8 (occupancy level).

[0251] In this disclosure, each LPU and PU has an ID (identification) value. In addition, the LPU can have parent-child relationship information.

[0252] Additionally, each LPU and PU can have a type, which can represent the characteristics of points in the area, such as ground, object, dynamic object, or static object.

[0253] In the case of origin information in the present disclosure, PUs belonging to upper nodes can be sorted in Molton order and the residual values ​​of origin information between PUs can be entropy-coded and transmitted to a receiving device.

[0254] And, the scene composition is divided according to geometry, and the attributes can follow the shape composed from the geometry.

[0255] Meanwhile, in the present disclosure, the remaining range outside the ground PU range can be divided into LPUs containing all objects. These LPUs can be further grouped into LPUs or PUs based on the characteristics of the content or their location.

[0256] At this time, the LPU can apply different coding tools. Furthermore, the LPU can include multiple PUs.

[0257] That is, the present disclosure can define a range outside the ground PU range as an object LPU. In other words, when dividing points of point cloud data into ground PUs and object LPUs, the range outside the ground PU range can be defined as an object LPU. This may mean that the ground is defined as a road PU. In addition, PUs can be grouped into object LPUs based on location. Furthermore, LPUs can be grouped based on selection time and / or location, and then further divided into LPUs or PUs.

[0258] For example, the road PU in FIG. 12 corresponds to the ground PU. That is, in the case of content captured via LiDAR equipment, the characteristics of ground points corresponding to roads or floors may differ significantly from the characteristics of points corresponding to objects. In this case, various methods for configuring the ground PU may be provided. For example, for any point in the content, the point may be segmented as a road using the z-axis variance information value and / or a z-axis-specific threshold value, or the point may be segmented as a road using the normal value of the point when the normal value is within the range of the same vector as the z-axis. In this way, the present disclosure can segment the ground PU in various ways. In addition, different coding tools may be applied to each PU.

[0259] In the present disclosure, an object LPU may be composed of object PUs. According to embodiments, the present disclosure may compose object PUs by traversing an occupancy tree (or octree) in a bottom-up manner.

[0260] In the present disclosure, object LPUs and PUs can be configured in the following manner.

[0261] - When the occupancy percentage (e.g., the number of points) in a node is greater than a certain threshold, it can be moved to a higher level in the occupancy tree. This means that there are many points in that node, and in this case, it is moved to a higher level because there may be many points in the same object. That is, as you go up in the occupancy tree from the bottom to the top, two or more levels (e.g., a child node and a parent node) can be grouped and defined as a single PU (or LPU). As another example, at least one PU and at least one LPU can be defined at the same level.

[0262] - When the occupancy percentage at the upper level exceeds a certain threshold, you can move to another higher level of the occupancy tree.

[0263] - When it is below the threshold value, all child nodes of the node can be defined as PUs, and the node can be defined as an object LPU.

[0264] Here, the threshold can be defined for each occupancy level (i.e., tree level), or can be defined and applied as a single threshold. Taking the object LPU of Fig. 12 as an example, by traversing the object LPU's occupancy tree from the bottom up, whether one or more nodes can be grouped as the same object can be estimated, and thus configured as a building PU, a utility pole PU, a car PU, a mixed PU, etc.

[0265] Additionally, different coding tools can be applied to each PU. PUs can also define semantic type values. For example, they can add characteristics such as ground, dynamic objects, and static objects.

[0266] Another example of configuring LPUs and PUs is that, in the case of autonomous driving, as mentioned above, various types of equipment can be used as a concept to assist each other. For example, if 3D points are constructed using LiDAR equipment and the location of each object can be identified through 2D image information, the location and size information of the object can be input into the point cloud constructed using LiDAR equipment, and the corresponding area can be organized into PUs, and the ID and characteristics transferred through image processing can be applied to the PUs. In addition, if PUs can be grouped by area, they can be organized into LPUs. Taking the object LPU of Fig. 12 as an example, in this case, there is no estimation process, and it can be organized into a building PU, a utility pole PU, a car PU, a mixed PU, etc. based on the origin information and size information of the object obtained through 2D image information.

[0267] Next, we will describe a point cloud transmission device to which the method of composing a scene based on the aforementioned LPU / PU is applied.

[0268] FIG. 13 is a drawing showing another example of a point cloud transmission device according to embodiments.

[0269] A point cloud transmission device according to embodiments may include a data input unit (51001), a coordinate system transformation unit (51002), a quantization processing unit (51003), a space division unit (51004), a signaling processing unit (51005), a geometry encoder (51006), an attribute encoder (51007), and a transmission processing unit (51008). According to embodiments, the coordinate system transformation unit (51002), the quantization processing unit (51003), the space division unit (51004), the geometry encoder (51006), and the attribute encoder (51007) may be referred to as a point cloud video encoder.

[0270] The point cloud transmission device of FIG. 13 may correspond to the transmission device (10000), the point cloud video encoder (10002), the transmitter (10003) of FIG. 1, the acquisition-encoding-transmission (20000-20001-20002) of FIG. 2, the point cloud video encoder of FIG. 3, the transmission device of FIG. 8, the device of FIG. 10, etc. The elements of the point cloud transmission device illustrated in FIG. 13 may be implemented by hardware, software, a processor connected to a memory, and / or a combination thereof. That is, the elements of the point cloud transmission device of FIG. 13 may be implemented by hardware, software, firmware, or a combination thereof, including one or more processors or integrated circuits configured to be able to communicate with one or more memories, although not illustrated in the drawing. One or more processors may perform at least one or more of the operations and / or functions of the elements of the point cloud transmission device of FIG. 13 described above. Additionally, one or more processors may operate or execute a set of software programs and / or instructions for performing operations and / or functions of elements of the point cloud transmitting device of FIG. 13.

[0271] The data input unit (51001) may perform part or all of the operations of the point cloud video acquisition unit (10001) of FIG. 1, or part or all of the operations of the data input unit (8000) of FIG. 8. In addition, the coordinate system transformation unit (51002) may perform part or all of the operations of the coordinate system transformation unit (30000) of FIG. 3. In addition, the quantization processing unit (51003) may perform part or all of the operations of the quantization unit (30001) of FIG. 3, or part or all of the operations of the quantization processing unit (8001) of FIG. 8. That is, the data input unit (51001) may receive data in order to encode point cloud data. Data can be geometry data (which can be referred to as geometry, geometry information, etc.), attribute data (which can be referred to as attribute, attribute information, etc.), parameter information indicating coding-related settings, etc.

[0272] The above coordinate system conversion unit (51002) can support coordinate system conversion of point cloud data, such as changing the xyz axis or converting the coordinate system from the xyz orthogonal coordinate system to a spherical coordinate system.

[0273] The above quantization processing unit (51003) can quantize point cloud data. For example, the scale can be adjusted by multiplying the position x, y, and z values ​​of the point cloud data by a scale (scale = geometry quantization value) setting. The scale value can follow the set value or be included in the bitstream as parameter information and transmitted to the receiving end.

[0274] The above-described spatial division unit (51004) can spatially divide the point cloud data quantized and output from the quantization processing unit (51003) into one or more 3D blocks based on a bounding box and / or a sub-bounding box. For example, the above-described spatial division unit (51004) can divide the quantized point cloud data into tile units or slice units for region-specific access or parallel processing of content. In addition, signaling information for spatial division can be entropy-encoded in the signaling processing unit (51005) and then transmitted in the form of a bitstream through the transmission processing unit (51008).

[0275] In one embodiment, point cloud content may be a single person or multiple people, or a single object or multiple objects, such as an actor. However, it may also be a map for autonomous driving or a map for indoor navigation of a robot on a larger scale. Furthermore, point cloud content may be point cloud data captured by a lidar device from a moving or stationary vehicle. In such cases, point cloud content may be a large amount of locally connected data. Therefore, since point cloud content cannot be encoded / decoded in one go, tile partitioning may be performed before compression of the point cloud content.

[0276] That is, a tile may refer to a portion of a three-dimensional space (e.g., a rectangular cube) occupied by point cloud data according to embodiments. A tile according to embodiments may include one or more slices. A tile according to embodiments may be divided (partitioned) into one or more slices, so that a point cloud video encoder can encode point cloud data in parallel.

[0277] A slice may refer to a unit of data (or bitstream) that can be independently encoded in a point cloud video encoder according to embodiments and / or a unit of data (or bitstream) that can be independently decoded in a point cloud video decoder. A slice according to embodiments may refer to a set of data in a three-dimensional space occupied by point cloud data, or may refer to a set of some data among point cloud data. A slice may refer to an area of ​​points or a set of points included in a tile according to embodiments. A tile according to embodiments may be divided into one or more slices based on the number of points included in one tile. For example, one tile may refer to a set of points divided according to the number of points. A tile according to embodiments may be divided into one or more slices based on the number of points, and during the division process, some data may be split or merged. In other words, a slice may be a unit that can be independently coded within the corresponding tile. Tiles divided into spaces in this way can be further divided into one or more slices for fast and efficient processing.

[0278] A point cloud video encoder according to embodiments may encode point cloud data in units of slices or in units of tiles including one or more slices. In addition, the point cloud video encoder according to embodiments may perform quantization and / or transformation differently for each tile or slice.

[0279] In the above space division unit (51004), positions of one or more three-dimensional blocks (e.g., slices) that are space-divided are output to a geometry encoder (51006), and attribute information (or attributes) are output to an attribute encoder (51007). The positions may be location information of points included in the divided units (boxes or blocks or tiles or tile groups or slices) and are referred to as geometry information.

[0280] The above geometry encoder (51006) performs encoding based on inter-prediction or intra-prediction on positions output from the space segmentation unit (51004) to output a geometry bitstream. At this time, the geometry encoder (51006) can apply the LPU / PU segmentation method of the present disclosure to point cloud data (or point cloud content) in units of frames, tiles, or slices to segment them into LPUs and / or PUs. That is, the geometry encoder (51006) can perform an LPU / PU segmentation / integration process.

[0281] The above geometry encoder (51006) can construct a scene from point cloud data in units of frames, tiles, or slices based on information indicating whether a scene is constructed (e.g., scene_description_enable_flag), and signal information about the constructed scene to transmit it to a decoder. In the present disclosure, information indicating whether a scene is constructed (e.g., scene_description_enable_flag) can be referred to as scene construction information.

[0282] In addition, in order to compose a scene, the geometry encoder (51006) can generate a ground PU through a ground threshold value, and compose the remaining area as an object LPU. For example, in the case of an object LPU, an object PU can be composed through an object threshold value while traversing the occupancy tree bottom-up, and if adjacent object PUs exist, these PUs can be grouped to compose an object LPU. As another example, the location (i.e., origin information) / size information of an object identified through image processing can be directly transmitted to compose a ground PU, an object PU, and an object LPU.

[0283] The above geometry encoder (51006) can compose a scene with one or more LPUs and one or more PUs configured as above.

[0284] At this time, the geometry encoder (51006) and / or the signaling processing unit (51005) can signal information indicating whether a scene is configured (e.g., scene_description_enable_flag) and scene configuration information and transmit them to the decoder of the receiving device. For example, ID information, location information, size information, characteristic (type) information, parent node information, threshold information, and applied coding tool information for identifying each LPU, PU, ​​and / or object can be transmitted to the decoder of the receiving device.

[0285] Additionally, the geometry encoder (51006) can reconstruct encoded geometry information and output it to the attribute encoder (51007).

[0286] The above attribute encoder (51007) encodes (i.e., compresses) attributes (e.g., segmented attribute original data) output from the space segmentation unit (51004) based on the reconstructed geometry output from the geometry encoder (51006) and outputs an attribute bitstream.

[0287] In the present disclosure, signaling may be performed in a geometry encoder (51006), an attribute encoder (51007), and / or a signaling processor (51005).

[0288] FIG. 14 is a diagram showing an example of the operation of a geometry encoder (51006) and an attribute encoder (51007) according to embodiments. Elements of FIG. 14 and corresponding drawings may correspond to software, hardware, a processor connected to a memory, and / or a combination thereof. That is, elements of the geometry encoder (51006) and the attribute encoder (51007) of FIG. 14 may be implemented by hardware, software, firmware, or a combination thereof, including one or more processors or integrated circuits configured to communicate with one or more memories, although not shown in the drawings. One or more processors may perform at least one of the operations and / or functions of the elements of the geometry encoder (51006) and the attribute encoder (51007) of FIG. 14 described above. Additionally, one or more processors may operate or execute a set of software programs and / or instructions for performing operations and / or functions of elements of the geometry encoder (51006) and the attribute encoder (51007) of FIG. 14.

[0289] In one embodiment, a quantization processing unit may be further provided between the spatial division unit (51004) and the voxelization processing unit (53001). The quantization processing unit quantizes positions of one or more 3D blocks (e.g., slices) spatially divided by the spatial division unit (51004). In this case, the quantization unit may perform part or all of the operation of the quantization unit (30001) of FIG. 3, or part or all of the operation of the quantization processing unit (8001) of FIG. 8. When the quantization processing unit is further provided between the spatial division unit (51004) and the voxelization processing unit (53001), the quantization processing unit (51003) of FIG. 13 may or may not be omitted.

[0290] A voxelization processing unit (53001) according to embodiments performs voxelization based on positions or quantized positions of one or more spatially divided 3D blocks (e.g., slices). Voxelization refers to the minimum unit that expresses position information in a 3D space. That is, the voxelization processing unit (53001) may support a process of rounding the geometric position values ​​of points to which scale is applied to an integer. Points of point cloud content (or 3D point cloud video) according to embodiments may be included in one or more voxels. According to embodiments, one voxel may include one or more points. In one embodiment, if quantization is performed before voxelization, a case may occur where multiple points belong to one voxel.

[0291] In the present disclosure, when two or more points are included in a single voxel, these two or more points are referred to as duplicate points (or overlapping points). That is, duplicate points can be generated through geometry quantization and voxelization during the geometry encoding process.

[0292] The voxel processing unit (53001) according to the embodiments may output duplicate points belonging to one voxel as is without merging them, or may output them by merging duplicate points into one point.

[0293] According to embodiments, points divided into tiles or slices in the space division unit (51004) or points in frame units are voxelized in the voxelization processing unit (53001), the voxelized points are divided into one or more LPUs and / or PUs in the scene composition unit (53002), and a scene can be composed based on the divided one or more LPUs and / or PUs. To this end, the scene composition unit (53002) can include an LPU / PU division unit. For example, the points voxelized in the voxelization processing unit (53001) can be divided into ground PUs and / or object LPUs in the scene composition unit (53002), and the object LPUs can be divided into one or more LPUs and / or PUs.

[0294] The above scene composition unit (53002) can divide points of point cloud data in units of frames, tiles, or slices into one or more LPUs and / or one or more PUs based on information indicating whether a scene is composed (e.g., scene_description_enable_flag), compose a scene with the divided one or more LPUs and / or one or more PUs, and signal information about the composed scene and transmit it to a decoder.

[0295] More specifically, in order to compose a scene, the scene composition unit (53002) can generate a ground PU through a ground threshold value and compose other areas as object LPUs. For example, in the case of an object LPU, the scene composition unit (53002) can compose an object PU through an object threshold value while traversing the occupancy tree bottom-up, and if adjacent object PUs exist, group these PUs to compose an object LPU. As another example, the scene composition unit (53002) can directly receive the location (i.e., origin information) / size information of an object identified through image processing and compose a ground PU, an object PU, and an object LPU.

[0296] To this end, the scene composition unit (53002) may receive information indicating whether a scene is composed. In addition, the scene composition unit (53002) may receive ground threshold information, object threshold information, and / or object position / size information identified through image processing for LPU / PU segmentation.

[0297] And, the scene composition unit (53002) can compose a scene with one or more LPUs and one or more PUs configured as above.

[0298] At this time, information indicating whether a scene is composed (e.g., scene_description_enable_flag) and scene composition information may be transmitted to the decoder of the receiving device. For example, the scene composition information may include identification information for identifying each LPU and PU (e.g., LPU[].id, PU[].id), location information (e.g., LPU[].origin, PU[i].origin), size information (e.g., LPU[].size, PU[].size), characteristic (type) information (e.g., PU[].type), parent node information (e.g., PU[].parentID), threshold information (e.g., ground_threshold, object_threshold), applied coding tool information (e.g., PU[].codingtool), etc., and such scene composition information may be transmitted to the decoder of the receiving device. In the present disclosure, the information indicating whether a scene is composed and the scene composition information are combined and referred to as scene composition-related information. In addition, the present disclosure may refer to the scene composition information as scene graph information or scene description information. Scene configuration information may further include LPU / PU configuration information.

[0299] In Fig. 14, reference numeral 53003 may be referred to as a determination unit that determines whether the output of the scene composition unit (53002) is to be encoded based on an octree, a prediction tree, or a tri-subset. The determination unit (53003) may be determined using signaling information or may be determined based on a preset method.

[0300] If it is confirmed as an octree-based compression method, the octree generation unit (53004) generates an octree based on the position information (i.e., geometry information) of the points output from the scene composition unit (53002), and compresses the geometry information based on the octree. If it is confirmed as a tri-soup-based compression method, the tri-soup generation unit (53006) generates a tri-soup based on the position information (i.e., geometry information) of the points output from the scene composition unit (53002), and compresses the geometry information based on the tri-soup.

[0301] If it is confirmed as a prediction tree-based compression method, the prediction tree generation unit (53005) generates a prediction tree based on the position information (i.e., geometry information) of points output from the scene composition unit (53002), and performs prediction tree-based intra-prediction or inter-prediction to generate at least one of a plurality of intra-prediction modes and / or a plurality of inter-prediction modes, and the prediction position value (or prediction position information) for each mode is output to the prediction decision unit (53007).

[0302] The above prediction decision unit (53007) can select an optimal prediction mode by performing an RDO (Rate-Distortion Optimization) process based on a prediction position value calculated based on one or more of the multiple intra prediction modes and / or multiple inter prediction modes generated by the above prediction tree generation unit (53005), i.e., a prediction position value for each intra and inter prediction mode.

[0303] The geometry position reconstruction unit (53008) according to the embodiments restores (or reconstructs) geometry information based on positions changed through geometry coding in the octree generation unit (53004), the prediction decision unit (53007), or the tri-subset generation unit (53006), and outputs the restored geometry information (or so-called restored geometry) to the attribute encoder (51007). That is, since the attribute information is dependent on the geometry information (position), the restored (or reconstructed) geometry information is required to compress the attribute information.

[0304] The geometry information entropy encoding unit (53009) according to the embodiments may perform entropy encoding on compressed geometry information to form a geometry information bitstream. In the case of origin information in the present disclosure, PUs belonging to upper nodes may be sorted in Moulton order, and the residual values ​​of origin information between PUs may be entropy-coded by the geometry information entropy encoding unit (53009) and transmitted to a receiving device.

[0305] The color conversion processing unit (55001) of the attribute encoder (51007) corresponds to the color conversion processing unit (30006) of FIG. 3 or the color conversion processing unit (8008) of FIG. 8. The color conversion processing unit (55001) according to embodiments performs color conversion coding to convert color values ​​(or textures) included in attributes provided from the data input unit (53001) and / or the space division unit (53004). For example, the color conversion processing unit (55001) may convert the format of color information (e.g., convert from RGB to YCbCr). The operation of the color conversion processing unit (55001) according to embodiments may be optionally applied depending on the color values ​​included in the attributes. In another embodiment, the color conversion processing unit (55001) may perform color conversion coding based on reconstructed geometry. The following description is omitted here and refers to the description of Fig. 3 and / or Fig. 8.

[0306] The attribute information entropy encoding unit (55002) according to the embodiments performs entropy encoding on compressed attribute information to output an attribute bitstream (or attribute information bitstream).

[0307] Meanwhile, the geometry bitstream compressed and output from the geometry encoder (51006) and the attribute bitstream compressed and output from the attribute encoder (51007) are output to the transmission processing unit (51008).

[0308] The transmission processing unit (51008) according to the embodiments may perform the same or similar operation and / or transmission method as the operation and / or transmission method of the transmission processing unit (8012) of FIG. 8, and may perform the same or similar operation and / or transmission method as the operation and / or transmission method of the transmitter (10003) of FIG. 1. A specific description will be omitted here, referring to the description of FIG. 1 or FIG. 8.

[0309] The transmission processing unit (51008) according to the embodiments may transmit the geometry bitstream output from the geometry encoder (51006), the attribute bitstream output from the attribute encoder (51007), and the signaling bitstream output from the signaling processing unit (51005) separately, or may multiplex them into a single bitstream and transmit them.

[0310] The transmission processing unit (51008) according to the embodiments may encapsulate a bitstream into a file or segment (e.g., a streaming segment) and then transmit it through various networks such as a broadcasting network and / or a broadband network.

[0311] A signaling processing unit (51005) according to embodiments may generate and / or process signaling information and output it to a transmission processing unit (51008) in the form of a bitstream. The signaling information generated and / or processed by the signaling processing unit (51005) may be provided to a geometry encoder (51006), an attribute encoder (51007), and / or a transmission processing unit (51008) for geometry encoding, attribute encoding, and transmission processing, or the signaling processing unit (51005) may receive signaling information generated by the geometry encoder (51006), the attribute encoder (51007), and / or the transmission processing unit (51008).

[0312] In the present disclosure, signaling information may be signaled and transmitted in units of parameter sets (e.g., sequence parameter set (SPS), geometry parameter set (GPS), attribute parameter set (APS), tile parameter set (TPS), etc.). In addition, signaling information may be signaled and transmitted in units of coding units of each image, such as slices or tiles. In the present disclosure, signaling information may include metadata (e.g., setting values, etc.) regarding point cloud data, and may be provided to a geometry encoder (51006), an attribute encoder (51007), and / or a transmission processor (51008) for geometry encoding, attribute encoding, and transmission processing. Depending on the application, signaling information may also be defined in a system layer such as a file format, dynamic adaptive streaming over HTTP (DASH), MPEG media transport (MMT), or a wired interface layer such as High Definition Multimedia Interface (HDMI), Display Port, VESA (Video Electronics Standards Association), or CTA.

[0313] The method / device according to the embodiments may signal relevant information to add / perform the operations of the embodiments. The signaling information according to the embodiments may be used in a transmitting device and / or a receiving device.

[0314] In the present disclosure, information indicating whether a scene is composed and some or all of the scene composition information may be signaled in at least one of the SPS, TPS, GPS, and / or the geometry header for each slice. In the present disclosure, information indicating whether a scene is composed and the scene composition information may be referred to as parameter information for scene composition.

[0315] FIG. 15 is a drawing showing another example of a point cloud receiving device according to embodiments.

[0316] A point cloud receiving device according to embodiments may include a receiving processing unit (61001), a signaling processing unit (61002), a geometry decoder (61003), an attribute decoder (61004), and a post-processor (61005). According to embodiments, the geometry decoder (61003) and the attribute decoder (61004) may be referred to as a point cloud video decoder. According to embodiments, the point cloud video decoder may be referred to as a PCC decoder, a PCC decoding unit, a point cloud decoder, a point cloud decoding unit, etc.

[0317] The point cloud receiving device of FIG. 15 may correspond to the receiving device (10004), the receiver (10005), the point cloud video decoder (10006) of FIG. 1, the transmission-decoding-rendering (20002-20003-20004) of FIG. 2, the point cloud video decoder of FIG. 7, the receiving device of FIG. 9, the device of FIG. 10, etc. The elements of FIG. 15 and the corresponding drawings may correspond to software, hardware, a processor connected to a memory, and / or a combination thereof. That is, the elements of the point cloud receiving device of FIG. 15 may be implemented by hardware, software, firmware, or a combination thereof, including one or more processors or integrated circuits configured to be able to communicate with one or more memories, although not shown in the drawings. One or more processors may perform at least one or more of the operations and / or functions of the elements of the point cloud receiving device of FIG. 15 described above. Additionally, one or more processors may operate or execute a set of software programs and / or instructions for performing operations and / or functions of elements of the point cloud receiving device of FIG. 15.

[0318] The receiving processing unit (61001) according to the embodiments may receive one bitstream, or may receive each of a geometry bitstream (or geometry information bitstream), an attribute bitstream (or attribute information bitstream), and a signaling bitstream. When a file and / or segment is received, the receiving processing unit (61001) according to the embodiments may decapsulate the received file and / or segment and output it as a bitstream.

[0319] According to the embodiments, when one bitstream is received (or decapsulated), the reception processing unit (61001) can demultiplex a geometry bitstream, an attribute bitstream, and / or a signaling bitstream from the one bitstream, and output the demultiplexed signaling bitstream to the signaling processing unit (61002), the geometry bitstream to the geometry decoder (61003), and the attribute bitstream to the attribute decoder (61004).

[0320] According to the embodiments, when a geometry bitstream, an attribute bitstream, and / or a signaling bitstream are each received (or decapsulated), the receiving processing unit (61001) can transmit the signaling bitstream to the signaling processing unit (61002), the geometry bitstream to the geometry decoder (61003), and the attribute bitstream to the attribute decoder (61004).

[0321] The signaling processing unit (61002) may parse and process signaling information, such as SPS, GPS, APS, TPS, and metadata, from the input signaling bitstream and provide the information to the geometry decoder (61003), the attribute decoder (61004), and the post-processing unit (61005). In another embodiment, the signaling information included in the geometry slice header and / or the attribute slice header may also be pre-parsed by the signaling processing unit (61002) before decoding the corresponding slice data. That is, if the point cloud data is divided into tiles and / or slices on the transmitting side, the TPS includes the number of slices included in each tile, so the point cloud video decoder according to the embodiments can check the number of slices and quickly parse information for parallel decoding.

[0322] Accordingly, the point cloud video decoder according to the present disclosure can quickly parse a bitstream including point cloud data by receiving an SPS with a reduced amount of data. The receiving device can perform decoding of the tiles as they are received, and can maximize decoding efficiency by performing decoding on a slice-by-slice basis based on the GPS and APS included in each tile. Alternatively, the receiving device can maximize decoding efficiency by performing decoding of geometry information by composing a scene based on information indicating whether a scene is composed and the scene composition information signaled in the SPS, GPS, TPS, and geometry slice headers.

[0323] That is, the geometry decoder (61003) can restore the geometry by performing the reverse process of the geometry encoder (51006) of FIG. 13 based on signaling information (e.g., geometry-related parameters) for the compressed geometry bitstream. The geometry restored (or reconstructed) by the geometry decoder (61003) is provided to the attribute decoder (61004).

[0324] According to embodiments, the geometry decoder (61003) can parse and restore information indicating whether a scene is composed and the scene composition information included in the signaling information, and restore LPUs / PUs based on the restored information to reconstruct the scene. According to embodiments, the geometry decoder (61003) can restore LPUs / PUs to reconstruct the scene if the information indicating whether a scene is composed indicates a scene composition. According to embodiments, the reconstructed scene can be used for rendering in units of LPUs / PUs. In the present disclosure, scene composition information may be referred to as scene graph information or scene description information, and may include identification information of each LPU / PU (e.g., LPU[].id, PU[].id), location information (e.g., LPU[].origin, PU[i].origin), size information (e.g., LPU[].size, PU[].size), characteristic (type) information (e.g., PU[].type), parent node information (e.g., PU[].parentID), threshold information (e.g., ground_threshold, object_threshold), applied coding tool information (e.g., PU[].codingtool), etc. In the present disclosure, information indicating whether a scene is composed may be referred to as scene composition information. Since information included in the scene composition information in the present disclosure may be added, deleted, or modified according to those skilled in the art, the present invention is not limited to the above-described examples. Information indicating whether a scene is composed and the scene composition information will be described in detail later with reference to FIGS. 18 to 21.

[0325] The above attribute decoder (61004) can restore attributes by performing the reverse process of the attribute encoder (51007) of FIG. 13 based on signaling information (e.g., attribute-related parameters) and reconstructed geometry for the compressed attribute bitstream.

[0326] FIG. 16 is a diagram showing an example of the operation of a geometry decoder (61003) and an attribute decoder (61004) according to embodiments. Elements of FIG. 16 and corresponding drawings may correspond to software, hardware, a processor connected to a memory, and / or a combination thereof. That is, elements of the geometry decoder (61003) and the attribute decoder (61004) of FIG. 16 may be implemented by hardware, software, firmware, or a combination thereof, including one or more processors or integrated circuits configured to communicate with one or more memories, although not shown in the drawings. One or more processors may perform at least one of the operations and / or functions of the elements of the geometry decoder (61003) and the attribute decoder (61004) of FIG. 16 described above. Additionally, one or more processors may operate or execute a set of software programs and / or instructions for performing operations and / or functions of elements of the geometry decoder (61003) and the attribute decoder (61004) of FIG. 16.

[0327] The geometry information entropy decoding unit (63001) included in the geometry decoder (61003) of FIG. 16 may perform part or all of the operations of the arithmetic decoder (11000) of FIG. 7, or part or all of the operations of the arithmetic decoder (9002) of FIG. 9. That is, the geometry information entropy decoding unit (63001) entropy decodes the input geometry bitstream. The geometry information entropy decoding unit (63001) may additionally entropy decode the residual value of origin information between PUs.

[0328] According to embodiments, if information indicating whether a scene is composed and scene composition information are signaled in at least one of a sequence parameter set (SPS), a geometry parameter set (GPS), a tile parameter set (TPS), and / or a geometry slice header, the signaling processing unit (61002) may obtain the information and provide it to a geometry decoder (61003), or may obtain the information directly from the geometry decoder (61003).

[0329] According to embodiments, the scene reconstruction unit (63002) can reconstruct a scene by restoring LPUs / PUs from entropy-decoded geometry information by applying the scene composition information and the information indicating whether a scene is composed, which are included in the signaling information. According to embodiments, the scene reconstruction unit (63002) reconstructs a scene by restoring LPUs / PUs when the information indicating whether a scene is composed indicates a scene composition. According to embodiments, the reconstructed scene can be used for rendering in units of LPUs / PUs. In the present disclosure, the scene composition information may be referred to as scene graph information, scene description information, or LPU / PU composition information, and may include identification information (ID) of each LPU / PU, location information, size information, characteristic (type) information, parent node information, threshold information, and applied coding tool information.

[0330] According to embodiments, if octree-based encoding is applied to geometry information at the transmitting side, the octree reconstruction unit (63004) performs octree-based restoration on the geometry information output from the scene reconstruction unit (63002). Conversely, if prediction-based encoding is applied to geometry information at the transmitting side, the prediction tree reconstruction unit (63005) performs prediction-based restoration on the geometry information output from the scene reconstruction unit (63002). In addition, if tryshop-based encoding is applied to geometry information at the transmitting side, the tryshop generation unit (63006) performs tryshop-based restoration on the geometry information output from the scene reconstruction unit (63002).

[0331] To this end, drawing code 63003 (or the determination unit) determines whether octree-based coding, prediction-based coding, or tryshop-based coding is applied to the geometry information.

[0332] The geometry information restored in the above octree reconstruction unit (63004), prediction tree reconstruction unit (63005), or tryshop generation unit (63006) is output to the geometry position reconstruction unit (63007) to reconstruct the position of the geometry information.

[0333] The output of the above geometry position reconstruction unit (63007) is input to the geometry information inverse transformation inverse quantization processing unit (63009) through the geometry information prediction unit (63008).

[0334] The above geometry information inverse transformation inverse quantization unit (63009) can perform the reverse process of the transformation performed by the geometry information transformation quantization processing unit (51003) of the transmitting device on the geometry information of which the position is reconstructed, and multiply the result by a scale (=geometry quantization value) to generate restored geometry information for which inverse quantization has been performed. That is, the geometry information inverse transformation inverse quantization processing unit (63009) can perform inverse quantization of the geometry information by applying the scale (scale=geometry quantization value) included in the signaling information to the geometry position x, y, and z values ​​of the restored point.

[0335] The above coordinate system inversion unit (63010) can perform the reverse process of the coordinate system transformation performed by the coordinate system transformation unit (51002) of the transmitting device on the inverse quantized geometry information. For example, the coordinate system inversion unit (63010) restores the xyz axes changed on the transmitting side or inversely transforms the transformed coordinate system into the xyz orthogonal coordinate system and outputs it to the post-processing unit (61005) of FIG. 15.

[0336] According to embodiments, the geometry information output from the geometry information prediction unit (63008) goes through a geometry restoration process and is output to the attribute decoder (61004).

[0337] According to embodiments, the attribute information entropy decoding unit (65001) of the attribute decoder (61004) can entropy decode residual information included in an input attribute bitstream. The following description refers to the description of FIG. 7 and / or FIG. 9 and is omitted here.

[0338] According to embodiments, the restored attribute information may be provided to a color inverse transformation processing unit (65003) through an attribute information inverse quantization processing unit (65002) to be restored to RGB colors. That is, the color inverse transformation processing unit (65003) performs inverse transformation coding to inversely transform the color value (or texture) included in the restored attribute information and outputs it to the post-processing unit (61005) of FIG. 15.

[0339] The post-processing unit (61005) above can reconstruct point cloud data by matching the geometry information (i.e., positions) restored and output from the geometry decoder (61003) with the attribute information restored and output from the attribute decoder (61004). In addition, if the reconstructed point cloud data is in tile and / or slice units, the post-processing unit (61005) can perform the reverse process of the space division of the transmitting side based on signaling information.

[0340] Fig. 17 shows an example of a bitstream structure of point cloud data for transmission / reception according to embodiments.

[0341] Related information can be signaled to add / perform the embodiments described so far. The signaling information according to the embodiments can be used in a point cloud video encoder at a transmitting end or a point cloud video decoder at a receiving end.

[0342] A point cloud video encoder according to embodiments may encode geometry information and attribute information as described above to generate a bitstream as in FIG. 17. In addition, signaling information regarding point cloud data may be generated and processed by at least one of a geometry encoder, an attribute encoder, and a signaling processing unit of the point cloud video encoder and included in the bitstream.

[0343] Signaling information according to embodiments may be received / obtained by at least one of a geometry decoder, an attribute decoder, and a signaling processing unit of a point cloud video decoder.

[0344] The bitstream according to the embodiments may be transmitted / received as being divided into a geometry bitstream, an attribute bitstream, and a signaling bitstream, or may be transmitted / received by being combined into a single bitstream.

[0345] When a geometry bitstream, an attribute bitstream, and a signaling bitstream according to embodiments are configured as one bitstream, the bitstream may include one or more sub-bitstreams. The bitstream according to embodiments may include a Sequence Parameter Set (SPS) for sequence-level signaling, a Geometry Parameter Set (GPS) for signaling geometry information coding, one or more Attribute Parameter Sets (APS0, APS1) for signaling attribute information coding, a Tile Parameter Set (TPS) for tile-level signaling, and one or more slices (slice 0 to slice n). That is, the bitstream of point cloud data according to embodiments may include one or more tiles, and each tile may be a group of slices including one or more slices (slice 0 to slice n). The TPS according to embodiments may include information about each tile (e.g., coordinate value information of a bounding box, height / size information, etc.) for one or more tiles. Each slice may contain one geometry bitstream (Geom0) and one or more attribute bitstreams (Attr0, Attr1). For example, the first slice (slice 0) may contain one geometry bitstream (Geom0 0) and one or more attribute bitstreams (Attr0 0 , Attr1 0 ) may be included.

[0346] A geometry bitstream (or geometry slice) within each slice may be composed of a geometry slice header (geom_slice_header) and geometry slice data (geom_slice_data). According to embodiments, the geometry bitstream within each slice is referred to as a geometry data unit, the geometry slice header is referred to as a geometry data unit header, and the geometry slice data is referred to as geometry data unit data.

[0347] Each attribute bitstream (or attribute slice) within each slice may be composed of an attribute slice header (attr_slice_header) and attribute slice data (attr_slice_data). According to embodiments, the attribute bitstream within each slice is referred to as an attribute data unit, the attribute slice header is referred to as an attribute data unit header, and the attribute slice data is also referred to as attribute data unit data.

[0348] The transmitting device according to the embodiments transmits point cloud data according to the bitstream structure as illustrated in FIG. 17, thereby enabling the application of different encoding operations according to importance, and providing a method for utilizing high-quality encoding methods in important areas. Furthermore, the device supports efficient encoding and transmission according to the characteristics of point cloud data and can provide attribute values ​​according to user requirements.

[0349] The receiving device according to the embodiments receives point cloud data according to the structure of the bitstream as illustrated in FIG. 17, thereby enabling application of different filtering (decoding methods) to each region (region divided into tiles or slices) instead of applying a complex decoding (filtering) method to the entire point cloud data, depending on the processing capacity of the receiving device. Accordingly, it is possible to provide better image quality to regions important to the user and ensure appropriate latency in the system.

[0350] As mentioned above, tiles or slices are provided to allow point cloud data to be divided into regions and processed. Furthermore, when dividing point cloud data into regions, options can be set to generate different sets of neighboring points for each region, providing a choice between low complexity but somewhat low reliability, or conversely, high complexity but high reliability.

[0351] According to embodiments, at least one of the SPS, TPS, GPS, and geometry slice headers may include information indicating whether a scene is composed and scene composition information. According to embodiments, at least one of the SPS, TPS, and GPS may include information indicating whether a scene is composed, and the geometry slice header may include information indicating whether a scene is composed and scene composition information.

[0352] That is, depending on where a signal (e.g., information indicating whether a scene is composed and scene composition information) is transmitted, it can have different meanings. If defined in SPS, it can be applied equally to the entire sequence. If defined in GPS, it can indicate that it is applied to geometry restoration. If defined in TPS, it can indicate that the signaling is applied only to points within a tile. If transmitted at the slice level, it can indicate that the signaling is applied only to the slice. In addition, if the fields defined below (or referred to as syntax elements) can be applied to multiple point cloud data streams as well as the current point cloud data stream, they can be transmitted through a higher-level parameter set, etc.

[0353] The term field, as used in the syntaxes of this specification described hereinafter, may have the same meaning as parameter or element.

[0354] Fig. 18 is a diagram showing an example of the syntax structure of a sequence parameter set (seq_parameter_set_rbsp()) (SPS) according to the present specification. The SPS may include sequence information of a point cloud data bitstream, and in particular, shows an example of including information indicating whether a scene is composed (scene_description_enable_flag).

[0355] In Figure 18, the profile_idc field indicates the profile that the bitstream conforms to.

[0356] If the value of the profile_compatibility_flags field is 1, it may indicate that the bitstream conforms to the profile indicated by the profile_idc field.

[0357] The level_idc field indicates the level to which the bitstream conforms.

[0358] The sps_seq_parameter_set_id field provides an identifier for the SPS for reference by other syntax elements.

[0359] The sps_num_attribute_sets field indicates the number of coded attributes in the bitstream.

[0360] According to embodiments, an SPS includes a loop that repeats as many times as the value of the sps_num_attribute_sets field. In this case, i is initialized to 0 and increases by 1 each time the loop is executed, and this loop is repeated until the value of i becomes the value of the sps_num_attribute_sets field. In one embodiment, this loop may include an attribute_dimension[i] field, an attribute_instance_id[i] field, etc.

[0361] The above attribute_dimension[i] field specifies the number of components of the i-th attribute.

[0362] The above attribute_instance_id[i] field indicates the instance identifier of the i-th attribute.

[0363] According to embodiments, the SPS may further include a scene_description_enable_flag field. The scene_description_enable_flag field specifies whether a scene configuration is applied to the corresponding sequence. In other words, it indicates whether a scene configuration is applied to the corresponding sequence. In one embodiment of the present disclosure, if the value of the scene_description_enable_flag field is 1, the scene configuration is applied to the corresponding sequence, and if it is 0, the scene configuration is not applied.

[0364] FIG. 19 is a diagram showing an embodiment of the syntax structure of a geometry parameter set (geometry_parameter_set()) (APS) according to the present specification. The GPS according to the embodiments may include information on a method for encoding geometry information of point cloud data included in one or more slices, and in particular, an example of including information (scene_description_enable_flag) indicating whether a scene is composed is shown. That is, information indicating whether a scene is composed can be added to the GPS and signaled during the geometry information encoding / decoding process.

[0365] In Figure 19, the gps_geom_parameter_set_id field represents the identifier of the GPS for reference by other syntax elements.

[0366] The gps_seq_parameter_set_id field indicates the value of sps_seq_parameter_set_id for the active SPS.

[0367] The geom_tree_type field indicates the coding type for the geometry.

[0368] In one embodiment, a value of 0 in the geom_tree_type field may indicate that the geometry is coded using an occupancy tree (or octree), and a value of 1 may indicate that the geometry is coded using a prediction tree.

[0369] According to embodiments, the GPS may further include a scene_description_enable_flag field. The scene_description_enable_flag field specifies whether a scene configuration is applied to the corresponding frame. In other words, it indicates whether the scene configuration is applied to the corresponding frame. In one embodiment of the present disclosure, if the value of the scene_description_enable_flag field is 1, it indicates that the scene configuration is applied to the corresponding frame, and if it is 0, it indicates that the scene configuration is not applied.

[0370] FIG. 20 is a diagram illustrating an example of a syntax structure of a tile parameter set (tile_parameter_set()) (TPS) according to the present disclosure. According to embodiments, the TPS (Tile Parameter Set) may also be referred to as a tile inventory. The TPS according to embodiments includes information related to each tile for each tile, and in particular, an example of including information indicating whether a scene is composed (scene_description_enable_flag) is shown.

[0371] A TPS according to embodiments includes a num_tiles field.

[0372] The num_tiles field above indicates the number of tiles signaled for the bitstream. If no tiles exist, the value of the num_tiles field will be 0 (when not present, num_tiles is inferred to be 0).

[0373] A TPS according to embodiments includes a loop that is repeated as many times as the value of the num_tiles field. In this case, i is initialized to 0, increases by 1 each time the loop is performed, and the loop is repeated until the value of i becomes the value of the num_tiles field. This loop may include a tile_bounding_box_offset_x[i] field, a tile_bounding_box_offset_y[i] field, a tile_bounding_box_offset_z[i] field, a tile_bounding_box_size_width[i] field, a tile_bounding_box_size_height[i] field, a tile_bounding_box_size_depth[i] field, and a scene_description_enable_flag[i] field.

[0374] The above tile_bounding_box_offset_x[i] field indicates the x offset of the i-th tile in the cartesian coordinates.

[0375] The above tile_bounding_box_offset_y[i] field indicates the y offset of the i-th tile in the orthogonal coordinate system.

[0376] The above tile_bounding_box_offset_z[i] field indicates the z offset of the i-th tile in the orthogonal coordinate system.

[0377] The above tile_bounding_box_size_width[i] field indicates the width of the i-th tile in the orthogonal coordinate system.

[0378] The above tile_bounding_box_size_height[i] field indicates the height of the i-th tile in the orthogonal coordinate system.

[0379] The above tile_bounding_box_size_depth[i] field indicates the depth of the i-th tile in the orthogonal coordinate system.

[0380] The above scene_description_enable_flag[i] field specifies whether a scene configuration is applied to the i-th tile in the orthogonal coordinate system. In other words, it indicates whether a scene configuration is applied to the corresponding tile. In one embodiment of the present disclosure, if the value of the scene_description_enable_flag field is 1, the scene configuration is applied to the corresponding tile, and if it is 0, the scene configuration is not applied.

[0381] FIG. 21 is a diagram showing an example of a syntax structure of a geometry slice header (geometry_slice_header()) according to embodiments. The geometry slice header according to embodiments includes signaling information for the corresponding geometry slice, and in particular, shows an example of including information indicating whether a scene is composed and scene composition information. In the present disclosure, the geometry slice header (geometry_slice_header()) may be referred to as a geometry data unit header (geometry_data_unit_header()).

[0382] A bitstream transmitted by a transmitting device according to embodiments (or a bitstream received by a receiving device) may include one or more slices. Each slice may include a geometry slice and an attribute slice. A geometry slice includes a geometry slice header (GSH). An attribute slice includes an attribute slice header (ASH).

[0383] The geometry slice header (geometry_slice_header()) according to embodiments may include a gsh_geom_parameter_set_id field, a gsh_tile_id field, a gsh_slice_id field, etc.

[0384] The above gsh_geom_parameter_set_id field specifies the value of the gps_geom_parameter_set_id of the active GPS.

[0385] The above gsh_tile_id field indicates the identifier of the corresponding tile referenced by the corresponding geometry slice header (GSH).

[0386] The above gsh_slice_id represents the identifier of the slice for reference by other syntax elements.

[0387] According to embodiments, the geometry slice header may further include a scene_description_enable_flag field. The scene_description_enable_flag field specifies whether a scene configuration is applied to the corresponding slice. In other words, it indicates whether a scene configuration is applied to the corresponding slice. In one embodiment of the present disclosure, if the value of the scene_description_enable_flag field is 1, it indicates that a scene configuration is applied to the corresponding slice, and if it is 0, it indicates that a scene configuration is not applied.

[0388] According to embodiments, the geometry slice header may further include scene composition information if the value of the scene_description_enable_flag field is 1, i.e., indicating that scene composition is applied to the corresponding slice.

[0389] According to embodiments, the scene configuration information may include a numofLPU field, a numofPU field, a ground_threshold field, and an object_threshold field included per octree depth (occtree_depth_minus1, or octree level) and LPU / PU configuration information.

[0390] The above numofLPU field indicates the number of LPUs specified in the scene configuration of the corresponding slice.

[0391] The above numofPU field indicates the number of PUs specified in the scene configuration of the corresponding slice.

[0392] The above ground_threshold field specifies the threshold for ground PU segmentation specified in the scene configuration of the corresponding slice.

[0393] According to embodiments, the scene configuration information may include an object_threshold field and LPU / PU configuration information while repeating as many times as the occtree_depth_minus1 value.

[0394] The above occtree_depth_minus1 is included in the geometry slice header when the value of the geom_tree_type field included in the GPS is 0, i.e., indicates that the geometry is coded using an occupancy tree (or octree).

[0395] The above occtree_depth_minus1 specifies the maximum number of tree levels that exist in the occupancy tree (or octree).

[0396] The above object_threshold[x] field specifies a threshold for object PU segmentation at the x-th tree level specified in the scene configuration of the corresponding slice. That is, the above object_threshold field specifies a threshold for object PU segmentation specified in the scene configuration of the corresponding slice.

[0397] LPU / PU configuration information may include LPU configuration information by repeating as many times as the value of the numofLPU field, and may include PU configuration information by repeating as many times as the value of the numofPU field. In other words, LPU configuration information is signaled for each LPU, and PU configuration information is signaled for each PU.

[0398] The above LPU configuration information may include an LPU[i].id field, an LPU[i].origin field, and an LPU[i].size field.

[0399] The LPU[i].id field indicates the identification information (ID) of the ith LPU, the LPU[i].origin field indicates the origin information of the ith LPU, and the LPU[i].size field indicates the size information of the ith LPU. That is, the LPU[].id field indicates the LPU ID specified in the scene configuration of the corresponding slice, the LPU[].origin field indicates the LPU origin information specified in the scene configuration of the corresponding slice, and the LPU[].size field indicates the LPU size information specified in the scene configuration of the corresponding slice.

[0400] The above PU configuration information may include a PU[i].id field, a PU[i].origin field, a PU[i].size field, a PU[i].type field, a PU[i].parentID field, and a PU[i].codingtool field.

[0401] The above PU[i].id field indicates the identification information (ID) of the i-th PU.

[0402] The above PU[i].origin field indicates the origin information of the i-th PU.

[0403] The above PU[i].size field indicates the size information of the i-th PU.

[0404] The above PU[i].type field indicates the type of the ith PU. For example, if the value of the PU[i].type field is 0, the type of the ith PU indicates ground, if the value of the PU[i].type field is 1, the type of the ith PU indicates an object, if the value of the PU[i].type field is 2, the type of the ith PU indicates a dynamic object, and if the value of the PU[i].type field is 3, the type of the ith PU may indicate a static object.

[0405] The above PU[i].parentID field indicates identification information of the parent node of the i-th PU.

[0406] The above PU[i].codingtool field indicates the coding tool of the i-th PU.

[0407] That is, the PU[].id field represents the PU ID specified in the scene configuration of the corresponding slice, the PU[].origin field represents the PU origin specified in the scene configuration of the corresponding slice, the PU[].size field represents the PU size specified in the scene configuration of the corresponding slice, the PU[].type field represents the PU type specified in the scene configuration of the corresponding slice, the PU[].parentID field represents the PU parent ID specified in the scene configuration of the corresponding slice, and the PU[].codingtool field represents the PU coding tool specified in the scene configuration of the corresponding slice.

[0408] Fig. 22 shows a flowchart of a point cloud data transmission method according to embodiments.

[0409] A method for transmitting point cloud data according to embodiments may include a step of encoding point cloud data (S71001) and a step of transmitting a bitstream including the encoded point cloud data (S71002).

[0410] The step of encoding the above point cloud data (S71001) can be divided into a step of encoding geometry and a step of encoding attributes.

[0411] The step (S71001) of encoding the above point cloud data may perform part or all of the operations of the point cloud video encoder (10002) of FIG. 1, the encoding (20001) of FIG. 2, the point cloud video encoder of FIG. 3, the point cloud video encoder of FIG. 8, the geometry encoder and attribute encoder of FIG. 14, and / or the geometry encoder and attribute encoder of FIG. 15.

[0412] The step of encoding the above geometry can construct a scene from point cloud data in units of frames, tiles, or slices based on information indicating whether a scene is constructed (e.g., scene_description_enable_flag), and signal information about the constructed scene to transmit it to the decoder.

[0413] In addition, in order to compose a scene, a ground PU can be generated through a ground threshold in the geometry encoding step, and the remaining area can be configured as an object LPU. For example, in the case of an object LPU, an object PU can be configured through an object threshold while traversing the occupancy tree bottom-up, and if there are adjacent object PUs, these PUs can be grouped to configure an object LPU. As another example, the location (i.e., origin information) / size information of an object identified through image processing can be directly received to configure a ground PU, an object PU, and an object LPU.

[0414] The step of encoding the above geometry can configure a scene with one or more LPUs and one or more PUs configured as above.

[0415] At this time, information indicating whether or not to configure a scene (e.g., scene_description_enable_flag) and scene configuration information can be signaled and transmitted to the decoder of the receiving device. For example, the scene configuration information can include identification information for identifying each LPU and PU (e.g., LPU[].id, PU[].id), location information (e.g., LPU[].origin, PU[i].origin), size information (e.g., LPU[].size, PU[].size), characteristic (type) information (e.g., PU[].type), parent node information (e.g., PU[].parentID), threshold information (e.g., ground_threshold, object_threshold), and applied coding tool information (e.g., PU[].codingtool).

[0416] The step (S71002) of transmitting a bitstream including the encoded point cloud data may be performed in the transmitter (10003) of FIG. 1, the transmission step (20002) of FIG. 2, the transmission processing unit (8012) of FIG. 8, or the transmission processing unit (51005) of FIG. 14.

[0417] The bitstream to be transmitted may further include signaling information. The signaling information may be included in an SPS, GPS, APS, TPS (or tile inventory), a geometry data header (or geometry slice header), and / or an attribute data header (or attribute slice header). According to embodiments, the signaling information may include information indicating whether a scene is composed and scene composition information. That is, signaling information including information indicating whether a scene is composed and scene composition information may be included in at least one of an SPS, a TPS, a GPS, or a geometry slice header and transmitted to a receiving device. A detailed description of the information indicating whether a scene is composed and the scene composition information will be omitted herein with reference to FIGS. 18 to 21.

[0418] Fig. 23 shows a flowchart of a method for receiving point cloud data according to embodiments.

[0419] A method for receiving point cloud data according to embodiments may include a step (S81001) of receiving a bitstream including point cloud data and a step (S81002) of decoding point cloud data.

[0420] The step (S81001) of receiving a bitstream according to embodiments may be performed in the receiver (10005) of FIG. 1, the transmitting unit (20002) or decoding unit (20003) of FIG. 2, the receiving unit (9000) or receiving processing unit (9001) of FIG. 9, or the receiving processing unit (61001) of FIG. 16. The received bitstream may further include signaling information. The signaling information may include information related to reflectivity prediction. For details on the signaling information including the information related to reflectivity prediction, refer to the descriptions of FIGS. 19 to 23, and are omitted here to avoid redundant description.

[0421] The step of decoding the above point cloud data (S81002) can be divided into a step of decoding geometry and a step of decoding attributes. The step of decoding the above point cloud data (S81002) can perform part or all of the operations of the point cloud video decoder (10006) of FIG. 1, the decoding (20003) of FIG. 2, the point cloud video decoder of FIG. 7, the point cloud video decoder of FIG. 9, the geometry decoder and the attribute decoder of FIG. 16, and the geometry decoder and the attribute decoder of FIG. 17.

[0422] The step of decoding the above geometry can restore LPUs / PUs and reconstruct the scene based on signaling information transmitted from the encoder of the transmitting device (e.g., information indicating whether a scene is composed, scene composition information).

[0423] That is, in the step of decoding the geometry, the information indicating whether a scene is composed and the scene composition information included in the signaling information are parsed and restored, and the LPUs / PUs are restored based on the restored information to reconstruct the scene. According to embodiments, the step of decoding the geometry reconstructs the scene by restoring the LPUs / PUs when the information indicating whether a scene is composed indicates a scene composition. In the present disclosure, a detailed description of the information indicating whether a scene is composed and the scene composition information will be omitted here with reference to FIGS. 18 to 21.

[0424] The method for receiving point cloud data according to embodiments may further include a rendering step. The rendering step may restore point cloud data based on decoded geometry and decoded attributes, and render the restored point cloud data according to various rendering methods. For example, points of the point cloud content may be rendered as vertices having a certain thickness, cubes having a certain minimum size centered on the vertex position, or circles centered on the vertex position. All or a portion of the rendered point cloud content is provided to a user through a display (e.g., a VR / AR display, a general display, etc.).

[0425] The step of rendering point cloud data according to embodiments may be performed in the renderer (10007) of FIG. 1, the renderer (20004) of FIG. 2, or the renderer (9011) of FIG. 9. The scene reconstructed in the step of decoding the geometry may be used for rendering in units of LPU / PU through the step of rendering the point cloud data.

[0426] As described above, the present disclosure can efficiently support compression of captured content by combining a scene description or scene graph with a compression unit and efficiently applying coding tools, inter-tools, etc., thereby reducing the bitstream configuration and size. That is, in the case of content used in real time, the present disclosure proposes a method for composing a scene by compressing a point cloud rather than composing a scene description or scene graph used in graphics, thereby enabling more efficient compression of geometry information.

[0427] In particular, the present disclosure proposes a method of representing and / or composing a scene through LPU and PU to efficiently support compression of content, thereby increasing the intra / inter geometry compression efficiency of an encoder / decoder of G-PCC for compressing 3D point cloud data, thereby providing a point cloud content stream.

[0428] Each of the parts, modules, or units described above may be software, processors, or hardware parts that execute sequential execution processes stored in memory (or storage units). Each of the steps described in the embodiments described above may be performed by processors, software, or hardware parts. Each of the modules / blocks / units described in the embodiments described above may operate as a processor, software, or hardware. In addition, the methods presented in the embodiments may be implemented as code. This code may be written on a processor-readable storage medium and thus may be read by a processor provided by an apparatus.

[0429] Furthermore, throughout the specification, when a part is said to "include" a component, this does not exclude other components, unless otherwise specifically stated, but rather implies the inclusion of other components. Furthermore, terms such as "part" described in the specification mean a unit that processes at least one function or operation, which may be implemented using hardware, software, or a combination of hardware and software.

[0430] For convenience of explanation, this specification has been described separately in each drawing. However, it is also possible to design new embodiments by combining the embodiments described in each drawing. Furthermore, designing a computer-readable recording medium containing a program for executing the previously described embodiments, as required by those skilled in the art, is also within the scope of the embodiments.

[0431] The devices and methods according to the embodiments are not limited to the configurations and methods of the embodiments described above, but the embodiments may be configured by selectively combining all or part of each embodiment so that various modifications can be made.

[0432] Although preferred embodiments of the embodiments have been illustrated and described, the embodiments are not limited to the specific embodiments described above, and various modifications may be made by those skilled in the art to which the invention pertains without departing from the spirit or scope of the embodiments claimed in the claims, and such modifications should not be understood individually from the technical idea or prospect of the embodiments.

[0433] The various components of the devices of the embodiments may be implemented by hardware, software, firmware, or a combination thereof. The various components of the embodiments may be implemented by a single chip, for example, a single hardware circuit. The components according to the embodiments may be implemented by separate chips. At least one of the components of the devices of the embodiments may be configured with one or more processors capable of executing one or more programs, and the one or more programs may perform, or include instructions for performing, one or more of the operations / methods according to the embodiments. The executable instructions for performing the methods / operations of the devices of the embodiments may be stored in non-transitory CRMs or other computer program products configured to be executed by one or more processors, or may be stored in temporary CRMs or other computer program products configured to be executed by one or more processors. In addition, the memory according to the embodiments may be used as a concept including not only volatile memory (e.g., RAM, etc.), but also non-volatile memory, flash memory, PROM, etc. Additionally, it may include implementations in the form of carrier waves, such as transmissions via the Internet. Furthermore, processor-readable recording media may be distributed across network-connected computer systems, allowing processor-readable code to be stored and executed in a distributed manner.

[0434] In this document, " / " and "," are interpreted as "and / or". For example, "A / B" is interpreted as "A and / or B", and "A, B" is interpreted as "A and / or B". Additionally, "A / B / C" means "at least one of A, B, and / or C". Also, "A, B, C" means "at least one of A, B, and / or C". Additionally, "or" in this document is interpreted as "and / or". For example, "A or B" can mean 1) "A" only, 2) "B" only, or 3) "A and B". In other words, "or" in this document can mean "additionally or alternatively".

[0435] Various elements of the embodiments may be implemented by hardware, software, firmware, or a combination thereof. Various elements of the embodiments may be implemented on a single chip, such as a hardware circuit. In some embodiments, the embodiments may optionally be implemented on separate chips. In some embodiments, at least one of the elements of the embodiments may be implemented within one or more processors that include instructions for performing operations according to the embodiments.

[0436] Additionally, the operations according to the embodiments described in this document may be performed by a transceiver device including one or more memories and / or one or more processors according to the embodiments. One or more memories may store programs for processing / controlling the operations according to the embodiments, and one or more processors may control various operations described in this document. One or more processors may be referred to as a controller, etc. The operations according to the embodiments may be performed by firmware, software, and / or a combination thereof, and the firmware, software, and / or a combination thereof may be stored in a processor or a memory.

[0437] Terms such as "first," "second," etc. may be used to describe various components of the embodiments. However, the various components according to the embodiments should not be interpreted as limited by these terms. These terms are merely used to distinguish one component from another. For example, a first user input signal may be referred to as a second user input signal. Similarly, a second user input signal may be referred to as a first user input signal. The use of these terms should be interpreted as not departing from the scope of the various embodiments. Although a first user input signal and a second user input signal are both user input signals, they do not mean the same user input signals unless the context clearly indicates otherwise.

[0438] The terminology used to describe the embodiments is for the purpose of describing particular embodiments and is not intended to be limiting of the embodiments. As used in the description of the embodiments and in the claims, the singular is intended to include the plural unless the context clearly dictates otherwise. The expressions “and / or” are used to mean all possible combinations of the terms. The expression “comprises” or “includes” describes the presence of features, numbers, steps, elements, and / or components, but does not mean that additional features, numbers, steps, elements, and / or components are not included. Conditional expressions such as “if” or “when” used to describe the embodiments are not intended to be limited to only optional cases. When a specific condition is satisfied, a related action is performed in response to a specific condition, or a related definition is intended to be interpreted.

[0439] As described above, the relevant contents have been described in the best form for carrying out the embodiments.

[0440] As described above, the embodiments may be applied, in whole or in part, to point cloud data transmission and reception devices and systems. Those skilled in the art may variously modify or alter the embodiments within the scope of the embodiments. The embodiments may include modifications / alterations, and such modifications / alterations do not depart from the scope of the claims and their equivalents.

Claims

1. A step of receiving a bitstream containing point cloud data; and A step of decoding the above point cloud data; comprising; How to decode.

2. In paragraph 1, The step of decoding the above point cloud data is A step of decoding geometry information within the above point cloud data; and A step of decoding attribute information within the above point cloud data is included, The step of decoding the above geometry information comprises a step of dividing the geometry information into a plurality of prediction units based on information related to scene composition, and A decoding method comprising a step of composing a scene based on the above plurality of prediction units.

3. In paragraph 2, The above scene has a minimum k containing the point cloud data2 k A decoding method comprising a bounding box region, wherein each of the plurality of prediction units has origin information and size information.

4. In paragraph 3, The above bitstream contains information related to the scene composition, Information related to the above scene composition includes information indicating whether a scene is composed and scene composition information. A decoding method in which the above scene configuration information includes identification information, origin information, and size information for each of the plurality of prediction units.

5. In paragraph 3, A decoding method wherein the above scene configuration information further includes threshold information for the above scene configuration.

6. Memory; and comprising at least one processor connected to said memory, At least one processor of the above: Receive a bitstream containing point cloud data; and configured to decode the above point cloud data; Decoding device.

7. In the 6th paragraph, the at least one processor A geometry decoder that decodes geometry information within the above point cloud data; and Includes an attribute decoder that decodes attribute information within the above point cloud data, The above geometry decoder is a decoding device that divides the geometry information into a plurality of prediction units based on information related to scene composition, and constructs a scene based on the plurality of prediction units.

8. In paragraph 7, The above scene has a minimum k containing the point cloud data2 k A decoding device comprising a bounding box region, wherein each of the plurality of prediction units has origin information and size information.

9. In paragraph 8, The above bitstream contains information related to the scene composition, Information related to the above scene composition includes information indicating whether a scene is composed and scene composition information. A decoding device wherein the scene configuration information includes identification information, origin information, and size information for each of the plurality of prediction units.

10. In paragraph 8, A decoding device wherein the scene configuration information further includes threshold information for the scene configuration.

11. Step of encoding point cloud data; and A step of transmitting a bitstream including the point cloud data; comprising: Encoding method.

12. In paragraph 11, The step of encoding the above point cloud data is A step of encoding geometry information within the above point cloud data; and A step of encoding attribute information within the above point cloud data is included, The step of encoding the above geometry information includes a step of dividing the geometry information into a plurality of prediction units based on information related to scene composition, and An encoding method comprising a step of composing a scene based on the above plurality of prediction units.

13. In paragraph 12, The above scene has a minimum k containing the point cloud data2 k An encoding method comprising a bounding box region, wherein each of the plurality of prediction units has origin information and size information.

14. In paragraph 13, The above bitstream contains information related to the scene composition, Information related to the above scene composition includes information indicating whether a scene is composed and scene composition information. An encoding method wherein the scene configuration information includes identification information, origin information, and size information for each of the plurality of prediction units.

15. In paragraph 13, An encoding method wherein the above scene configuration information further includes threshold information for the above scene configuration.

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