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

Abstract

Disclosed are a decoding method and device according to embodiments. The decoding method according to embodiments comprises the steps of: decoding geometry data of point cloud data in a bitstream; and decoding attribute data of the point cloud data, wherein the geometry data is decoded on the basis of intra-prediction or inter-prediction, the attribute data is decoded on the basis of intra-prediction or inter-prediction, and, for inter-prediction of the next frame, a frame including the decoded geometry data may be stored in a buffer as location information about a geometry reference frame.

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 point cloud data transmission device, transmission method, point cloud data reception device, and reception method for efficiently transmitting and receiving point clouds in order to solve the problems described above.

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

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

[0007] The technical problem according to the embodiments is to provide a point cloud data transmission device, a transmission method, a point cloud data reception device, and a reception method for efficiently compressing and transmitting and receiving point cloud data captured by LiDAR equipment.

[0008] The technical problem according to the embodiments is to provide a point cloud data transmission device, a transmission method, a point cloud data reception device, and a reception method for efficient inter-prediction compression of point cloud data.

[0009] 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.

[0010] To achieve the above-described purpose and other advantages, a decoding method according to embodiments may include a step of decoding geometry data of point cloud data in a bitstream and a step of decoding attribute data of the point cloud data.

[0011] According to embodiments, the geometry data is decoded based on intra prediction or inter prediction, the attribute data is decoded based on intra prediction or inter prediction, and a frame including the decoded geometry data can be stored in a buffer as position information of a geometry reference frame for inter prediction of the next frame.

[0012] According to embodiments, position information of an attribute reference frame can be reconstructed based on signaled and received buffer-related option information and position information of a geometry reference frame stored in the buffer, and applied to inter prediction of attribute data of the next frame.

[0013] According to embodiments, the position information of the attribute reference frame can be reconstructed by applying one of non-sampling, down-sampling, partial sampling, or adaptive sampling based on the buffer-related option information and the position information of the geometry reference frame.

[0014] According to embodiments, the down sampling may be a method of applying sampling to all groups when reconstructing an attribute reference frame through the geometry reference frame, the partial sampling may be a method of applying sampling to some groups when reconstructing an attribute reference frame through the geometry reference frame, and the adaptive sampling may be a method of applying a different sampling technique to each group when reconstructing an attribute reference frame through the geometry reference frame.

[0015] According to embodiments, the method for reconstructing the position information of the attribute reference frame may vary depending on whether the geometry reference frame is an orthogonal coordinate system or an angular coordinate system.

[0016] According to embodiments, the buffer-related option information may include type information for identifying a method applied to reconstruct position information of the attribute reference frame and identification information for identifying whether the geometry reference frame is an orthogonal coordinate system or an angular coordinate system.

[0017] According to embodiments, a decoding device includes a memory and at least one processor connected to the memory, wherein the at least one processor can be configured to decode geometry data of point cloud data in a bitstream and decode attribute data of the point cloud data.

[0018] According to embodiments, the encoding method may include a step of encoding geometry data of point cloud data and a step of encoding attribute data of the point cloud data.

[0019] According to embodiments, the geometry data is encoded based on intra prediction or inter prediction, the attribute data is encoded based on intra prediction or inter prediction, and a frame including the geometry data restored after the encoding for inter prediction of the next frame can be stored in a buffer as position information of a geometry reference frame.

[0020] According to embodiments, position information of an attribute reference frame can be reconstructed based on position information of a geometry reference frame stored in the buffer and applied to inter prediction of attribute data of the next frame.

[0021] According to embodiments, the position information of the attribute reference frame can be reconstructed by applying one of non-sampling, down-sampling, partial sampling, or adaptive sampling based on the position information of the geometry reference frame.

[0022] According to embodiments, the encoding device includes a memory and at least one processor connected to the memory, wherein the at least one processor can be configured to encode geometry data of point cloud data and encode attribute data of the point cloud data.

[0023] According to embodiments, a computer-readable storage medium can store a bitstream generated by the encoding method described above.

[0024] According to embodiments, a method for transmitting point cloud data includes a step of obtaining a bitstream for point cloud data, wherein the bitstream can be generated based on a step of encoding geometry data of the point cloud data and a step of encoding attribute data of the point cloud data. The method for transmitting point cloud data can further include a step of transmitting data including the bitstream.

[0025] The point cloud data transmission method, transmission device, point cloud data reception method, and reception device according to the embodiments can provide a high-quality point cloud service.

[0026] The point cloud data transmission method, transmission device, point cloud data reception method, and reception device according to the embodiments can achieve various video codec methods.

[0027] The point cloud data transmission method, transmission device, point cloud data reception method, and reception device according to the embodiments can provide general-purpose point cloud content such as autonomous driving services.

[0028] The point cloud data transmission method, transmission device, point cloud data reception method, and reception device according to the embodiments can provide improved parallel processing and scalability by performing spatial adaptive division of point cloud data for independent encoding and decoding of point cloud data.

[0029] A point cloud data transmission method, a transmission device, a point cloud data reception method, and a reception device according to embodiments can improve the encoding and decoding performance of a point cloud by spatially dividing point cloud data into tile and / or slice units to perform encoding and decoding and signaling data required for this purpose.

[0030] According to embodiments, a point cloud data transmission method, a transmission device, a point cloud data reception method, and a reception device can reconstruct the position information of an attribute reference frame by applying one of non-sampling, down-sampling, partial sampling, or adaptive sampling based on the position information of a geometry reference frame stored in a reference integrated frame during encoding / decoding based on inter prediction, thereby minimizing the computational load and minimizing the memory usage. In addition, since the precision of the reference frame can be adjusted, the effect of removing noise is also provided in the case of data with a lot of noise, thereby increasing the compression efficiency for inter prediction of point cloud data.

[0031] The drawings are included to further understand the embodiments, and the drawings illustrate the embodiments together with the description related to the embodiments.

[0032] Figure 1 illustrates an example of a point cloud content provision system according to embodiments.

[0033] FIG. 2 is a block diagram illustrating a point cloud content provision operation according to embodiments.

[0034] FIG. 3 illustrates an example of a point cloud encoder according to embodiments.

[0035] Figure 4 illustrates examples of octree and occupancy codes according to embodiments.

[0036] Figure 5 shows an example of a point configuration by LOD according to embodiments.

[0037] Figure 6 shows an example of a point configuration by LOD according to embodiments.

[0038] Fig. 7 illustrates an example of a point cloud decoder according to embodiments.

[0039] Figure 8 is an example of a transmission device according to embodiments.

[0040] Fig. 9 is an example of a receiving device according to embodiments.

[0041] Fig. 10 shows an example of a structure that can be linked with a point cloud data transmission / reception method / device according to embodiments.

[0042] Figures 11(a) and 11(b) are diagrams showing examples of spinning lidar acquisition models according to embodiments.

[0043] FIG. 12(a) is a drawing showing an example of separately managing a reference frame for geometry information and attribute information according to embodiments.

[0044] FIG. 12(b) is a drawing showing an example of integrating and managing a reference frame into a single buffer for geometry information and attribute information according to embodiments.

[0045] FIG. 13 is a diagram showing an example of a decoder that generates a non-sampling based attribute reference frame according to embodiments.

[0046] FIG. 14 is a diagram showing an example of a decoder that generates a sampling-based attribute reference frame according to embodiments.

[0047] FIG. 15 is a diagram showing an example of performing downsampling in an angular coordinate system according to embodiments.

[0048] FIG. 16 is a diagram showing an example of performing downsampling in an orthogonal coordinate system according to embodiments.

[0049] FIG. 17 is a diagram showing another example of an integrated reference frame management method according to embodiments.

[0050] Fig. 18 is a drawing showing another example of a point cloud transmission device according to embodiments.

[0051] FIG. 19 is a diagram showing an example of the operation of a geometry encoder and an attribute encoder according to embodiments.

[0052] FIG. 20 is a drawing showing another example of a point cloud receiving device according to embodiments.

[0053] FIG. 21 is a diagram showing an example of the operation of a geometry decoder and an attribute decoder according to embodiments.

[0054] Figure 22 shows an example of a bitstream structure of point cloud data for transmission / reception according to embodiments.

[0055] FIG. 23 is a diagram showing an example of a syntax structure of a sequence parameter set according to embodiments.

[0056] FIG. 24 is a diagram showing an example of a syntax structure of an attitude parameter set according to embodiments.

[0057] FIG. 25a and FIG. 25b are diagrams showing an example of a syntax structure of an attribute data unit header according to embodiments.

[0058] Fig. 26 is a flowchart showing an example of a reference frame generation method according to embodiments.

[0059] Fig. 27 is a flowchart showing an example of a point cloud data encoding method according to embodiments.

[0060] Fig. 28 is a flowchart showing an example of a point cloud data decoding method according to embodiments.

[0061] 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.

[0062] 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.

[0063] 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.

[0064] FIG. 1 illustrates an example of a point cloud content provision system according to embodiments.

[0065] 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.

[0066] 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.

[0067] 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.

[0068] 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.

[0069] 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.

[0070] 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.

[0071] 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)).

[0072] 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).

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] FIG. 2 is a block diagram illustrating a point cloud content provision operation according to embodiments.

[0083] 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).

[0084] 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.).

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.).

[0090] 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.

[0091] FIG. 3 illustrates an example of a point cloud encoder according to embodiments.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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).

[0106] The attribute conversion 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.

[0107] As shown in the drawing, the converted attributes are input to the RAHT conversion unit (30008) and / or the LOD generation unit (30009).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] A coefficient quantization unit (30011) according to embodiments quantizes attribute-coded attributes based on coefficients.

[0112] An arithmetic encoder (30012) according to embodiments encodes quantized attributes based on arithmetic coding.

[0113] 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).

[0114] Figure 4 illustrates examples of octree and occupancy codes according to embodiments.

[0115] 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.

[0116] 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.

[0117]

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127]

[0128] 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.

[0129] [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)

[0130] 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).

[0131] Figure 5 shows an example of a point configuration by LOD according to embodiments.

[0132] 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.

[0133] 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.

[0134] Figure 6 shows an example of a point configuration by LOD according to embodiments.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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);}}

[0141] int PCCInverseQuantization(int value, int quantStep) {if( quantStep ==0) {return value;} else {return value * quantStep;}}

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 3) Create temporary arrays called updateweight and update and initialize them to 0.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152]

[0153] 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:

[0154]

[0155] The gDC values ​​are also quantized and entropy coded, like the high-pass coefficients.

[0156] Fig. 7 illustrates an example of a point cloud decoder according to embodiments.

[0157] 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.

[0158] As described in Figure 1, the point cloud decoder can perform geometry decoding and attribute decoding. Geometry decoding is performed before attribute decoding.

[0159] 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).

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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 Hierarchial Transform) decoding, 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.

[0167] An arithmetic decoder (7005) according to embodiments decodes an attribute bitstream using arithmetic coding.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] Figure 8 is an example of a transmission device according to embodiments.

[0173] 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).

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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).

[0181] 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).

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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).

[0187] 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.

[0188] A slice is a series of syntax elements that represent all or part of a coded point cloud frame.

[0189] 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.

[0190] Fig. 9 is an example of a receiving device according to embodiments.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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).

[0195] 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.

[0196] 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).

[0197] 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).

[0198] The inverse quantization processing unit (9005) according to the embodiments can inverse quantize the decoded geometry.

[0199] 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.

[0200] 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.

[0201] 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).

[0202] 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).

[0203] 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.

[0204] Fig. 10 shows an example of a structure that can be linked with a point cloud data transmission / reception method / device according to embodiments.

[0205] 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.

[0206] 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.

[0207] 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).

[0208] 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.

[0209] 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.

[0210] <PCC+XR>

[0211] 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.

[0212] 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.

[0213] <PCC+XR+모바일폰>

[0214] The XR / PCC device (1030) can be implemented as a mobile phone (1040) or the like by applying PCC technology.

[0215] The mobile phone (1040) can decode and display point cloud content based on PCC technology.

[0216] <PCC+자율주행+XR>

[0217] Autonomous vehicles (1020) can be implemented as mobile robots, vehicles, unmanned aerial vehicles, etc. by applying PCC technology and XR technology.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] The PCC method / device according to the embodiments can be applied to a vehicle providing an autonomous driving service.

[0225] Vehicles providing autonomous driving services are connected to PCC devices to enable wired / wireless communication.

[0226] 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.

[0227] As mentioned above, a point cloud is composed of a collection of points, each of which can have geometry information and attribute information. The geometry information is 3D position (XYZ) information, and the attribute information is color (RGB, YUV, etc.) and / or reflectance values.

[0228] The G-PCC encoding process can be comprised of dividing a point cloud into tiles by region, and each tile can be divided into slices for parallel processing. Geometry is compressed for each slice, and attribute information is compressed based on the reconstructed geometry (decoded geometry) based on the positional information changed through compression.

[0229] The G-PCC decoding process can be composed of a process of receiving an encoded slice unit geometry bitstream and attribute bitstream, decoding the geometry, and decoding attribute information based on the geometry reconstructed through the decoding process.

[0230] At this time, compression techniques based on octree, predictive tree, or trisoup can be used to compress geometry information. Furthermore, compression techniques based on predictive transform, lifting transform, or RAHT transform can be used to compress attribute information.

[0231] Meanwhile, as described above, the point cloud content provision system may use one or more cameras (e.g., an infrared camera capable of acquiring depth information, an RGB camera capable of extracting color information corresponding to depth information, etc.), a projector (e.g., an infrared pattern projector for acquiring depth information, etc.), LiDAR, etc., to generate point cloud content (or point cloud data).

[0232] LiDAR is a device that measures distance by measuring the time it takes for irradiated light to reflect off a subject and return, providing precise 3D information about 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. In other words, 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.

[0233] These lidars have different elevations θ(i)i=1,…,N It consists of N lasers (N=16, 32, 64, etc.) located in the azimuth of the Z-axis. Point cloud data can be captured as shown in Fig. 11(a) and / or Fig. 11(b) by spinning along φ. This type is called a spinning LiDAR model, and point cloud content captured and generated using a spinning LiDAR model has angular characteristics.

[0234] Figures 11(a) and 11(b) are diagrams showing examples of spinning lidar acquisition models according to embodiments.

[0235] Referring to FIG. 11(a) and FIG. 11(b), when the laser i hits the object M, the position of M can be estimated as (x, y, z) on the orthogonal coordinate system. At this time, due to the fixed position of the laser sensors, the characteristic of moving straight, and the characteristic of the sensors rotating at a certain azimuth, the position of the object M can have a characteristic that the rules between points can be derived in a way that is advantageous for compression when expressed as (r, φ, i) rather than (x, y, z) on the orthogonal coordinate system.

[0236] Therefore, by utilizing these characteristics, the compression efficiency can be improved by applying the angular mode during the geometry encoding / decoding process for data captured by spinning lidar equipment. The angular mode is a method of compressing with (r, φ, i) instead of (x, y, z). Here, r is the radius, φ is the azimuth (or azimuthal angle), and i represents the ith laser of the lidar (e.g., laser index). In other words, the frames of the point cloud content generated through the lidar equipment are not combined, but are composed of each frame, and each origin can be 0,0,0, so the angular mode can be used by changing it to an angular (or spherical) coordinate system.

[0237] Meanwhile, the point cloud data acquired in this way includes geometric information and attribute information.

[0238] At this time, the encoder of the transmitting device encodes / decodes geometry information, reconstructs geometry position information, and compresses attribute information based on that position. At this time, attribute information can optionally use either an orthogonal coordinate system or an angular mode. Generally, the orthogonal coordinate system is primarily used.

[0239] Additionally, geometry information and attribute information can be encoded (i.e., compressed) based on intra prediction, or encoded (i.e., compressed) based on inter prediction using a reference frame. In the case of inter prediction, a process of storing the reference frame in a buffer is required.

[0240] However, when storing reference frames in a buffer for inter-prediction, if the coordinate systems of geometry and attribute information are different, information from two different coordinate systems may be stored for a single reference frame. Storing only one of the two increases the computational load and complexity because the decoder must perform additional coordinate transformations. Furthermore, if some points are missing in the geometry reference frame, the attribute reference frame may not be fully constructed, which may result in inaccurate attribute information prediction. Furthermore, storing both geometry and attribute reference frames increases memory usage, which can burden the decoder. In particular, when bi-prediction is applied, more computation and memory usage may be required. Furthermore, global / local motion may be applied only to geometry or only to attributes. This can also increase the number of reference frames.

[0241] This disclosure proposes an efficient reference frame management method for applying inter-prediction compression techniques to point clouds captured by a spinning lidar and comprising multiple frames. Specifically, this disclosure proposes a method that minimizes computational load, minimizes memory usage, and maximizes accuracy.

[0242] That is, the present disclosure proposes a reference frame management method and a signaling method for inter prediction-based compression of G-PCC for point cloud frames.

[0243] The following describes a reference frame management method for efficiently applying compression through inter prediction of point cloud content captured by lidar equipment.

[0244] Inter-prediction, a technique that leverages inter-frame similarity to improve compression efficiency, is a widely used technique in 2D image compression. When compressing the current frame from a series of highly similar consecutive frames, rather than directly compressing the current frame's attribute values, the residual value, which is the difference between the attribute values ​​in the reference frame, can be compressed to achieve higher compression efficiency. This is because, due to the nature of entropy coding, values ​​closer to 0 have a higher probability of existence, allowing them to be expressed with fewer bits. This inter-prediction technique is also utilized in the compression of multi-frame point clouds, allowing both geometry and attribute information to be compressed using this inter-prediction technique.

[0245] However, unlike 2D images, when performing inter-prediction-based compression on 3D point clouds, two types of reference frames exist for a single point cloud frame: a geometry reference frame and an attribute reference frame. This is because the positional information of the geometry reference frame and the attribute reference frame exist in different coordinate systems or different scaling is applied to them.

[0246] At this time, both frames can be stored in the buffer by managing them separately as shown in Fig. 12(a), but this is an inappropriate buffer management method if the decoder's local memory is insufficient. That is, Fig. 12(a) is a drawing showing an example of separately managing the reference frame for geometry information and attribute information according to embodiments. In this case, one buffer stores the position information ((x1, y1, z1), (x2, y2, z2), (x3, y3, z3), …) of the geometry reference frame, and the other buffer stores the position information ((x1', y1', z1'), (x2', y2', z2'), (x3', y3', z3'), …) of the attribute reference frame and the attribute information ((r1, g1, b1), (r2, g2, b2), (r3, g3, b3), …). Here, the position information ((x1', y1', z1'), (x2', y2', z2'), (x3', y3', z3'), …) of the attribute reference frame is stored as attribute information ((r1, g1, b1), (r2, g2, b2), (r3, g3, b3), … is the location information corresponding to.

[0247] FIG. 12(b) is a drawing showing an example of integrating and managing a reference frame into a single buffer for geometry information and attribute information according to embodiments.

[0248] That is, in Fig. 12(b), all position information of the geometry frame ((x1, y1, z1), (x2, y2, z2), (x3, y3, z3), …) is stored, and in the case of attributes, only attribute information (e.g., color values ​​((r1, g1, b1), (r2, g2, b2), (r3, g3, b3), …), reflection values, etc.) excluding the position information of the attribute frame are stored in the buffer. In other words, the method of Fig. 12(b) that integrates the geometry reference frame and the attribute reference frame into one reference frame and manages them as one buffer is more efficient than the method of Fig. 12(a) that manages them separately.

[0249] However, in the case of Fig. 12(b), the position information of the attribute reference frame must be reconstructed by utilizing the position information of the geometry reference frame stored in the integrated reference frame buffer in the decoder. At this time, since the position information of the geometry reference frame and the position information of the attribute reference frame are different, when the position information of the attribute reference frame is reconstructed by utilizing the position information of the geometry reference frame, the position information of the attribute reference frame is lost.

[0250] The present disclosure proposes various methods for reducing loss in the position information of an attribute reference frame when reconstructing the position information of an attribute reference frame using the position information of a geometry reference frame, as follows.

[0251] FIG. 13 is a diagram showing an example of a decoder that generates a non-sampling based attribute reference frame according to embodiments.

[0252] When only the position information of the geometry reference frame is stored in the buffer (40011), when the decoder reconstructs the attribute reference frame through the geometry reference frame, it may be efficient to use an unsampled reference frame as in Fig. 13.

[0253] Accordingly, the present disclosure can generate a separate attribute reference frame during the decoding process, then scale all position information of the reference frame stored in the buffer (40011) and store the scaled position information in the attribute reference frame, and attribute information (reflection values, etc.) can be stored without separate conversion. After the attribute reference frame is completely generated, the geometry reference frame can be transformed through sampling.

[0254] That is, the reference frame buffer (40011) stores the position information of the geometry reference frame and the attribute information of the attribute reference frame. In other words, the reference frame buffer (40011) does not store the position information of the attribute reference frame. Therefore, in order to perform inter prediction-based decoding on the attribute information in the attribute inter prediction decoding unit (40014), a process of reconstructing the position information of the attribute reference frame based on the position information of the geometry reference frame stored in the reference frame buffer (40011) is required.

[0255] The present disclosure reconstructs position information of an attribute reference frame without performing sampling on position information of a geometry reference frame read from a reference frame buffer (40011). That is, the position information of the attribute reference frame is reconstructed based on the position information of an unsampled geometry reference frame. In the present disclosure, the reconstructing of the position information of the attribute reference frame may be performed before or after scaling in a scaling unit (40012). In one embodiment of the present disclosure, the reconstructing of the position information of the attribute reference frame is performed after scaling.

[0256] And, the attribute inter prediction decoding unit (40014) performs inter prediction on the attribute information based on the position information of the reconstructed attribute reference frame to restore the attribute information.

[0257] In contrast, the geometry inter prediction decoding unit (40016) performs inter prediction on the geometry information based on the position information of the geometry reference frame sampled in the sampling unit (40013) to restore the geometry information. At this time, the position information of the geometry reference frame sampled in the sampling unit (40013) may be provided as is to the geometry inter prediction decoding unit (40016), or may be provided to the geometry inter prediction decoding unit (40016) after the global motion is applied in the global motion application unit (40015).

[0258] In this disclosure, sampling refers to the process of selecting one point for every multiple points. For example, if one point is selected for every three points, the number of positional information in the geometric reference frame is reduced by the number of unselected points.

[0259] According to embodiments, the position information of the geometry information of the current frame restored by the geometry inter prediction decoding unit (40016) is stored in the reference frame buffer (40011) for inter prediction of the next frame. Then, the position information of the geometry information of the current frame restored by the geometry inter prediction decoding unit (40016) is scaled by the scaling unit (40017) and then provided to the attribute inter prediction decoding unit (40014).

[0260] Additionally, attribute information of the current frame restored in the attribute inter prediction decoding unit (40014) is also stored in the reference frame buffer (40011) for inter prediction of the next frame.

[0261] That is, the integrated reference frame generation unit (40018) generates an integrated reference frame using the position information of the geometry information of the current frame output from the geometry inter prediction decoding unit (40016) and the attribute information output from the attribute inter prediction decoding unit (40014), and stores it in the reference frame buffer (40011).

[0262] In this way, in the present disclosure, the position information of the attribute reference frame required for attribute inter prediction is reconstructed based on the position information of the unsampled geometry reference frame and then used for attribute inter prediction. In other words, the attribute inter prediction decoding unit (40014) performs inter prediction based on the position information of the unsampled (i.e., non-sampled) attribute reference frame.

[0263] Meanwhile, the present disclosure can generate position information of an attribute reference frame by applying one of downsampling, partial sampling, or adaptive sampling based on position information of a geometry reference frame stored in a reference frame buffer.

[0264] The following describes the process of generating an attribute reference frame through down-sampling in the sampling unit (40021). In this case, the sampling type information (attr_sampling_type) indicates down-sampling and may have a value of 0, for example.

[0265] FIG. 14 is a diagram showing an example of a decoder that generates a sampling-based attribute reference frame according to embodiments.

[0266] If only the position information of the geometry reference frame is stored in the buffer without storing the position information of the attribute reference frame, when the decoder reconstructs the attribute reference frame through the geometry reference frame, it may be efficient to use a down-sampling / partial sampling / adaptive sampling reference frame as in Fig. 14.

[0267] That is, the reference frame buffer (40011) stores the position information of the geometry reference frame and the attribute information of the attribute reference frame. In other words, the reference frame buffer (40011) does not store the position information of the attribute reference frame. Therefore, in order to perform inter prediction-based decoding on the attribute information in the attribute inter prediction decoding unit (40014), a process of reconstructing the position information of the attribute reference frame based on the position information of the geometry reference frame stored in the reference frame buffer (40011) is required.

[0268] The present disclosure generates position information of an attribute reference frame based on position information of a geometry reference frame read from a reference frame buffer (40011), and performs down-sampling on the position information of the generated attribute reference frame so that it can be used for attribute inter prediction. In one embodiment of the present disclosure, the generation and down-sampling of position information of the attribute reference frame are performed in a sampling unit (40021).

[0269] And, the attribute inter prediction decoding unit (40014) performs inter prediction on the attribute information based on the position information of the attribute reference frame down-sampled in the sampling unit (40021) and scaled in the scaling unit (40012) and / or the position information of the geometry reference frame scaled in the scaling unit (40017) to restore the attribute information.

[0270] Meanwhile, the geometry inter prediction decoding unit (40016) performs inter prediction on the geometry information based on the position information of the geometry reference frame sampled in the sampling unit (40013) to restore the geometry information. At this time, the position information of the geometry reference frame sampled in the sampling unit (40013) may be provided as is to the geometry inter prediction decoding unit (40016), or may be provided to the geometry inter prediction decoding unit (40016) after the global motion is applied in the global motion application unit (40015).

[0271] According to embodiments, the position information of the geometry information of the current frame restored by the geometry inter prediction decoding unit (40016) is stored in the reference frame buffer (40011) for inter prediction of the next frame. Then, the position information of the geometry information of the current frame restored by the geometry inter prediction decoding unit (40016) may be provided to the attribute inter prediction decoding unit (40014) after scaling by the scaling unit (40017).

[0272] Additionally, attribute information of the current frame restored in the attribute inter prediction decoding unit (40014) is also stored in the reference frame buffer (40011) for inter prediction of the next frame.

[0273] That is, the integrated reference frame generation unit (40018) generates an integrated reference frame using the position information of the geometry information of the current frame output from the geometry inter prediction decoding unit (40016) and the attribute information output from the attribute inter prediction decoding unit (40014), and stores it in the reference frame buffer (40011).

[0274] In this way, in the present disclosure, the position information of the attribute reference frame required for attribute inter prediction is reconstructed and downsampled based on the position information of the geometry reference frame, and then used for attribute inter prediction.

[0275] A method for downsampling an attribute reference frame based on positional information of a geometry reference frame in the sampling unit (40021) of the present disclosure may be as follows. It is assumed that the attribute reference frame uses an angular coordinate system.

[0276] In the present disclosure, when the geometry reference frame is a spherical coordinate system as shown in FIG. 15, the sampling unit (40021) can store only attr_N points from the first point of each group, or only attr_N points in order of the smallest value in the azimuth range of each group, or only attr_N points from the last point of each group, or only attr_N points in order of the largest value in the azimuth range of each group. Here, storage does not mean storing in the reference frame buffer, but temporarily storing in a memory such as RAM for use in inter prediction during encoding / decoding. This also applies to partial sampling and adaptive sampling, which will be described later. That is, the position information of the attribute reference frame generated by applying one of non-sampling, down-sampling, partial sampling, or adaptive sampling is not stored in the reference frame buffer.

[0277] FIG. 15 is a diagram illustrating an example of performing downsampling in an angular coordinate system according to embodiments. In particular, FIG. 15 is an example when attr_N is 1. That is, the present disclosure selects and stores only one point from each group through sampling. For example, when attr_N is 1, the point selected through sampling may be the first point in the group, the smallest value in the azimuth range, the last point, or the largest value in the azimuth range.

[0278] In the present disclosure, when the geometry reference frame is a Cartesian coordinate system as shown in FIG. 16, the sampling unit (40021) can reconstruct the attribute reference frame by designating an octree-specific depth level as attr_octree_node_level, and if the leaf nodes have the same parents, storing attr_N nodes starting from the node with the earliest octree index and then converting them back to the angular coordinate system. As another example, the sampling unit (40021) can reconstruct the attribute reference frame by designating an octree-specific depth level as attr_octree_node_level, and if the leaf nodes have the same parents, storing attr_N nodes starting from the node with the latest octree index and then converting them back to the angular coordinate system.

[0279] Fig. 16 is a diagram showing an example of performing down-sampling in an orthogonal coordinate system according to embodiments. In particular, Fig. 16 is an example when attr_N is 1. That is, the present disclosure selects and stores only one point through sampling from each group. For example, when attr_N is 1, the point selected through sampling may be the node with the earliest octree index or the node with the latest octree index among leaf nodes with the same parent. That is, among leaf nodes with the same parent, N nodes may be selected from the node with the earliest octree index or N nodes may be selected from the node with the latest octree index. The present disclosure may refer to nodes with the same parent as one group. That is, if leaf nodes have the same parent, they may be defined as nodes belonging to the same group.

[0280] The following describes the process of generating an attribute reference frame through partial sampling in the sampling unit (40021). In this case, the sampling type information (attr_sampling_type) indicates partial sampling and may have a value of 1, for example.

[0281] When storing only the position information of the geometry reference frame in the buffer, it may be efficient to use a partially sampled reference frame that applies sampling only to some groups when reconstructing the attribute reference frame through the geometry reference frame in the decoder.

[0282] According to embodiments, a method for partially sampling an attribute reference frame based on positional information of a geometry reference frame in a sampling unit (40021) may be as follows. It is assumed that the attribute reference frame uses an angular coordinate system.

[0283] First, here is an explanation for the case where the geometry reference frame is an angular coordinate system.

[0284] In one embodiment, whether sampling is performed for all groups is indicated through attr_sampling_flag and stored in an array, and only attr_N from the first point are stored for groups for which attr_sampling_flag is true.

[0285] In another embodiment, whether sampling is performed for all groups is indicated and stored in an array via attr_sampling_flag, and for groups for which attr_sampling_flag is true, only attr_N values ​​are stored in the order of the smallest values ​​in the azimuth range.

[0286] In another embodiment, whether sampling is performed for all groups is indicated through attr_sampling_flag and stored in an array, and only attr_N from the last point are stored for groups for which attr_sampling_flag is true.

[0287] In another embodiment, whether sampling is performed for all groups is indicated and stored in an array via attr_sampling_flag, and for groups where attr_sampling_flag is true, only points up to attr_N in the order of the largest value in the azimuth range are stored.

[0288] The following is an explanation for the case where the geometry reference frame is an orthogonal coordinate system.

[0289] In this disclosure, an octree-specific depth level is designated as attr_octree_node_level, and if leaf nodes have the same parent, they are assumed to be in the same group. Whether sampling is performed for each group is indicated and stored in an array through attr_sampling_flag. In addition, the following sampling method can be performed for groups for which attr_sampling_flag is true.

[0290] In one embodiment, the attr_N octree indices within the group are stored starting from the fastest node, and then converted back to the angular coordinate system to reconstruct the attribute reference frame.

[0291] In another embodiment, the attr_N octree indices in the group are stored starting from the latest node and then converted back to the angular coordinate system to reconstruct the attribute reference frame.

[0292] The following describes the process of generating an attribute reference frame through adaptive sampling in the sampling unit (40021). In this case, the sampling type information (attr_sampling_type) indicates adaptive sampling and may have a value of 2, for example.

[0293] When only the position information of the geometry reference frame is stored in the buffer, it may be efficient to use an adaptive-sampled reference frame that applies a different sampling technique to each group when the decoder reconstructs the attribute reference frame through the geometry reference frame. That is, when applying adaptive sampling, the number of sampled points may be different for each group to which sampling is applied. In other words, in the case of downsampling or partial sampling, the number of sampled points is the same for each group to which sampling is applied.

[0294] According to embodiments, an adaptive sampling method of an attribute reference frame based on positional information of a geometry reference frame may be as follows. It is assumed that the attribute reference frame uses an angular coordinate system.

[0295] First, here is an explanation for the case where the geometry reference frame is an angular coordinate system.

[0296] According to embodiments, when the adaptive sampling application flag is true and / or the sampling type information indicates adaptive sampling, up to N points are stored for each group, and the points are stored sequentially in an array. If N is -1, it is assumed that all points in the group have been stored (sampling has not been performed). The criteria for storing up to N points may be as follows.

[0297] In one example, only the first N points for each group are stored.

[0298] In another embodiment, only up to N points are stored in order of the smallest value in the azimuth range for each group.

[0299] In another embodiment, only N points from the last point of each group are stored.

[0300] In another embodiment, only up to N points are stored in order of the largest value in the azimuth range for each group.

[0301] The following is an explanation for the case where the geometry reference frame is an orthogonal coordinate system.

[0302] According to embodiments, when the adaptive sampling application flag is true and / or the sampling type information indicates adaptive sampling, up to N points are stored for each group, and the points are stored sequentially in an array. If N is -1, it is assumed that all points in the group have been stored (sampling has not been performed). The criteria for storing up to N points may be as follows.

[0303] In one embodiment, the attribute reference frame is reconstructed by storing N nodes with the fastest octree index within a group of octree nodes with the same parent node and then converting them back to an angular coordinate system.

[0304] In another embodiment, the attribute reference frame is reconstructed by storing N nodes starting from the latest octree index in a group of octree nodes with the same parent node and then converting them back to an angular coordinate system.

[0305] The following describes how to manage reference frames based on indexing.

[0306] According to embodiments, managing reference frames based on indexing can be efficient to minimize computational load and memory usage. When managing reference frames based on indexing according to embodiments, as illustrated in FIG. 17, the location information of a sampled reference frame can be expressed as a single piece of information, called an index, rather than three pieces of information in a three-dimensional space, thereby enabling efficient local memory management.

[0307] Fig. 18 is a drawing showing another example of a point cloud transmission device according to embodiments.

[0308] The elements of the point cloud transmission device illustrated in FIG. 18 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. 18 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 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. 18 described above. In addition, 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 transmission device of FIG. 18. The execution order of each block in FIG. 18 may be changed, some blocks may be omitted, and some blocks may be newly added.

[0309] The point cloud transmission device of Fig. 18 can correspond to the transmission device (10000), point cloud video encoder (10002), transmitter (10003) of Fig. 1, acquisition-encoding-transmission (20000-20001-20002) of Fig. 2, point cloud video encoder of Fig. 3, transmission device of Fig. 8, device of Fig. 10, etc.

[0310] 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.

[0311] 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. The data input unit (51001) may receive data to encode point cloud data. That is, the data input unit (51001) may read and set the input data (e.g., ply, configuration file, etc.). For example, the input data may be geometry data (which may be referred to as geometry, geometry information, etc.), attribute data (which may be referred to as attribute, attribute information, etc.), parameter information indicating coding-related settings, etc.

[0312] The above coordinate system conversion unit (51002) can support coordinate system conversion of point cloud data, such as changing the xyz axes or converting the coordinate system from the xyz rectangular coordinate system to the angular (or spherical) coordinate system.

[0313] The above quantization processing unit (or geometry information transformation 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 according to 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.

[0314] 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 one embodiment, signaling information for spatial division is entropy-encoded by the signaling processing unit (51005) and then transmitted in bitstream form through the transmission processing unit (51008).

[0315] In one embodiment, point cloud content can be a person or multiple people, or an object or multiple objects, such as an actor, but on a larger scale, it can also be a map for autonomous driving or a map for indoor navigation of a robot. Furthermore, point cloud content can be point cloud data captured by a lidar device from a moving or stationary vehicle. In such cases, point cloud content can be a large amount of locally connected data. Therefore, since point cloud content cannot be encoded / decoded all at once, tile partitioning can be performed before compression. For example, room 101 in a building can be divided into one tile, and room 102 into another tile. The divided tiles can be further partitioned (or divided) into slices to support fast encoding / decoding through parallelization. This is referred to as slice partitioning (or division).

[0316] 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.

[0317] 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.

[0318] 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.

[0319] 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.

[0320] The above geometry encoder (51006) performs inter-prediction or intra-prediction-based encoding on positions output from the spatial segmentation unit (51004) to output a geometry bitstream. At this time, the geometry encoder (51006) can apply an LPU (largest prediction unit) / PU (prediction unit) segmentation method (e.g., a cuboid segmentation method) to a frame, tile, or slice to segment them into LPUs and / or PUs. That is, the geometry encoder (51006) can segment point cloud data into LPUs and / or PUs, which are prediction units, by reflecting the characteristics of the content in order to apply a compression technique based on inter-prediction through a reference frame to point cloud data captured by a lidar and having multiple frames.

[0321] The above geometry encoder (51006) may or may not apply a motion vector to each partitioned region (i.e., LPU or PU) for motion compensation. Furthermore, whether a motion vector is applied to each partitioned region may be signaled. Here, the motion vector may be a global motion vector or a local motion vector.

[0322] In addition, the geometry encoder (51006) can reconstruct the encoded geometry information and output it to the attribute encoder (51007). At this time, the reconstructed geometry information is stored in the reference frame buffer as position information of the geometry reference frame. The attribute encoder (51007) encodes (i.e., compresses) the attributes (e.g., the 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.

[0323] According to embodiments, when position information of a geometry reference frame is stored in a reference frame buffer but position information of an attribute reference frame is not stored in the reference frame buffer, when inter prediction is performed in the attribute encoder (51007), the attribute encoder (51007) can reconstruct position information of an attribute reference frame based on position information of a geometry reference frame stored in a reference frame buffer, and perform inter prediction based on position information of the reconstructed attribute reference frame to compress the attribute information. At this time, the position information of the attribute reference frame can be generated by applying at least one of a non-sampling, down-sampling, partial sampling, or adaptive sampling method. In addition, since a description of generating (i.e., reconstructing) an attribute reference frame by applying at least one of a non-sampling, down-sampling, partial sampling, or adaptive sampling method when reconstructing an attribute reference frame based on position information of a geometry reference frame has been described above, it will be omitted here.

[0324] According to embodiments, buffer-related option information used for inter prediction in the geometry encoder (51006) and / or the attribute encoder (51007) may be signaled and transmitted to a receiving device. A detailed description of the buffer-related option information will be described with reference to FIGS. 22 to 25. In the present disclosure, signaling of the buffer-related option information may be performed in at least one of the signaling processing unit (51005), the geometry encoder (51006), or the attribute encoder (51007).

[0325] Additionally, the attribute encoder (51007) can entropy code the attribute residual value, which is the difference between the predicted attribute information and the current attribute information.

[0326] FIG. 19 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. 19 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. 19 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. 19 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. 19. The execution order of each block in FIG. 19 may be changed, some blocks may be omitted, and some blocks may be newly added.

[0327] 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. 18 may or may not be omitted.

[0328] 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.

[0329] 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.

[0330] 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.

[0331] The geometry information intra prediction unit (53003) according to the embodiments may apply geometry intra prediction coding to the geometry information of the I-frame if the frame of the input point cloud data (i.e., the frame to which the input points belong) is an I-frame. The intra prediction coding method may include octree coding, predictive tree coding, trisoup coding, etc.

[0332] To this end, the drawing code 53002 (or determination unit) checks whether the points output from the voxelization processing unit (53001) are points belonging to the I frame or points belonging to the P frame.

[0333] According to the embodiments, the LPU / PU splitting unit (53004) may split the points split into tiles or slices in the space splitting unit (51004) again into LPUs / PUs to support inter-prediction if the frame identified by the determination unit (53002) is a P frame. In another embodiment, the LPU / PU splitting unit (53004) may split the points included in the frame into LPUs / PUs to support inter-prediction if the frame identified by the determination unit (53002) is a P frame.

[0334] Although the present disclosure is a P frame, if the change rate is greater than a certain threshold compared to the previous reference frame, intra-prediction coding can be performed on the P frame like an I frame. For example, if the change of the entire frame is large and goes beyond a certain threshold range, intra-prediction coding can be performed on the P frame instead of inter-prediction coding. This is because intra-prediction coding can be more accurate and efficient than inter-prediction coding when the change rate is large. Here, the previous reference frame, i.e., the geometry reference frame, is provided from the reference frame buffer (53009) or the reference frame generation unit (53010).

[0335] To this end, drawing code 53005 (or the discriminator) checks whether the rate of change is greater than a threshold value.

[0336] If the determination unit (53005) determines that the change rate between the P frame and the reference frame is greater than the threshold value, the P frame is output to the geometry information intra prediction unit (53003) to perform intra prediction. In addition, if the determination unit (53005) determines that the change rate is not greater than the threshold value, the P frame divided into LPU and / or PU is output to the motion compensation application unit (53006) to perform inter prediction.

[0337] According to embodiments, a motion compensation application unit (53006) determines whether to apply a motion vector for each divided LPU / PU and signals the result. For example, it may check the RDO of a specific PU to determine whether to apply a motion vector to the corresponding PU. If applying a motion vector to the corresponding PU provides a better gain, in one embodiment, the motion vector is applied to the PU. If applying a motion vector to the corresponding PU does not provide a better gain, in one embodiment, the motion vector is not applied to the PU. Here, the gain can be determined by comparing the bitstream size when the motion vector is applied. At this time, the motion vector applied to the PU may be a global motion vector obtained through full motion estimation between frames, a local motion vector obtained in the corresponding PU, or both a global motion vector and a local motion vector.

[0338] That is, the LPU / PU splitting unit (53004) splits the points split into slices into LPUs / PUs to support inter-prediction when the frame is a P frame, and the motion compensation application unit (53006) can find and assign a motion vector corresponding to the split area. In other words, the motion compensation application unit (53006) can apply a motion vector to the split LPU / PU to generate a predicted point cloud.

[0339] The geometry information inter prediction unit (53007) according to the embodiments may perform octree-based inter-coding, predictive-tree-based inter-coding, or trisoup-based inter-coding based on the difference in geometry prediction values ​​between the current frame and a motion-compensated reference frame or a previous frame that has not been motion-compensated.

[0340] The geometry information intra prediction unit (53003) according to the embodiments may apply geometry intra prediction coding to the geometry information of the P frame input through the judgment unit (53005). The intra prediction coding method may include octree coding, predictive tree coding, trisoup coding, etc.

[0341] The geometry information entropy encoding unit (53008) according to the embodiments performs entropy encoding on geometry information coded based on intra prediction in the geometry information intra prediction unit (53003) or geometry information coded based on inter prediction in the geometry information inter prediction unit (53007) to output a geometry bitstream (or referred to as a geometry information bitstream).

[0342] A geometry restoration unit (53011) according to embodiments restores (or reconstructs) geometry information based on positions changed through intra-prediction-based coding or inter-prediction-based coding, and outputs the restored geometry information (or so-called restored geometry) to an attribute encoder (51007). That is, since attribute information is dependent on geometry information (position), restored (or reconstructed) geometry information is required to compress attribute information.

[0343] In addition, the restored geometry information is stored in the reference frame buffer (53009) to be provided as a reference frame during inter prediction coding of the P frame. That is, the stored geometry information becomes position information of the geometry reference frame during inter prediction coding of the P frame. The reference frame buffer (53009) also stores attribute information restored by the attribute encoder (51007). That is, the restored geometry information and the restored attribute information stored in the reference frame buffer (53009) can be used as previous reference frames for geometry information inter prediction coding and attribute information inter prediction coding in the geometry information inter prediction unit (53007) of the geometry encoder (51006) and the attribute information inter prediction unit (55005) of the attribute encoder (51007).

[0344] At this time, the reference frame buffer (53009) may separately manage the geometry reference frame and the attribute reference frame as in Fig. 12(a), or may manage them as an integrated reference frame as in Fig. 12(a). In the case of Fig. 12(a), both the position information of the geometry reference frame and the position information of the attribute reference frame are stored in the reference frame buffer (53009). At this time, the attribute information corresponding to the position information of the attribute reference frame is also stored in the reference frame buffer (53009). In contrast, in the case of Fig. 12(b), only the position information and attribute information of the geometry reference frame are stored in the reference frame buffer (53009), and the position information of the attribute reference frame is not stored in the reference frame buffer (53009). In this case, the reference frame generation unit (53010) reconstructs the position information of the attribute reference frame based on the position information of the geometry reference frame stored in the reference frame buffer (53009). That is, the reference frame generation unit (53010) may transfer the position information of the geometry reference frame stored in the reference frame buffer (53009), the position information of the attribute reference frame, and the attribute information to a required module, or may transfer the position information of the attribute reference frame reconstructed based on the geometry reference frame and the attribute information stored in the reference frame buffer (53009), and one of non-sampling, down-sampling, partial sampling, or adaptive sampling to a required module.

[0345] That is, in the present disclosure, when only the position information of the geometry reference frame is stored in the reference frame buffer (53009), the reference frame generation unit (53010) may reconstruct the attribute reference frame by applying at least one of a non-sampling, down-sampling, partial sampling, or adaptive sampling method when reconstructing the attribute reference frame based on the position information of the geometry reference frame. In one embodiment, the reference frame generation unit (53010) may generate the attribute reference frame by applying a non-sampling reference frame generation method based on the position information of the geometry reference frame. In another embodiment, the reference frame generation unit (53010) may generate the attribute reference frame by applying a down-sampling reference frame generation method based on the position information of the geometry reference frame. In yet another embodiment, the reference frame generation unit (53010) may generate the attribute reference frame by applying a partial sampling reference frame generation method based on the position information of the geometry reference frame. In another embodiment, the reference frame generation unit (53010) may generate an attribute reference frame by applying an adaptive sampling reference frame generation method based on the positional information of the geometry reference frame. Furthermore, the reference frame generation unit (53010) may utilize an indexing-based reference frame management method. A more detailed explanation has already been provided above, so it will be omitted here.

[0346] The color conversion processing unit (55001) of the attribute encoder (51007) corresponds to the color conversion unit (40006) of FIG. 3 or the color conversion processing unit (12008) 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 (51001) and / or the space division unit (51004). 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.

[0347] According to embodiments, the attribute encoder (51007) may perform color rescaling depending on whether lossy coding has been applied to the geometry information. To this end, the reference numeral 55002 (also referred to as a determination unit) determines whether lossy coding has been applied to the geometry information in the geometry encoder (51006).

[0348] For example, if the determination unit (55002) determines that Losi coding has been applied to the geometry information, the color readjustment unit (55003) performs color readjustment (or recoloring) to reset the attribute (color) due to the lost point. That is, the color readjustment unit (55003) can find and set an attribute value appropriate for the location of the lost point from the original point cloud data. In other words, if a scale is applied to the geometry information and the location information value is changed, the color readjustment unit (55003) can predict an attribute value appropriate for the changed location.

[0349] According to embodiments, the operation of the color re-adjustment unit (55003) may be optionally applied depending on whether duplicated points are merged. In one embodiment, whether or not the duplicated points are merged is performed in the voxelization processing unit (53001) of the geometry encoder (51006).

[0350] The present disclosure, as an example, performs color readjustment (i.e., recoloring) in the color readjustment unit (55003) when points belonging to one voxel are merged into one point in the voxelization processing unit (53001).

[0351] The above color readjustment unit (55003) performs operations and / or methods identical to or similar to those of the attribute conversion unit (40007) of FIG. 3 or the attribute conversion processing unit (12009) of FIG. 8.

[0352] If it is determined in the above determination unit (55002) that no Losi coding is applied to the geometry information, it is determined in the drawing symbol 55004 (or determination unit) whether encoding based on inter prediction is applied to the attribute information.

[0353] If the above determination unit (55004) determines that encoding based on inter prediction is not applied to the attribute information, the attribute information intra prediction unit (55006) performs intra prediction coding on the input attribute information. According to embodiments, the intra prediction coding method performed by the attribute information intra prediction unit (55006) may include a Predicting Transform coding method, a Lift Transform coding method, a RAHT coding method, and the like.

[0354] If it is confirmed in the above determination unit (55004) that encoding based on inter prediction is applied to the attribute information, the attribute information inter prediction unit (55005) performs inter prediction coding on the input attribute information.

[0355] According to embodiments, the attribute information inter prediction unit (55005) may include a method of coding a residual value based on the difference in attribute prediction values ​​between the current frame and a motion compensated reference frame. At this time, the attribute information inter prediction unit (55005) may receive position information and attribute information of an attribute reference frame stored in a reference frame buffer (53009) for inter prediction through the reference frame generation unit (53010), or may receive position information of an attribute reference frame reconstructed by applying one of non-sampling, down-sampling, partial sampling, or adaptive sampling in the reference frame generation unit (53010) and the attribute information stored in the reference frame buffer (53009) through the reference frame generation unit (53010).

[0356] The attribute information entropy encoding unit (55008) according to the embodiments performs entropy encoding on attribute information encoded based on intra prediction in the attribute information intra prediction unit (55006) or on attribute information encoded based on inter prediction in the attribute information inter prediction unit (55005) to output an attribute bitstream (or attribute information bitstream).

[0357] An attribute restoration unit (55009) according to embodiments restores (or reconstructs) attribute information based on attributes changed through intra-prediction coding or inter-prediction coding, and stores the restored attribute information (or so-called restored attribute) in a reference frame buffer (53009).

[0358] In the present disclosure, some or all of the buffer-related option information may be signaled in an SPS, APS, or attribute data unit header. The attribute data unit header may be referred to as an attribute slice header. In this case, as an embodiment, the buffer-related option information is processed by a signaling processing unit (51005). A detailed description of the buffer-related option information will be provided later with reference to FIGS. 22 to 25.

[0359] Meanwhile, the geometry bitstream compressed and output based on intra prediction or inter prediction from the geometry encoder (51006) and the attribute bitstream compressed and output based on intra prediction or inter prediction from the attribute encoder (51007) are output to the transmission processing unit (51008).

[0360] 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 (12012) 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.

[0361] 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.

[0362] 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.

[0363] 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).

[0364] 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.

[0365] 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.

[0366] In one embodiment of the present disclosure, some or all of the buffer-related option information is signaled in at least one of a sequence parameter set, an attribute parameter set, a tile parameter set, and an attribute data unit header (or attribute slice header).

[0367] FIG. 20 is a drawing showing another example of a point cloud receiving device according to embodiments.

[0368] The elements of the point cloud receiving device illustrated in FIG. 20 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 receiving device illustrated in FIG. 20 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 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 receiving device illustrated in FIG. 20. In addition, 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 receiving device illustrated in FIG. 20. The execution order of each block in FIG. 20 may be changed, some blocks may be omitted, and some blocks may be newly added.

[0369] The point cloud receiving device of Fig. 20 can correspond to the receiving device (10004), receiver (10005), point cloud video decoder (10006) of Fig. 1, transmission-decoding-rendering (20002-20003-20004) of Fig. 2, point cloud video decoder of Fig. 7, receiving device of Fig. 9, device of Fig. 10, etc.

[0370] 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.

[0371] 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.

[0372] 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).

[0373] 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).

[0374] 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, signaling information included in a geometry slice header (or referred to as geometry data unit header) and / or an attribute slice header (or referred to as attribute data unit header) may also be pre-parsed by the signaling processing unit (61002) before decoding the corresponding slice data. That is, if point cloud data is divided into tiles and / or slices on the transmitting side, since the TPS includes the number of slices included in each tile, the point cloud video decoder according to the embodiments can check the number of slices and quickly parse information for parallel decoding.

[0375] 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 inter prediction by managing a reference frame buffer based on buffer-related option information signaled in the SPS, APS, TPS, and / or attribute data unit header.

[0376] That is, the geometry decoder (61003) can restore the geometry by performing the reverse process of the geometry encoder (51006) of FIG. 18 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). Here, the geometry-related parameters can include inter-prediction-related option information to be used for inter-prediction restoration of geometry information.

[0377] The attribute decoder (61004) can perform the reverse process of the attribute encoder (51007) of FIG. 18 to restore attributes based on signaling information (e.g., attribute-related parameters) and reconstructed geometry for the compressed attribute bitstream. According to embodiments, if the point cloud data is divided into tile and / or slice units at the transmitting side, the geometry decoder (61003) and the attribute decoder (61004) can perform geometry decoding and attribute decoding for tile and / or slice units. According to embodiments, if the point cloud data is divided into LPUs and / or PUs at the transmitting side, the geometry decoder (61003) and the attribute decoder (61004) can perform geometry decoding and attribute decoding for each LPU and / or PU.

[0378] According to embodiments, when the geometry information restored by the geometry decoder (61003) is stored in the reference frame buffer as the position information of the geometry reference frame, but the position information of the attribute reference frame is not stored in the reference frame buffer, and when the attribute decoder (61004) performs inter prediction, the attribute decoder (61004) can reconstruct the position information of the attribute reference frame based on the position information of the geometry reference frame stored in the reference frame buffer, and perform inter prediction based on the reconstructed position information of the attribute reference frame to restore the attribute information. At this time, the position information of the attribute reference frame can be generated by applying at least one of a non-sampling, down-sampling, partial sampling, or adaptive sampling method based on the position information of the geometry reference frame. And, when reconstructing an attribute reference frame based on the position information of the geometry reference frame, the description of generating (i.e., reconstructing) the attribute reference frame by applying at least one of the non-sampling, down-sampling, partial sampling, or adaptive sampling methods is omitted here as it has been described above.

[0379] FIG. 21 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. 21 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. 21 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. 21 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. 21. The execution order of each block in FIG. 21 may be changed, some blocks may be omitted, and some blocks may be newly added.

[0380] The geometry information entropy encoding unit (63001), the inverse quantization processing unit (63007), and the coordinate inverse transformation unit (63008) included in the geometry decoder (61003) of Fig. 21 may perform part or all of the operations of the arithmetic decoder (11000) and the coordinate inverse transformation unit (11004) of Fig. 7, or may perform part or all of the operations of the arithmetic decoder (13002) and the inverse quantization processing unit (13005) of Fig. 9. The positions restored by the geometry decoder (61003) are output to the post-processing unit (61005).

[0381] According to embodiments, if inter prediction related option information for inter prediction restoration of geometry information is signaled in at least one of a geometry parameter set (GPS), a tile parameter set (TPS), and a geometry slice header, the signaling processing unit (61002) may obtain it and provide it to a geometry decoder (61003), or may obtain it directly from the geometry decoder (61003).

[0382] That is, the above geometry information entropy decoding unit (63001) entropy decodes the input geometry bitstream.

[0383] According to embodiments, if intra-prediction-based encoding is applied to geometry information at the transmitting side, the geometry decoder (61003) performs intra-prediction-based restoration on the geometry information. Conversely, if inter-prediction-based encoding is applied to geometry information at the transmitting side, the geometry decoder (61003) performs inter-prediction-based restoration on the geometry information.

[0384] For this purpose, drawing code 63002 (or the determination unit) checks whether intra prediction-based coding or inter prediction-based coding is applied to the geometry information.

[0385] If the above-described determination unit (63002) determines that intra-prediction-based coding has been applied to the geometry information, the entropy-decoded geometry information is provided to the geometry information intra-prediction restoration unit (63003). Conversely, if the above-described determination unit (63002) determines that inter-prediction-based coding has been applied to the geometry information, the entropy-decoded geometry information is output to the LPU / PU division unit (63004).

[0386] The geometry information intra prediction restoration unit (63003) according to the embodiments decodes and restores geometry information based on an intra prediction method. That is, the geometry information intra prediction restoration unit (63003) can restore geometry information predicted by geometry intra prediction coding. Intra prediction coding methods may include octree coding, prediction tree coding, and tri-Soop coding methods.

[0387] The LPU / PU splitter (63004) according to the embodiments splits a reference frame (or tile or slice) into LPU / PUs using a signaled region value (e.g., inter-prediction related option information) for LPU / PU split indication and to support inter-prediction-based restoration when the frame of geometry information to be decoded is a P frame.

[0388] A motion compensation application unit (63005) according to embodiments may generate predicted geometry information by applying a motion vector (e.g., a global motion vector and / or a local motion vector) to an LPU / PU segmented from a reference frame (or tile or slice). Here, the motion vector may be received as included in signaling information.

[0389] The geometry information inter prediction restoration unit (63006) according to the embodiments decodes geometry information based on an inter prediction method to restore a prediction value. That is, geometry inter prediction coded geometry information can be restored based on geometry information of a motion-compensated reference frame (or a reference frame on which motion compensation has not been performed). The inter prediction coding method according to the embodiments may include an octree-based inter-coding method, a predictive-tree-based inter-coding method, a trisoup-based inter-coding method, etc.

[0390] The geometry information restored by the geometry information intra prediction restoration unit (63003) or the geometry information restored by the geometry information inter prediction restoration unit (63006) is input to the geometry information conversion dequantization processing unit (63007).

[0391] The geometry information inverse transformation inverse quantization unit (63007) according to the embodiments may perform the reverse process of the transformation performed by the geometry information transformation quantization processing unit (51003) of the transmitting device on the restored geometry information and multiply the result by a scale (=geometry quantization value) to generate restored geometry information on which inverse quantization has been performed. That is, the geometry information transformation inverse quantization processing unit (63007) may perform inverse quantization of the geometry information by applying the scale (scale=geometry quantization value) included in the signaling information to the geometry location x, y, and z values ​​of the restored point.

[0392] The above coordinate system inversion unit (63008) 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 (63008) can restore the xyz axes changed on the transmitting side or inversely transform the transformed coordinate system into the xyz orthogonal coordinate system.

[0393] According to embodiments, the geometry information dequantized in the geometry information conversion dequantization processing unit (63007) goes through a geometry restoration process and is stored as a reference frame in a reference frame buffer (63009), and is also output to an attribute decoder (61004) for attribute decoding. At this time, the geometry information stored in the reference frame buffer (63009) becomes position information of a geometry reference frame during inter prediction of the next frame. The reference frame buffer (63009) also stores attribute information restored by the attribute decoder (61004). That is, the restored geometry information and restored attribute information stored in the reference frame buffer (63009) can be used as a previous reference frame for geometry information inter-prediction and attribute information inter-prediction in the geometry information inter-prediction restoration unit (63006) of the geometry decoder (61003) and the attribute information inter-prediction restoration unit (65003) of the attribute decoder (61004).

[0394] At this time, the reference frame buffer (63009) may separately manage the geometry reference frame and the attribute reference frame as in Fig. 12(a), or may manage them as an integrated reference frame as in Fig. 12(a). In the case of Fig. 12(a), both the position information of the geometry reference frame and the position information of the attribute reference frame are stored in the reference frame buffer (63009). At this time, the attribute information corresponding to the position information of the attribute reference frame is also stored in the reference frame buffer (63009). In contrast, in the case of Fig. 12(b), only the position information and attribute information of the geometry reference frame are stored in the reference frame buffer (63009), and the position information of the attribute reference frame is not stored in the reference frame buffer (63009). In this case, the reference frame generation unit (63010) reconstructs the position information of the attribute reference frame based on the position information of the geometry reference frame stored in the reference frame buffer (63009). That is, the reference frame generation unit (63010) may transfer the position information of the geometry reference frame stored in the reference frame buffer (63009), the position information of the attribute reference frame, and the attribute information to a required module, or may transfer the position information of the attribute reference frame reconstructed by applying one of the geometry reference frame and the attribute information stored in the reference frame buffer (63009), and non-sampling, down-sampling, partial sampling, or adaptive sampling to a required module.

[0395] That is, in the present disclosure, when only the position information of the geometry reference frame is stored in the reference frame buffer (63009), the reference frame generation unit (63010) may reconstruct the attribute reference frame by applying at least one of a non-sampling, down-sampling, partial sampling, or adaptive sampling method when reconstructing the attribute reference frame based on the position information of the geometry reference frame. In one embodiment, the reference frame generation unit (63010) may generate the attribute reference frame by applying a non-sampling reference frame generation method based on the position information of the geometry reference frame according to buffer-related option information. In another embodiment, the reference frame generation unit (63010) may generate the attribute reference frame by applying a down-sampling reference frame generation method based on the position information of the geometry reference frame according to buffer-related option information. In another embodiment, the reference frame generation unit (63010) may generate an attribute reference frame by applying a partial sampling reference frame generation method based on position information of a geometry reference frame according to buffer-related option information. In another embodiment, the reference frame generation unit (63010) may generate an attribute reference frame by applying an adaptive sampling reference frame generation method based on position information of a geometry reference frame according to buffer-related option information. In addition, the reference frame generation unit (63010) may utilize an indexing-based reference frame management method according to buffer-related option information. A more detailed description has already been provided above, so it will be omitted here.

[0396] In the present disclosure, some or all of the buffer-related option information may be signaled and received in an SPS, APS, or attribute data unit header, etc. A detailed description of the buffer-related option information will be provided later with reference to FIGS. 22 to 25.

[0397] According to embodiments, the attribute residual information entropy decoding unit (65001) of the attribute decoder (61004) can entropy decode an input attribute bitstream.

[0398] According to embodiments, if intra-prediction-based encoding is applied to attribute information at the transmitting end, the attribute decoder (61004) performs intra-prediction-based reconstruction on the attribute information. Conversely, if inter-prediction-based encoding is applied to the attribute information at the transmitting end, the attribute decoder (61004) performs inter-prediction-based reconstruction on the attribute information.

[0399] For this purpose, drawing code 65002 (or the determination unit) checks whether intra prediction-based coding or inter prediction-based coding is applied to attribute information.

[0400] If the above-described determination unit (65002) determines that intra-prediction-based coding has been applied to the attribute information, the entropy-decoded attribute information is provided to the attribute information intra-prediction restoration unit (65004). Conversely, if the above-described determination unit (65002) determines that inter-prediction-based coding has been applied to the attribute information, the entropy-decoded attribute information is provided to the attribute information inter-prediction restoration unit (65003).

[0401] The attribute information inter prediction restoration unit (65003) according to embodiments decodes and restores attribute information based on an inter prediction method. That is, it restores attribute information predicted by inter prediction coding. According to embodiments, the attribute information inter prediction restoration unit (65003) may receive position information and attribute information of an attribute reference frame stored in a reference frame buffer (63009) for inter prediction through a reference frame generation unit (63010), or may receive attribute information stored in the reference frame buffer (63009) and position information of an attribute reference frame reconstructed by the reference frame generation unit (63010) through the reference frame generation unit (63010).

[0402] The attribute information intra prediction restoration unit (65004) according to the embodiments decodes and restores attribute information based on an intra prediction method. That is, it restores attribute information predicted using intra prediction coding. Intra coding methods may include the Predicting Transform coding method, the Lift Transform coding method, and the RAHT coding method.

[0403] According to embodiments, the restored attribute information may be stored in a reference frame buffer (63009). The geometry information and attribute information stored in the reference frame buffer (63009) may be provided to a geometry information inter-prediction restoration unit (63003) and an attribute information inter-prediction restoration unit (65003) as a previous reference frame.

[0404] According to embodiments, the restored attribute information may be provided to the color inverse conversion processing unit (65005) to be restored to RGB colors. That is, the color inverse conversion processing unit (65005) performs inverse conversion coding to inversely convert the color value (or texture) included in the restored attribute information and outputs the inverse conversion coding to the post-processing unit (61005). The color inverse conversion processing unit (65005) performs operations and / or inverse conversion coding that are identical or similar to the operations and / or inverse conversion coding of the color inverse conversion unit (11010) of FIG. 7 or the color inverse conversion processing unit (13010) of FIG. 9.

[0405] 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.

[0406] Figure 22 shows an example of a bitstream structure of point cloud data for transmission / reception according to embodiments.

[0407] In some embodiments, the term “slice” in FIG. 22 may be referred to as the term “data unit.”

[0408] Also, in FIG. 22, each abbreviation means the following. Each abbreviation may be referred to by other terms within the scope of equivalent meaning. SPS: Sequence Parameter Set, GPS: Geometry Parameter Set, APS: Attribute Parameter Set, TPS: Tile Parameter Set, Geometry (Geom: Geometry bitstream = geometry slice header + [geometry PU header + Geometry PU data] | geometry slice data), Attribute (Attr: Attribute bitstream = attribute data unit header + [attribute PU header + attribute PU data] | attribute data unit data).

[0409] The present disclosure may signal relevant information to add / perform the embodiments described so far. The signaling information according to the embodiments may be used in a point cloud video encoder at a transmitting end or a point cloud video decoder at a receiving end.

[0410] The point cloud video encoder according to the embodiments can generate a bitstream as in FIG. 22 by encoding geometry information and attribute information as described above. In addition, signaling information regarding point cloud data can 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.

[0411] For example, a point cloud video encoder that performs geometry encoding and / or attribute encoding can generate an encoded point cloud (or a bitstream including a point cloud) as illustrated in FIG. 22. Additionally, signaling information regarding point cloud data can be generated and processed by a metadata processing unit of a point cloud data transmission device and included in the point cloud as illustrated in FIG. 22.

[0412] 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.

[0413] 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.

[0414] 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).

[0415] Each geometry bitstream within a slice (also called a geometry slice) can consist of a geometry slice header and one or more geometry PUs (Geom PU0, Geom PU1). Each geometry PU can consist of a geometry PU header and geometry PU data.

[0416] Each attribute bitstream (or attribute slice) within each slice may consist of an attribute slice header and one or more attribute PUs (Attr PU0, Attr PU1). Each attribute PU may consist of an attribute PU header (attr PU header) and attribute PU data (attr PU data).

[0417] Some or all of the buffer-related option information according to embodiments may be added and signaled to the SPS and / or APS.

[0418] Some or all of the buffer-related option information according to embodiments may be signaled by being added to the attribute slice header (or attribute data unit header) for each slice.

[0419] As shown in Fig. 22, the bitstream of point cloud data is divided into tiles, slices, LPUs, and / or PUs so that the point cloud data can be processed by dividing the point cloud data by region. Each region of the bitstream according to embodiments may have different importance. Therefore, when point cloud data is divided into tiles, different filters (encoding methods) and different filter units can be applied to each tile. In addition, when point cloud data is divided into slices, different filters and different filter units can be applied to each slice. In addition, when point cloud data is divided into LPUs / PUs, different filters and different filter units can be applied to each LPU / PU.

[0420] The transmitting device according to the embodiments transmits point cloud data according to the bitstream structure as illustrated in FIG. 22, thereby enabling the application of different encoding operations based on importance and providing a method for utilizing high-quality encoding methods in important areas. Furthermore, the device supports efficient encoding and transmission based on the characteristics of point cloud data and can provide attribute values ​​according to user requirements.

[0421] The receiving device according to the embodiments receives point cloud data according to the structure of the bitstream as illustrated in FIG. 22, 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.

[0422] 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.

[0423] The term field, used in the syntaxes of the present disclosure described hereinafter, may have the same meaning as a parameter or syntax element.

[0424] FIG. 23 is a diagram showing an example of a syntax structure of a sequence parameter set (sequency__parameter_set()) (SPS) including buffer-related option information according to embodiments. Information on whether to apply indexing-based reference frame management and whether to utilize an integrated reference frame for supporting inter prediction compression of the present disclosure may be added to and signaled in the SPS. In the present disclosure, information on whether to apply indexing-based reference frame management and whether to utilize an integrated reference frame for supporting inter prediction compression may be referred to as buffer-related option information. The name of the signaling information may be understood within the scope of the meaning and function of the signaling information.

[0425] In Figure 23, the profile_idc field indicates the profile that the bitstream conforms to.

[0426] 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.

[0427] The sps_seq_parameter_set_id field provides an identifier for the SPS for reference by other syntax elements.

[0428] The sps_num_attribute_sets field indicates the number of coded attributes in the bitstream.

[0429] 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.

[0430] The above attribute_dimension[i] field specifies the number of components of the i-th attribute.

[0431] The above attribute_instance_id[i] field indicates the instance identifier of the i-th attribute.

[0432] The SPS according to embodiments may further include an index_based_reference_frame_management_usage_flag field and an integrated_reference_frame_buffer_usage_flag field.

[0433] The above index_based_reference_frame_management_usage_flag field can specify whether indexing-based reference frame management is applied. For example, if the value of the above index_based_reference_frame_management_usage_flag field is true (i.e., 1), it can indicate that the reference frame is managed based on indexing.

[0434] The above integrated_reference_frame_buffer_usage_flag field can specify whether to use an integrated reference frame buffer for the frame. For example, if the value of the above integrated_reference_frame_buffer_usage_flag field is 1, it can indicate that an integrated reference frame buffer is used for the frame.

[0435] The present disclosure may refer to the index_based_reference_frame_management_usage_flag field and the integrated_reference_frame_buffer_usage_flag field as buffer-related option information.

[0436] FIG. 24 is a diagram illustrating an example syntax structure of an attribute parameter set (attribute_parameter_set()) (APS) including buffer-related option information according to embodiments. Parameter information for reference frame generation in the present disclosure may be added to the APS and signaled. In the present disclosure, parameter information for reference frame generation may be referred to as buffer-related option information. The name of the signaling information may be understood within the scope of the meaning and function of the signaling information.

[0437] In Figure 24, the aps_attr_parameter_set_id field indicates the identifier of the APS for reference by other syntax elements.

[0438] The aps_seq_parameter_set_id field indicates the value of sps_seq_parameter_set_id for the active SPS.

[0439] The attr_coding_type field indicates the coding type for the attribute.

[0440] The lifting_num_pred_nearest_neighbours field specifies the maximum number of nearest neighbors to be used for prediction.

[0441] The lifting_max_num_direct_predictors field specifies the maximum number of predictors to be used for direct prediction. The variable MaxNumPredictors is used in the decoding process as follows: MaxNumPredictors = lifting_max_num_direct_predicots + 1

[0442] The spherical_coord_flag field indicates whether the geometry reference frame is in orthogonal or angular coordinates.

[0443] According to embodiments, the APS may further include an attr_sampling_flag field and an attr_sampling_type field.

[0444] The above attr_sampling_flag field specifies whether sampling is applied to the attribute frame. For example, if the value of the above attr_sampling_flag field is true (i.e., 1), it can indicate that sampling is applied to the attribute frame. In addition, when creating a reference frame based on partial sampling, whether sampling is performed for each group can be indicated through the attr_sampling_flag field. For example, sampling can be performed only for groups for which the attr_sampling_flag is true.

[0445] The above attr_sampling_type field specifies the sampling type for the attribute frame. For example, if the value of the attr_sampling_type field is 0, it indicates that the attribute reference frame is reconstructed (or created) through down-sampling, if it is 1, it indicates that the attribute reference frame is reconstructed (or created) through partial sampling, and if it is 2, it indicates that the attribute reference frame is reconstructed (or created) through adaptive sampling.

[0446] The present disclosure may refer to the spherical_coord_flag field, the attr_sampling_flag field, and / or the attr_sampling_type field as buffer-related option information.

[0447] FIGS. 25A and 25B are diagrams showing an example of a syntax structure of an attribute data unit header (attribute_data_unit_heaser()) including buffer-related option information according to embodiments. The name of the signaling information can be understood within the scope of the meaning and function of the signaling information. The storage of the selected points mentioned in FIGS. 25A and 25B does not mean storing them in a reference frame buffer, but temporarily storing them in a memory such as RAM for use in inter prediction during encoding / decoding. That is, the position information of an attribute reference frame generated by applying one of non-sampling, down-sampling, partial sampling, or adaptive sampling is not stored in the reference frame buffer.

[0448] The ash_attr_parameter_set_id field indicates the value of aps_attr_parameter_set_id for the active APS.

[0449] The ash_attr_sps_attr_idx field specifies the attribute set within the active SPS. The value of this field ranges from 0 to sps_num_attribute_sets within the active SPS.

[0450] The ash_attr_geom_slice_id field specifies the gsh_slice_id value of the active geometry slice header.

[0451] According to embodiments, the attribute data unit header may include at least one of the attr_N field, the attr_octree_node_level field, the attr_sampling_method_type field, the num_of_groups field, the sampling_applied_group_array field, the attr_N_array field, and the attr_sampling_method_type_array field, depending on the value of the attr_sampling_type field, when the value of the integrated_reference_frame_buffer_usage_flag field is true (i.e., 1) and the value of the attr_sampling_flag field is true (i.e., 1).

[0452] In one embodiment, if the value of the integrated_reference_frame_buffer_usage_flag field is true (i.e., 1), it indicates that the integrated reference frame buffer is used for the slice.

[0453] The above attr_sampling_flag field specifies whether sampling is applied to the attribute frame. For example, if the value of the above attr_sampling_flag field is true (i.e., 1), it may indicate that sampling is applied to the attribute frame.

[0454] More specifically, if the value of the attr_sampling_type field is 0 (i.e., indicating downsampling), the attribute data unit header includes the attr_N field, and if the value of the spherical_coord_flag field is 0, it further includes the attr_octree_node_level field, and if it is not 0, it further includes the attr_sampling_method_type field.

[0455] The above attr_N field indicates the number of points sampled for each group.

[0456] The above attr_octree_node_level field specifies the octree depth level value when the encoder / decoder uses an orthogonal coordinate system applied to the slice. That is, it is used to determine the interval based on the octree. In other words, when the geometry reference frame is an orthogonal coordinate system, the octree-specific depth level is specified as attr_octree_node_level.

[0457] The above attr_sampling_method_type field specifies how to select attr_N points per area divided by the azimuth applied to the slice.

[0458] For example, in the encoder / decoder, if the value of the attr_sampling_method_type field is 0, only attr_N points are selected and stored from the first point, if it is 1, only attr_N points are selected and stored in the order of the smallest value in the azimuth range, if it is 2, only attr_N points are selected and stored from the last point, if it is 3, only attr_N points are selected and stored in the order of the largest value in the azimuth range, and if it is 4, only attr_N points are selected and stored by downsampling.

[0459] If the value of the attr_sampling_type field is 1 (i.e., indicating partial sampling), the attribute data unit header includes the attr_N field and as many sampling_applied_group_array[i] fields as the number of groups. In addition, if the value of the spherical_coord_flag field is 0, the attr_octree_node_level field is further included, and if it is not 0, the attr_sampling_method_type field is further included.

[0460] The above attr_N field indicates the number of points sampled for each group.

[0461] The above num_of_groups field specifies the number of groups.

[0462] The above sampling_applied_group_array[i] specifies whether sampling is applied to the i-th group when using the partial sampling technique.

[0463] The above attr_octree_node_level field specifies the octree depth level value when the encoder / decoder uses an orthogonal coordinate system applied to the slice. That is, it is used to determine the interval based on the octree. In other words, when the geometry reference frame is an orthogonal coordinate system, the octree-specific depth level is specified as attr_octree_node_level.

[0464] The above attr_sampling_method_type field specifies how to select attr_N points per area divided by the azimuth applied to the slice.

[0465] For example, in the encoder / decoder, if the value of the attr_sampling_method_type field is 0, only attr_N points are selected and stored from the first point, if it is 1, only attr_N points are selected and stored in the order of the smallest value in the azimuth range, if it is 2, only attr_N points are selected and stored from the last point, if it is 3, only attr_N points are selected and stored in the order of the largest value in the azimuth range, and if it is 4, only attr_N points are selected and stored by downsampling.

[0466] If the value of the above attr_sampling_type field is 2 (i.e., indicating adaptive sampling), the attribute data unit header may include as many attr_N_array[i] fields and attr_sampling_method_type_array[i] fields as the number of groups. In addition, if the value of the spherical_coord_flag field is 0, the above attr_octree_node_level field is further included.

[0467] The above num_of_groups field specifies the number of groups.

[0468] The above attr_N_array[i] field specifies the number of points sampled in the i-th group when applying adaptive sampling.

[0469] The above attr_sampling_method_type_array[i] field specifies methods for selecting attr_N points of the ith group among the groups divided by the azimuth applied to the slice. For example, in the encoder / decoder, if the value of the attr_sampling_method_type_array[i] field is 0, only attr_N points are selected and stored from the first point for the ith group, if it is 1, only attr_N points are selected and stored in the order of the smallest value in the azimuth range, if it is 2, only attr_N points are selected and stored from the last point, if it is 3, only attr_N points are selected and stored in the order of the largest value in the azimuth range, and if it is 4, only attr_N points are selected and stored by downsampling.

[0470] The above attr_octree_node_level field specifies the octree depth level value when using an orthogonal coordinate system applied to the slice. That is, it is used to determine the interval based on the octree. In other words, when the geometry reference frame is an orthogonal coordinate system, an octree-specific depth level is specified as attr_octree_node_level.

[0471] Fig. 26 is a flowchart illustrating an example of a reference frame generation method according to embodiments. The reference frame generation method of Fig. 26 may be performed in an encoder of a transmitting device and / or in a decoder of a receiving device.

[0472] In this case, the reference frame buffer is an integrated reference frame buffer, as an example.

[0473] That is, it is assumed that the reference frame buffer stores position information and attribute information of the geometry reference frame, and does not store position information of the attribute reference frame. Therefore, FIG. 26 is a process for reconstructing an attribute reference frame by applying one of non-sampling, down-sampling, partial sampling, or adaptive sampling based on buffer-related option information included in signaling information and position information of the geometry reference frame. In one embodiment of the present disclosure, all or part of the buffer-related option information is included in the SPS, APS, and attribute data unit headers as shown in FIGS. 22 to 25.

[0474] Step S66001 determines whether sampling is applied. For example, if the value of the integrated_reference_frame_buffer_usage_flag field is true and the value of the attr_sampling_flag field is true, it can be determined that sampling is applied to reconstruct the attribute reference frame.

[0475] If it is determined that sampling is not applied in step S66001, a non-sampling method is applied based on the position information of the geometry reference frame to reconstruct the attribute reference frame and use it for inter prediction (step S66013).

[0476] If it is determined in step S66001 that sampling is applied, step S66002 determines whether downsampling is applied. For example, if the value of the attr_sampling_type field is 0, it can be determined that downsampling is applied.

[0477] If it is determined in step S66002 that downsampling is applied, step S66003 determines whether the geometric coordinate system is an orthogonal coordinate system. For example, if the value of the spherical_coord_flag field is 0, it is determined to be an orthogonal coordinate system, and if it is not 0, it is determined to be an angular coordinate system.

[0478] If it is determined to be an orthogonal coordinate system in step S66003, orthogonal coordinate system-based downsampling is applied (step S66004), and if it is determined to be an angular coordinate system, angular coordinate system-based downsampling is applied (step S66005), and then the attribute reference frame is reconstructed and used for inter prediction (step S66013).

[0479] According to embodiments, the attr_N field included in the attribute data unit header for down sampling indicates the number of sampled points for each group, and the attr_octree_node_level field specifies an octree depth level value when the geometry coordinate system is an orthogonal coordinate system. In addition, the attr_sampling_method_type field specifies a method for selecting attr_N points for each zone divided by azimuth when the geometry coordinate system is an angular coordinate system. For example, if the value of the attr_sampling_method_type field is 0, only attr_N points are selected and stored from the first point, if it is 1, only attr_N points are selected and stored in the order of the smallest value in the azimuth range, if it is 2, only attr_N points are selected and stored from the last point, if it is 3, only attr_N points are selected and stored in the order of the largest value in the azimuth range, and if it is 4, only attr_N points can be selected and stored by down sampling.

[0480] If it is determined in step S66002 that downsampling is not applied, step S66006 determines whether adaptive sampling is applied. For example, if the value of the attr_sampling_type field is 1, it can be determined that partial sampling is applied, and if the value of the attr_sampling_type field is 2, it can be determined that adaptive sampling is applied.

[0481] If it is determined in step S66006 that partial sampling is applied, step S66007 determines whether the geometric coordinate system is an orthogonal coordinate system. For example, if the value of the spherical_coord_flag field is 0, it can be determined that it is an orthogonal coordinate system, and if it is not 0, it can be determined that it is an angular coordinate system.

[0482] If it is determined to be an orthogonal coordinate system in step S66007, partial sampling based on the orthogonal coordinate system is applied (step S66008), and if it is determined to be an angular coordinate system, partial sampling based on the angular coordinate system is applied (step S66009), and the attribute reference frame is reconstructed and then utilized for inter prediction (step S66013).

[0483] According to embodiments, the attr_N field included in the attribute data unit header for partial sampling indicates the number of sampled points for each group, and sampling_applied_group_array[i] specifies whether sampling is applied to the i-th group when the partial sampling technique is used. In addition, the attr_octree_node_level field specifies an octree depth level value when the geometry coordinate system is an orthogonal coordinate system, and the attr_sampling_method_type field specifies a method for selecting attr_N points for each zone divided by azimuth when the geometry coordinate system is an angular coordinate system. For example, if the value of the attr_sampling_method_type field is 0, only attr_N points are selected and stored from the first point, if it is 1, only attr_N points are selected and stored in order of the smallest value in the azimuth range, if it is 2, only attr_N points are selected and stored from the last point, if it is 3, only attr_N points are selected and stored in order of the largest value in the azimuth range, and if it is 4, only attr_N points are selected and stored by downsampling.

[0484] If it is determined in step S66006 that adaptive sampling is applied, step S66010 determines whether the geometric coordinate system is an orthogonal coordinate system. For example, if the value of the spherical_coord_flag field is 0, it is determined to be an orthogonal coordinate system, and if it is not 0, it is determined to be an angular coordinate system.

[0485] If it is determined to be an orthogonal coordinate system in step S66010, adaptive sampling based on an orthogonal coordinate system is applied (step S66011), and if it is determined to be an angular coordinate system, adaptive sampling based on an angular coordinate system is applied (step S66012), and then the attribute reference frame is reconstructed and used for inter prediction (step S66013).

[0486] According to embodiments, the attr_N_array[i] field included in the attribute data unit header for adaptive sampling specifies the number of points sampled in the ith group when applying adaptive sampling, and when the geometry coordinate system is an angular coordinate system, the attr_sampling_method_type_array[i] field specifies methods for selecting attr_N points of the ith group among the groups by zones divided by the azimuth applied to the slice. For example, if the value of the attr_sampling_method_type_array[i] field is 0, only attr_N points are selected and stored from the first point, if it is 1, only attr_N points are selected and stored in the order of the smallest value in the azimuth range, if it is 2, only attr_N points are selected and stored from the last point, if it is 3, only attr_N points are selected and stored in the order of the largest value in the azimuth range, and if it is 4, only attr_N points can be selected and stored by downsampling. Additionally, the attr_octree_node_level field specifies the octree depth level value when the geometry coordinate system is an orthogonal coordinate system.

[0487] Fig. 27 shows a flowchart of a point cloud data encoding method according to embodiments.

[0488] A point cloud data encoding method according to embodiments may include a step of encoding geometry data of point cloud data (S71001) and an encoding step of encoding attribute data of point cloud data (S71002). The point cloud data encoding method may further include a step of transmitting a bitstream including encoded geometry data, encoded attribute data, and signaling information. In this case, the bitstream may be encapsulated and transmitted as a file. In the present disclosure, geometry data is used interchangeably with geometry information and attribute data is used interchangeably with attribute information. The step of encoding geometry data of point cloud data (S71001) and the step of encoding attribute data of point cloud data (S71002) 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. 18, and the geometry encoder and attribute encoder of FIG. 19 for encoding the geometry data and the attribute data.

[0489] According to embodiments, the step of encoding geometry data of point cloud data (S71001) performs inter-prediction or intra-prediction-based encoding on positions of point cloud data (i.e., referred to as geometry information or geometry data) to output a geometry bitstream.

[0490] According to embodiments, the step of encoding geometry data of point cloud data (S71001) uses position information of a geometry reference frame stored in a reference frame buffer during inter prediction.

[0491] At this time, the reference frame buffer can manage the geometry reference frame and the attribute reference frame separately as shown in Fig. 12(a), or can manage them as an integrated reference frame as shown in Fig. 12(a). In the case of Fig. 12(a), both the position information of the geometry reference frame and the position information of the attribute reference frame are stored in the reference frame buffer. At this time, the attribute information corresponding to the position information of the attribute reference frame is also stored in the reference frame buffer. In contrast, in the case of Fig. 12(b), only the position information and attribute information of the geometry reference frame are stored in the reference frame buffer, and the position information of the attribute reference frame is not stored in the reference frame buffer. In this case, the reference frame generation unit reconstructs the position information of the attribute reference frame by applying one of non-sampling, down-sampling, partial sampling, or adaptive sampling based on the position information of the geometry reference frame stored in the reference frame buffer. That is, the reference frame generation unit can transfer the position information of the geometry reference frame stored in the reference frame buffer, the position information of the attribute reference frame, and the attribute information to the required module, or transfer the geometry reference frame and the attribute information stored in the reference frame buffer, and the position information of the attribute reference frame reconstructed by applying one of non-sampling, down-sampling, partial sampling, or adaptive sampling to the required module.

[0492] That is, in the present disclosure, when only the position information of the geometry reference frame is stored in the reference frame buffer, the reference frame generation unit may reconstruct the attribute reference frame by applying at least one of a non-sampling, down-sampling, partial sampling, or adaptive sampling method when reconstructing the attribute reference frame based on the position information of the geometry reference frame. In one embodiment, the reference frame generation unit may generate the position information of the attribute reference frame by applying a non-sampling reference frame generation method based on the position information of the geometry reference frame. In another embodiment, the reference frame generation unit may generate the position information of the attribute reference frame by applying a down-sampling reference frame generation method based on the position information of the geometry reference frame. In yet another embodiment, the reference frame generation unit may generate the position information of the attribute reference frame by applying a partial sampling reference frame generation method based on the position information of the geometry reference frame. In another embodiment, the reference frame generation unit can generate the position information of the attribute reference frame by applying an adaptive sampling reference frame generation method based on the position information of the geometry reference frame. Furthermore, the reference frame generation unit can utilize an indexing-based reference frame management method. A more detailed explanation has already been provided above, so it will be omitted here.

[0493] The geometry information of each point compressed based on the above inter prediction or intra prediction is entropy encoded and then output in the form of a geometry bitstream.

[0494] According to embodiments, the step of encoding attribute data of point cloud data (S71002) performs inter-prediction or intra-prediction-based encoding on attribute information based on positions where geometry encoding has not been performed and / or reconstructed geometry information, thereby outputting an attribute bitstream.

[0495] According to embodiments, in the case of intra prediction, the attribute information may be coded by using one or a combination of one or more of RAHT coding, LOD-based prediction transform coding, and lifting transform coding.

[0496] According to embodiments, in the case of inter prediction, a method of coding a residual value based on a difference in attribute prediction values ​​between a current frame and a motion-compensated reference frame may be included. At this time, position information and attribute information of an attribute reference frame stored in a reference frame buffer may be provided through a reference frame generation unit, or position information of an attribute reference frame reconstructed based on one of non-sampling, down-sampling, partial sampling, or adaptive sampling in the reference frame generation unit and the attribute information stored in the reference frame buffer may be provided through a reference frame generation unit and used for inter prediction.

[0497] The attribute information compressed based on the above intra prediction or inter prediction is entropy encoded and then output in the form of an attribute bitstream.

[0498] In the present disclosure, signaling information may include buffer-related option information. Buffer option-related information is omitted here, with reference to the descriptions of FIGS. 22 to 25.

[0499] Figure 28 shows a flowchart of a point cloud data decoding method according to embodiments.

[0500] A method for decoding point cloud data according to embodiments may include a step of decoding geometry data of point cloud data (S81001) and a step of decoding attribute data of point cloud data (S81002). A method for decoding point cloud data according to embodiments may further include a step of receiving a bitstream including encoded point cloud data and signaling information. In the present disclosure, geometry data is used interchangeably with geometry information and attribute data is used interchangeably with attribute information.

[0501] The step of receiving a bitstream including point cloud data and signaling information according to embodiments may be performed in the receiver (10005) of FIG. 1, the transmitter (20002) or decoding (20003) of FIG. 2, or the receiving unit (9000) or receiving processing unit (9001) of FIG. 9.

[0502] In order to decode geometry information and attribute information in the step of decoding geometry data of point cloud data (S81001) and the step of decoding attribute data of point cloud data (S81002) according to embodiments, a 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. 8, the point cloud video decoder of FIG. 9, the decoder of FIG. 13, the decoder of FIG. 14, the geometry decoder and the attribute decoder of FIG. 20, or the geometry decoder and the attribute decoder of FIG. 21 may be performed.

[0503] According to embodiments, the step (S81001) of decoding geometry data of the point cloud data entropy decodes the input geometry bitstream.

[0504] According to embodiments, the step of decoding geometry data of the point cloud data (S81001) may decode (i.e., restore) geometry information by applying intra prediction or inter prediction based on signaling information.

[0505] According to embodiments, the step of decoding geometry data of point cloud data (S81001) uses position information of a geometry reference frame stored in a reference frame buffer during inter prediction.

[0506] At this time, the reference frame buffer can manage the geometry reference frame and the attribute reference frame separately as shown in Fig. 12(a), or can manage them as an integrated reference frame as shown in Fig. 12(a). In the case of Fig. 12(a), both the position information of the geometry reference frame and the position information of the attribute reference frame are stored in the reference frame buffer. At this time, the attribute information corresponding to the position information of the attribute reference frame is also stored in the reference frame buffer. In contrast, in the case of Fig. 12(b), only the position information and attribute information of the geometry reference frame are stored in the reference frame buffer, and the position information of the attribute reference frame is not stored in the reference frame buffer. In this case, the reference frame generation unit reconstructs the position information of the attribute reference frame by applying one of non-sampling, down-sampling, partial sampling, or adaptive sampling based on the position information of the geometry reference frame stored in the reference frame buffer. That is, the reference frame generation unit can transfer the position information of the geometry reference frame stored in the reference frame buffer, the position information of the attribute reference frame, and the attribute information to the required module, or transfer the geometry reference frame and the attribute information stored in the reference frame buffer, and the position information of the attribute reference frame reconstructed by applying one of non-sampling, down-sampling, partial sampling, or adaptive sampling to the required module.

[0507] That is, in the present disclosure, when only the position information of the geometry reference frame is stored in the reference frame buffer, the reference frame generation unit may reconstruct the attribute reference frame by applying at least one of a non-sampling, down-sampling, partial sampling, or adaptive sampling method when reconstructing the attribute reference frame based on the position information of the geometry reference frame. In one embodiment, the reference frame generation unit may generate the position information of the attribute reference frame by applying a non-sampling reference frame generation method based on the position information of the geometry reference frame according to signaled buffer-related option information as shown in FIGS. 22 to 25 . In another embodiment, the reference frame generation unit may generate the position information of the attribute reference frame by applying a down-sampling reference frame generation method based on the position information of the geometry reference frame according to signaled buffer-related option information. In yet another embodiment, the reference frame generation unit may generate the position information of the attribute reference frame by applying a partial sampling reference frame generation method based on the position information of the geometry reference frame according to signaled buffer-related option information. In another embodiment, the reference frame generation unit may generate position information of an attribute reference frame by applying an adaptive sampling reference frame generation method based on position information of a geometry reference frame according to signaled buffer-related option information. In addition, the reference frame generation unit may utilize an indexing-based reference frame management method according to signaled buffer-related option information. A more detailed explanation has already been provided above, so it will be omitted here.

[0508] The step of decoding the above attribute information decodes (i.e., decompresses) the attribute information by applying intra prediction or inter prediction to the attribute information based on the restored geometry information.

[0509] In one embodiment, for intra prediction, the attribute information may be decoded by using one or a combination of one or more of RAHT coding, LOD-based predictive transform coding, and lifting transform coding.

[0510] In another embodiment, in the case of inter prediction, the position information and attribute information of an attribute reference frame stored in a reference frame buffer may be provided through a reference frame generation unit, or the position information of an attribute reference frame reconstructed by applying one of non-sampling, down-sampling, partial sampling, or adaptive sampling to the attribute information stored in the reference frame buffer and the reference frame generation unit may be provided through a reference frame generation unit.

[0511] The method for decoding point cloud data according to embodiments may further include a rendering step.

[0512] The rendering step according to the embodiments may restore point cloud data based on restored (or reconstructed) geometry information and attribute information and render it 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 at the vertex position, or circles centered at the vertex position. All or a portion of the rendered point cloud content is provided to the user through a display (e.g., VR / AR display, general display, etc.). The rendering step according to the embodiments may be performed in the renderer (10007) of FIG. 1, the renderer (20004) of FIG. 2, or the renderer (9011) of FIG. 9.

[0513] As described above, when storing reference frames in a buffer for inter prediction, the present disclosure may store information in two different coordinate systems for a single reference frame, or only one of the two. If only one of the two is stored, the decoder must perform additional coordinate transformation, which increases computational load and complexity. In addition, storing both types of information may increase memory usage, placing a burden on the decoder. If bi-prediction is applied, more calculations and more memory usage may be required. In addition, there may be cases where global / local motion is applied only to geometry or only to attributes. In these cases, the number of reference frames may also increase.

[0514] In order to solve the above problems, embodiments of the present disclosure support a reference frame buffer management method for applying a prediction compression technique through a reference frame to a point cloud captured by a spinning lidar and having multiple frames, as described above.

[0515] Therefore, these embodiments can minimize computational load and memory usage. Furthermore, by adjusting the precision of the reference frame, they can also provide noise removal for noisy data, thereby improving compression efficiency for inter-prediction of point cloud data.

[0516] Accordingly, the transmission method / device according to the embodiments can efficiently compress point cloud data to transmit the data, and by transmitting signaling information for this, the reception method / device according to the embodiments can also efficiently decode / restore point cloud data.

[0517] 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.

[0518] 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.

[0519] 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.

[0520] 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.

[0521] 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.

[0522] 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.

[0523] 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".

[0524] 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.

[0525] 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.

[0526] 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.

[0527] 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.

[0528] The best mode for carrying out the invention has been specifically described.

[0529] It will be apparent to those skilled in the art that various modifications and variations can be made to the present embodiments without departing from the spirit or scope of the present embodiments. Accordingly, the present embodiments are intended to include modifications and variations of the present embodiments provided they come within the scope of the appended claims and their equivalents.

Claims

1. A step of decoding geometry data of point cloud data in a bitstream; and A step of decoding attribute data of the above point cloud data; comprising; How to decode.

2. In paragraph 1, The above geometry data is decoded based on intra prediction or inter prediction, The above attribute data is decoded based on intra prediction or inter prediction. A frame including the decoded geometry data is stored in a buffer as position information of a geometry reference frame for inter prediction of the next frame. How to decode.

3. In paragraph 2, Based on the signaled received buffer-related option information and the position information of the geometry reference frame stored in the buffer, the position information of the attribute reference frame is reconstructed and applied to the inter prediction of the attribute data of the next frame. How to decode.

4. In paragraph 3, Reconstructing the position information of the attribute reference frame by applying one of non-sampling, down-sampling, partial sampling, or adaptive sampling based on the above buffer-related option information and the position information of the geometry reference frame. How to decode.

5. In paragraph 4, The above down sampling is a method of applying sampling to all groups when reconstructing an attribute reference frame through the geometry reference frame, the partial sampling is a method of applying sampling to some groups when reconstructing an attribute reference frame through the geometry reference frame, and the adaptive sampling is a method of applying a different sampling technique to each group when reconstructing an attribute reference frame through the geometry reference frame. How to decode.

6. In paragraph 5, The method for reconstructing the position information of the attribute reference frame varies depending on whether the above geometry reference frame is an orthogonal coordinate system or an angular coordinate system. How to decode.

7. In paragraph 6, The above buffer-related option information includes type information for identifying a method applied to reconstruct position information of the attribute reference frame and identification information for identifying whether the geometry reference frame is an orthogonal coordinate system or an angular coordinate system. How to decode.

8. Memory; and At least one processor connected to the memory; wherein the at least one processor comprises: Decoding geometry data of point cloud data in the bitstream; and Decoding attribute data of the above point cloud data; configured to do so, Decoding device.

9. A step of encoding the geometry data of the point cloud data; and A step of encoding attribute data of the above point cloud data; comprising; Encoding method.

10. In paragraph 9, The above geometry data is encoded based on intra prediction or inter prediction, The above attribute data is encoded based on intra prediction or inter prediction. A frame including the geometry data restored after the encoding is stored in a buffer as position information of a geometry reference frame for inter prediction of the next frame. Encoding method.

11. In paragraph 10, Based on the position information of the geometry reference frame stored in the above buffer, the position information of the attribute reference frame is reconstructed and applied to the inter prediction of the attribute data of the next frame. Encoding method.

12. In paragraph 11, Reconstructing the position information of the attribute reference frame by applying one of non-sampling, down-sampling, partial sampling, or adaptive sampling based on the position information of the geometry reference frame. Encoding method.

13. Memory; and At least one processor connected to the memory; wherein the at least one processor comprises: Encoding the geometry data of point cloud data; and Encoding attribute data of the above point cloud data; configured to do so; Encoding device.

14. A computer-readable storage medium storing a bitstream generated by the method according to Article 9.

15. Step of obtaining bitstream for point cloud data; The bitstream is generated based on the steps of encoding geometry data of the point cloud data; and encoding attribute data of the point cloud data; and A method comprising the step of transmitting data including the bitstream.

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