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

Abstract

A decoding method and device according to embodiments are disclosed. The decoding method according to embodiments may comprise the steps of: decoding geometry data of point cloud data in a bitstream; and decoding attribute data of the point cloud data, wherein the step of decoding the attribute data includes the steps of: generating LODs on the basis of points; retrieving the nearest neighbor point of a point to be attribute-decoded in a first retrieval area including the point to be attribute-decoded; and additionally retrieving the nearest neighbor point in a second retrieval area according to the number of nearest neighbor points retrieved in the step.

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 for increasing the compression efficiency and speed of G-PCC for 3D point cloud data compression.

[0007] 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 encoding technology of attribute information of G-PCC to enhance the compression performance and speed of point clouds.

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

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

[0010] According to embodiments, the decoded geometry data may include position information of points, and the decoded attribute data may include attributes of the points.

[0011] According to embodiments, the step of decoding the attribute data may include the step of generating LODs (Level Of Detail) based on the points, the step of performing a nearest neighbor point search of the point to be attribute decoded within a first search area including the point to be attribute decoded, and the step of additionally performing a nearest neighbor point search within a second search area according to the number of nearest neighbor points searched in the step.

[0012] According to embodiments, the step of performing the nearest neighbor point search may further include the step of calculating a Molton code based on the three-dimensional coordinates of each point and assigning the Molton code to each point.

[0013] According to embodiments, the step of assigning the Molton code may include downscaling the three-dimensional coordinates of each point by applying a scaling factor, and then assigning the Molton code to the three-dimensional coordinates of each downscaled point.

[0014] According to embodiments, the step of assigning the Molton code may perform downscaling by applying different scaling factors to each axis of the three-dimensional coordinates of each point.

[0015] According to embodiments, the step of assigning the Molton code may perform downscaling by applying the same scaling factor to each axis of the three-dimensional coordinates of each point.

[0016] According to embodiments, the bitstream may include parameter information related to the downscaling.

[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 may include position information of points, and the attribute data may include attributes of the points.

[0020] According to embodiments, the step of decoding the attribute data may include the step of generating LODs (Level Of Detail) based on the points, the step of performing a nearest neighbor point search of the point to be attribute decoded within a first search area including the point to be attribute decoded, and the step of additionally performing a nearest neighbor point search within a second search area according to the number of nearest neighbor points searched in the step.

[0021] According to embodiments, the step of performing the nearest neighbor point search may further include the step of calculating a Molton code based on the three-dimensional coordinates of each point and assigning the Molton code to each point.

[0022] According to embodiments, the step of assigning the Molton code may include downscaling the three-dimensional coordinates of each point by applying a scaling factor, and then assigning the Molton code to the three-dimensional coordinates of each downscaled point.

[0023] 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 to encode attribute data of the point cloud data.

[0024] According to embodiments, a computer-readable storage medium can store a bitstream generated by the encoding method.

[0025] According to embodiments, the method may include: obtaining a bitstream for point cloud data, wherein the bitstream is 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; and transmitting data including the bitstream.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] The point cloud data transmission method, transmission device, point cloud data reception method, and reception device according to embodiments can perform optimized neighboring point search by modifying the atlas search area through at least one of the Moulton code scaling method, the atlas search area expansion method, and the axis removal method during the nearest neighbor point search performed in the LOD generation process. This can reduce the time required for the nearest neighbor point search, thereby increasing the attribute encoding / decoding speed.

[0032] The drawings are included to further understand the embodiments, and the drawings illustrate the embodiments together with the description related to the embodiments.

[0033] Figure 1 illustrates an example of a point cloud content provision system according to embodiments.

[0034] FIG. 2 is a block diagram illustrating a point cloud content provision operation according to embodiments.

[0035] FIG. 3 illustrates an example of a point cloud encoder according to embodiments.

[0036] Figure 4 illustrates examples of octree and occupancy codes according to embodiments.

[0037] Figure 5 shows an example of a point configuration by LOD according to embodiments.

[0038] Figure 6 shows an example of a point configuration by LOD according to embodiments.

[0039] Fig. 7 illustrates an example of a point cloud decoder according to embodiments.

[0040] Figure 8 is an example of a transmission device according to embodiments.

[0041] Fig. 9 is an example of a receiving device according to embodiments.

[0042] Fig. 10 shows an example of a structure that can be linked with a point cloud data transmission / reception method / device according to embodiments.

[0043] FIG. 11 is a diagram showing an example of atlas search according to embodiments.

[0044] FIG. 12 is a diagram showing an example of a Molton code on a two-dimensional plane according to embodiments.

[0045] FIG. 13 is a drawing showing another example of a point cloud transmission device according to embodiments.

[0046] Fig. 14 is a block diagram showing an example of an attribute information encoding unit according to embodiments.

[0047] FIG. 15 is a drawing showing another example of a point cloud receiving device according to embodiments.

[0048] Fig. 16 is a block diagram showing an example of an attribute information decoding unit according to embodiments.

[0049] Fig. 17 is a block diagram showing an example of an LOD generation unit according to embodiments.

[0050] Fig. 18 is a flowchart showing an example of a method for assigning and sorting a molton code of a molton code assignment and sorting unit according to embodiments.

[0051] Fig. 19 is a flowchart showing an example of a nearest neighbor search method of a nearest neighbor search unit according to embodiments.

[0052] Fig. 20 shows an example of a bitstream structure of point cloud data for transmission / reception according to embodiments.

[0053] FIG. 21 is a diagram showing an example of a syntax structure of an attitude parameter set according to embodiments.

[0054] FIG. 22 is a diagram showing an example of a syntax structure of an attribute data unit header according to embodiments.

[0055] Fig. 23 is a flowchart showing an example of a point cloud data encoding method according to embodiments.

[0056] Fig. 24 is a flowchart showing an example of a point cloud data decoding method according to embodiments.

[0057] 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.

[0058] 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.

[0059] 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 illustrate 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.

[0060] FIG. 1 illustrates an example of a point cloud content provision system according to embodiments.

[0061] 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.

[0062] 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.

[0063] 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.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] 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)).

[0068] 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).

[0069] 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.

[0070] 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.

[0071] 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.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] FIG. 2 is a block diagram illustrating a point cloud content provision operation according to embodiments.

[0079] 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).

[0080] 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.).

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.).

[0086] 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.

[0087] FIG. 3 illustrates an example of a point cloud encoder according to embodiments.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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).

[0100] 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.

[0101] 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).

[0102] The attribute transformation unit (30007) can search for neighboring points within a specific position / radius from the position of the center point of each voxel based on the KD tree or the Moulton code. The KD tree is a binary search tree that supports a data structure that can manage points based on their positions to enable fast nearest neighbor search (NNS). The Moulton code represents the coordinate values ​​(e.g. (x, y, z)) representing the 3D positions of all points as bit values ​​and is generated by mixing the bits. For example, if the coordinate values ​​representing the position of a point are (5, 9, 1), the bit values ​​of the coordinate values ​​are (0101, 1001, 0001). If the bit values ​​are mixed in the order of z, y, and x according to the bit index, it is 010001000111. If this value is expressed in decimal, it is 1095. That is, the Moulton code value of the point with coordinate values ​​(5, 9, 1) is 1095. The attribute transformation unit (30007) can sort points based on the Moulton code value and perform nearest neighbor search (NNS) through a depth-first traversal process. After the attribute transformation operation, if nearest neighbor search (NNS) is also required in other transformation processes for attribute coding, a KD tree or Moulton code is utilized.

[0103] As shown in the drawing, the converted attributes are input to the RAHT conversion unit (30008) and / or the LOD generation unit (30009).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] The coefficient quantization unit (30011) according to the embodiments quantizes attribute-coded attributes based on coefficients.

[0108] An arithmetic encoder (30012) according to embodiments encodes quantized attributes based on arithmetic coding.

[0109] 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).

[0110] Figure 4 illustrates examples of octree and occupancy codes according to embodiments.

[0111] 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.

[0112] 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.

[0113]

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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-sub 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).

[0122] 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.

[0123]

[0124] 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.

[0125] [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)

[0126] The upsampling process is performed to voxelize a triangle by adding points in the middle along the edges. Additional points are generated based on an 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).

[0127] Figure 5 shows an example of a point configuration by LOD according to embodiments.

[0128] 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.

[0129] 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.

[0130] Figure 6 shows an example of a point configuration by LOD according to embodiments.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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);}}

[0137] int PCCInverseQuantization(int value, int quantStep) {if( quantStep ==0) {return value;} else {return value * quantStep;}}

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 3) Create temporary arrays called updateweight and update and initialize them to 0.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] A point cloud encoder according to embodiments (e.g., a 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 region, 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.

[0147] 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.

[0148]

[0149] 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:

[0150]

[0151] The gDC values ​​are also quantized and entropy coded, like the high-pass coefficients.

[0152] Fig. 7 illustrates an example of a point cloud decoder according to embodiments.

[0153] 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.

[0154] As described in Figure 1, the point cloud decoder can perform geometry decoding and attribute decoding. Geometry decoding is performed before attribute decoding.

[0155] 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).

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] The arithmetic decoder (7005), the inverse quantization unit (7006), the RAHT transform unit (7007), the LOD generation unit (7008), the inverse lifting unit (7009), and / or the color inverse transform unit (7010) can perform attribute decoding. The attribute decoding according to the embodiments can include RAHT (Region Adaptive Hierarchical Transform) decoding, prediction transform (Interpolaration-based hierarchical nearest-neighbor prediction-Prediction Transform) decoding, and lifting transform (interpolation-based hierarchical nearest-neighbor prediction with an update / lifting step (Lifting Transform)) decoding. The three decodings described above can be used selectively, or a combination of one or more decodings can be used. In addition, the attribute decoding according to the embodiments is not limited to the above-described examples.

[0163] An arithmetic decoder (7005) according to embodiments decodes an attribute bitstream using arithmetic coding.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] Figure 8 is an example of a transmission device according to embodiments.

[0169] 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).

[0170] 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).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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).

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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).

[0183] 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.

[0184] A slice is a series of syntax elements that represent all or part of a coded point cloud frame.

[0185] 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.

[0186] Fig. 9 is an example of a receiving device according to embodiments.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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).

[0191] 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.

[0192] 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).

[0193] 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).

[0194] The inverse quantization processing unit (9005) according to the embodiments can inverse quantize the decoded geometry.

[0195] 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.

[0196] 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.

[0197] 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).

[0198] 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).

[0199] 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.

[0200] Fig. 10 shows an example of a structure that can be linked with a point cloud data transmission / reception method / device according to embodiments.

[0201] 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.

[0202] 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.

[0203] 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).

[0204] 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.

[0205] 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.

[0206] <PCC+XR>

[0207] 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.

[0208] 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.

[0209] <PCC+XR+모바일폰>

[0210] The XR / PCC device (1030) can be implemented as a mobile phone (1040) or the like by applying PCC technology.

[0211] The mobile phone (1040) can decode and display point cloud content based on PCC technology.

[0212] <PCC+자율주행+XR>

[0213] Autonomous vehicles (1020) can be implemented as mobile robots, vehicles, unmanned aerial vehicles, etc. by applying PCC technology and XR technology.

[0214] 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.

[0215] 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 provide passengers with XR / PCC objects corresponding to real objects or objects on a screen by having a HUD to output XR / PCC images.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] The PCC method / device according to the embodiments can be applied to a vehicle providing an autonomous driving service.

[0221] Vehicles providing autonomous driving services are connected to PCC devices to enable wired / wireless communication.

[0222] 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.

[0223] As mentioned above, a point cloud is composed of a collection of points, each of which may have geometry information and attribute information. The geometry information is the 3D position (XYZ) information of each point, and the attribute information is the color (RGB, YUV, etc.) and / or reflectance value of each point.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] According to embodiments, a nearest neighbor search process is required to generate a LoD (Level of Details) for a predlift transform and / or a lifting transform, and the prior art has used an atlas-based initial search. In the present disclosure, the initial search may be referred to as an initial search or an atlas search. In this case, the initial search, i.e., the atlas search, is a method of finding neighbors in a 3D space rather than in the order of the Moulton code, and the search is performed within the atlas search area. However, when a fixed atlas search area is used, there may be no points within the search area depending on the density of the point cloud, and if the initial search fails, a time-consuming extended search may be used, which may significantly increase the overall encoding / decoding time. In the present disclosure, the extended search may be referred to as a full search.

[0228] The present disclosure relates to a device and method for improving the speed and performance of attribute information. More specifically, the present disclosure relates to a nearest neighbor search method and device for increasing the compression efficiency of attributes and reducing the time required for encoding / decoding processes when generating level of detail (i.e., LOD) during the encoding and decoding processes of attribute information. In particular, the present disclosure proposes a method for setting an atlas search area and a signaling method by utilizing the density of a point cloud.

[0229] According to embodiments, point cloud data in the present disclosure may be captured by an RGB-D camera or a LiDAR device. The present disclosure relates to a device and method for generating a Level Of Detail (LOD) of a lifting transformation or a prediction transformation used for compressing attribute information of point cloud content captured by a LiDAR RGB-D camera or a LiDAR device, thereby improving speed and compression ratio by generating an LOD suited to the characteristics of the point cloud content.

[0230] The LOD generation method of the present disclosure can be applied identically or similarly to the encoder of a transmitting device and the decoder of a receiving device.

[0231] There may be three LOD generation methods according to embodiments. For example, the three methods may include a distance-based method, a sampling-based method, and an octree-based method. First, the distance-based method is a method of classifying LODs based on the distance between points, and the sampling-based method is a method of sorting points by Morton code, selecting (i.e., sampling) every Xth corresponding point, and classifying it into a lower LOD that can be a candidate for a neighboring set. And, the octree-based method is a method of constructing an octree for points, and selecting (i.e., sampling) points close to the center according to the node level of the octree, and classifying them into a lower LOD. In the present disclosure, the octree may be referred to as an occupancy tree.

[0232] Then, neighboring points within each LOD are searched to generate predictors for points sampled (or selected) using one of the three LOD generation methods above. The search continues until the three nearest neighboring points are found.

[0233] According to embodiments, there may be two search methods for finding nearest neighbor points. One is an atlas search (also called an initial search) method that specifies an area surrounding each point and searches only points within that area, and the other is a full search (also called an extended search) method that searches all surrounding points. According to embodiments, the two search methods are performed sequentially, with the atlas search performed first, and if three nearest neighbor points are not found in the atlas search, the full search process is performed.

[0234] The advantage of atlas search is that it preemptively searches a narrow area, and if the three nearest neighboring points are found, the entire search process can be skipped, significantly reducing the time required. However, if the atlas search is limited to a fixed range, the probability of finding neighboring points is low, and even if found, they are often inaccurate points that reduce the predictor's prediction efficiency. Conversely, a full search selects points with high accuracy by searching the entire area, but this process requires a significant amount of time.

[0235] Fig. 11 is a diagram illustrating an example of atlas search according to embodiments. That is, if a neighboring point search is performed when there are no neighboring points within the atlas search range as shown in Fig. 11, the atlas search proceeds unnecessarily and the entire search process proceeds, resulting in a significant delay.

[0236] Accordingly, the present disclosure aims to improve speed and compression efficiency by addressing the shortcomings of the nearest neighbor point search process in point cloud encoding / decoding, which requires fast and accurate encoding / decoding. The present disclosure applies an improved neighbor search technique to accelerate the encoding / decoding process and, at the same time, improve the compression ratio.

[0237] The neighbor search method of the present disclosure is performed in both the encoder of the transmitting device and the decoder of the receiving device.

[0238] Below is a detailed explanation of how to navigate the Atlas.

[0239] The atlas search according to the embodiments is a method of searching by setting an area including the point (e.g., the gray area (41001) in FIG. 11) based on the point for which the nearest neighbor point is being found, as in the example of FIG. 11, and setting an area of ​​the same size as the area in all directions. That is, when the area of ​​the size to which the point belongs is referred to as a mini-cube, a total of 27 mini-cubes are searched, including the mini-cube to which the point belongs. The area for these 27 mini-cubes can be referred to as an atlas search range. Generally, the size of the mini-cube is set to 2x2x2, so that a maximum of 8 points are within the mini-cube, in which case the size of the atlas search area becomes 8x8x8.

[0240] And, when setting up the atlas search area, we utilize the Molton code owned by each point.

[0241] FIG. 12 is a diagram showing an example of a Molton code on a two-dimensional plane according to embodiments. That is, a Molton code is one of space-filling curves, as shown in FIG. 12, and in general, in attribute compression of a G-PCC encoder / decoder, a Molton code is assigned by utilizing the three-dimensional coordinates of each point, and attribute encoding and attribute decoding are performed by arranging the points in the Molton code order. According to embodiments, attribute encoding may be performed in an encoder of a transmitting device, and attribute decoding may be performed in a decoder of a receiving device. In this way, the encoder / decoder can determine the relative positions and directions of surrounding points based on the Molton code, thereby setting a search area, and points within the search area become targets of atlas search. However, as shown in FIG. 11, there may be no points within the atlas search area.

[0242] When performing atlas search according to embodiments, the criterion for determining the closest neighboring point among neighboring points is distance. When the coordinates of the current point are (x, y, z) and the coordinates of the searched neighboring point are (x', y', z'), the distance D between the two points can be obtained as shown in the following mathematical equation 1.

[0243] [Mathematical Formula 1]

[0244] D = |xx' | + |yy' | +|zz' |

[0245] In Equation 1, the point with the smallest distance, or closest point, is called the nearest neighbor. The encoder / decoder first searches for three nearest neighbor points within the atlas search area. If there are fewer than three points within the atlas search area, the three nearest neighbor points cannot be found, and thus the full search step proceeds.

[0246] The following describes an atlas search method based on Moulton code scaling.

[0247] According to embodiments, when obtaining a Morton code from an encoder / decoder, it may be efficient to downscale position information. According to embodiments, the present disclosure provides that when assigning a Morton code to a point whose coordinates are (x, y, z), if the Morton code scaling coefficient (or scaling factor) is N, the Morton code can be assigned in a state where the coordinates of the corresponding point are downscaled by N. In addition, when a function for assigning a Morton code is expressed as mortonAddr(), the Morton code assigned through the Morton code scaling technique can be expressed as in Mathematical Expression 2 below. Mathematical Expression 2 is an example of obtaining a Morton code in an atlas search method based on Morton code scaling. In Mathematical Expression 2, the coordinate values ​​representing the positions of actual points do not change, and the downscaled coordinates are used when assigning a Morton code. In mathematical expression 2, N corresponds to the numerator, d corresponds to the exponent of the denominator 2, and can be understood as an integer downscaling that does not generate a decimal point. In other words, the coordinate value is N / 2. d It has the same effect as discarding the decimal point after multiplying.

[0248] [Equation 2]

[0249] MortonCode=mortonAddr(((x × N)>>d,(y × N)>>d,( z × N)>>d))

[0250] For example, assuming that the xyz coordinates of four points are (0,0,0), (0,0,1), (0,1,0), (1,0,0), that is, 0,1,2,3, when N and d are 1 and downscaling is performed by applying mathematical expression 2, the xyz coordinates of the four points will have the same value, for example, (0,0,0). That is, even if the attribute values ​​of the four points are different, the same Moulton code can be assigned. In other words, the present disclosure does not actually downscale the coordinates of the points, but downscales the coordinate values ​​of the points when assigning the Moulton codes and then assigns the Moulton codes to the coordinate values. By doing so, the atlas search area changes and more points are included in the atlas search area. Taking Fig. 1 as an example, by downscaling the position information of points by applying mathematical equation 2 and assigning a Moulton code in the downscaled state, points that were outside the atlas search area are included in the atlas search area. At this time, the scaling coefficient (or scaling factor) can be included in the signaling information and transmitted to the receiving device. In this case, the decoder of the receiving device performs scaling based on the scaling coefficient (or scaling factor) included in the signaling information.

[0251] As explained in the atlas search method above, the atlas search area is set based on the Moulton code. Applying Moulton code scaling, as shown in Equation 2, allows for more points to be included within the atlas search area. Furthermore, including more points within the atlas search area can have the following two effects.

[0252] First, since the atlas search generates more nearest neighbor point candidates, the likelihood of finding the three nearest neighbor points increases without resorting to a full search. Furthermore, by avoiding the time-consuming full search process, the overall encoding / decoding time can be reduced. Furthermore, by addressing the problem of selecting inaccurate neighbor points when there are insufficient nearest neighbor point candidates, the accuracy of the resulting predictor can be improved, thereby enhancing attribute compression performance.

[0253] The following describes how to set multiple scaling factors. That is, how to set multiple scaling factors in the atlas search method based on Moulton code scaling.

[0254] According to embodiments, when performing Morton code scaling in the present disclosure, rather than using the same scaling factor for the coordinates of all axes, it may be efficient to use different scaling factors for each axis. For example, when assigning a Morton code to a point whose coordinates are (x, y, z) by setting the scaling factors to (a, b, c), scaling by a (e.g., downscaling) may be applied to x, scaling by b (e.g., downscaling) may be applied to y, and scaling by c (e.g., downscaling) may be applied to z. This process can be expressed through the following mathematical expression 3. As described in the above Morton code scaling-based atlas search method, in the mathematical expression 3, a, b, and c correspond to the numerator, respectively, and d corresponds to the exponent of the denominator 2. That is, the mathematical expression 3 below is an example of obtaining a Morton code by utilizing multiple scaling factors in the Morton code scaling-based atlas search method.

[0255] [Equation 3]

[0256] MortonCode=mortonAddr(((x × a)>>d,(y × b)>>d,( z × c)>>d))

[0257] The following describes how to set up point alignment after Molton code scaling. In other words, this describes how to align points after Molton code scaling in an atlas search method based on Molton code scaling.

[0258] If the sorting order between points according to the embodiments is based on the Molton code, the sorting order between points may be affected when using the Molton code scaling method. That is, when downscaling coordinate values, points with different locations but overlapping Molton codes may occur. In this state, when the Molton code-based sorting is performed, the order between points with overlapping Molton codes is the same as the output order of the geometry decoder. In other words, the sorting is performed based on the Molton code order between points with different Molton codes, and the sorting is performed based on the output order of the geometry decoder between points with overlapping Molton codes. This mixed order-based sorting may be effective, or another order-based sorting method may be effective.

[0259] The present disclosure may set the sorting order to the output order of the geometry decoder, or may set the sorting order to the order of the Moulton code.

[0260] The following describes an atlas search method based on the expansion of the search area.

[0261] According to embodiments, a method for expanding an atlas search area in an atlas search in the present disclosure may be efficient. In the present disclosure, if the atlas search area expansion factor is N, the size of one minicube is (2 N , 2 N , 2 N ) can be used to expand the atlas search area. At this time, since the atlas search area corresponds to the size of minicube 27, as the size of the minicube increases, the atlas search area also expands.

[0262] In the present disclosure, the atlas search area expansion coefficient can be set as follows. For example, a user can set a value in advance and signal it to a decoder. That is, the encoder of a transmitting device can signal the value set in advance by the user and transmit it to the decoder of a receiving device. As another example, the atlas search area coefficient can be set by reflecting the density characteristics of the point cloud content input by the encoder. Then, the set atlas search area coefficient can be signaled and transmitted to the decoder of the receiving device. At this time, the density determination criterion may be the number of points within a specific area. Alternatively, the density determination criterion may be the average distance between all points.

[0263] As the atlas search area expands, the number of candidates for nearest neighbor points increases, resulting in similar benefits to the Moulton code scaling-based atlas search method described above. Specifically, it reduces the need for full searches, which speeds up encoding / decoding and reduces the number of incorrect nearest neighbor points selected.

[0264] The following describes how to set the multi-region expansion factor. That is, it describes how to set the multi-region expansion factor in the atlas search method based on the search area expansion.

[0265] Similar to the method of setting multiple scaling factors described above, multiple domain expansion factors can also be applied individually for each x, y, and z axis. According to embodiments, if the domain expansion factors are Nx, Ny, and Nz for each XYZ (i.e., x, y, and z) axis, the size of the minicube is (2 Nx , 2 Ny , 2 Nz ) and through this, the atlas search area can be expanded. These coefficients can also be user-specified or reflect density characteristics, as in the atlas search method based on search area expansion.

[0266] The following describes an atlas search method based on axis removal.

[0267] According to embodiments, in the present disclosure, when the coordinates are different based on a specific axis during atlas search, a method of removing the point from the nearest neighbor search target may be efficient. For example, if an axis removal method is used based on the Z axis, when the coordinates of the current point are (x, y, z) and the coordinates of a neighboring point within the atlas search range are (x', y', z'), if the values ​​of z and z' are different, the point can be excluded from the nearest neighbor target candidate without measuring the distance to the neighboring point.

[0268] This type of axis-removal-based atlas search method can speed up encoding / decoding by eliminating the need to calculate distances to points with different values ​​along a specific axis. Furthermore, if the point cloud content has attribute values ​​(e.g., color or reflectance) that vary significantly along a specific axis regardless of distance, axis-removal-based atlas search can prevent the selection of inaccurate nearest neighbor points.

[0269] The following describes a point-count-based axis removal method. That is, it describes how to perform axis removal based on the point count in an axis removal-based atlas search method.

[0270] In other words, it may be effective to reflect the number of points within the atlas search area in the axis removal criteria. For example, it may be efficient to apply an axis removal method when the number of points within the atlas search area is N or less, without unconditionally excluding neighboring points with different coordinates relative to a specific axis.

[0271] FIG. 13 is a drawing showing another example of a point cloud transmission device according to embodiments.

[0272] The elements of the point cloud transmission device illustrated in FIG. 13 may be implemented by hardware, software, a processor connected to a memory, and / or a combination thereof. That is, the elements of the point cloud transmission device illustrated in FIG. 13 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 illustrated in FIG. 13. 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 illustrated in FIG. 13. The execution order of each block in FIG. 13 may be changed, some blocks may be omitted, and some blocks may be newly added.

[0273] The point cloud transmission device of FIG. 13 can correspond to the point cloud video encoder (10002) of FIG. 1, the encoding (20001) of FIG. 2, the point cloud video encoder of FIG. 3, the transmission device of FIG. 8, the device of FIG. 10, etc. That is, point cloud data (or referred to as PCC data) input to the point cloud transmission device (or referred to as PCC data encoder) of FIG. 13 is encoded and output as a geometry information bitstream and an attribute information bitstream. In the present disclosure, the geometry information bitstream may be used interchangeably with the same meaning as the geometry bitstream, and the attribute information bitstream may be used interchangeably with the same meaning as the attribute bitstream.

[0274] In Fig. 13, 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 may perform part or all of the operations of the data input unit (8000) of Fig. 8. And 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 geometry information transformation quantization processing unit (51003) may perform part or all of the operations of the quantization unit (30001) of Fig. 3, or may perform part or all of the operations of the quantization processing unit (8001) of Fig. 8.

[0275] According to embodiments, the data input unit (51001) can receive data to encode point cloud data. That is, the data input unit (51001) can read and configure the input data (e.g., ply, configuration file, etc.). For example, the input data can be geometry data (which can be referred to as geometry, geometry information, etc.), attribute data (which can be referred to as attribute, attribute information, etc.), parameter information indicating coding-related settings, etc.

[0276] The 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.

[0277] The 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 (scale = geometry quantization value) setting. At this time, the scale value can follow the set value or be included in the bitstream as parameter information and transmitted to the receiving end.

[0278] The spatial division unit (51004) can spatially divide the point cloud data quantized and output from the quantization processing unit (51003) into one or more three-dimensional blocks based on a bounding box and / or a sub-bounding box. For example, the 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.

[0279] 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).

[0280] In the present disclosure, 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. That is, a tile is divided (partitioned) into one or more slices, so that a point cloud video encoder can encode point cloud data in parallel.

[0281] In the present disclosure, a slice may refer to a unit of data (or bitstream) that can be independently encoded by a point cloud video encoder according to embodiments and / or a unit of data (or bitstream) that can be independently decoded by 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 tile according to embodiments may be divided into one or more slices based on the number of points, and some data may be split or merged during the division process. In other words, a slice may be a unit that can be independently coded within the corresponding tile. A tile divided into space in this way may be further divided into one or more slices for fast and efficient processing.

[0282] 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.

[0283] The positions of one or more 3D blocks (e.g., slices) space-divided in the above space division unit (51004) are output to a geometry information encoding unit (51005), and attribute information (i.e., divided attribute original data) is output to a color conversion processing unit (51008) for attribute encoding. The positions may be position 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.

[0284] The above geometry information encoding unit (51005) performs inter-prediction or intra-prediction-based encoding on positions (i.e., geometry information) output from the space division unit (51004) to generate a geometry information bitstream. Intra-prediction-based coding methods may include octree coding, predictive tree coding, trisoup coding, and the like.

[0285] In the present disclosure, a reference frame generation unit (51007) generates a reference frame for encoding based on inter prediction based on one or more reference frames stored in a reference frame buffer (51006). The reference frame may be provided to a geometry information encoding unit (51005) for inter prediction encoding of geometry information and / or may be provided to an attribute information encoding unit (51010) for inter prediction encoding of attribute information. That is, the reference frame generation unit (51007) may store restored geometry information and restored attribute information in the reference frame buffer (51006) and transmit reference frame data to another module. The frame buffer (51006) may store a frame reconstructed based on the restored geometry information and the restored attribute information as a reference frame.

[0286] According to embodiments, the geometry information encoding unit (51005) may compress geometry information by performing inter prediction based on a reference frame provided by the reference frame generation unit (51007). That is, the geometry information encoding unit (51005) may perform octree-based inter-coding, predictive-tree-based inter-coding, or trisoup-based inter-coding based on a difference in geometry prediction values ​​between a current frame and a reference frame.

[0287] In addition, the geometry information encoding unit (51005) reconstructs (i.e., restores) the encoded geometry information based on inter prediction or intra prediction and provides it to the recoloring unit (5100). In addition, the reconstructed (i.e., restored) geometry information is stored in the reference frame buffer (51006) as position information of the reference frame.

[0288] The above geometry information encoding unit (51005) can perform entropy encoding on geometry information coded based on intra prediction or inter prediction.

[0289] The color conversion processing unit (51008) performs color conversion coding to convert the color values ​​included in the original attribute data divided by the space division unit (51004). For example, the color conversion processing unit (51008) can support attribute type conversion, such as changing RGB color to YUV or YCbCr. The operation of the color conversion processing unit (51008) can be optionally applied depending on the color values ​​included in the attribute information.

[0290] The above color readjustment unit (51009) performs color readjustment (or recoloring) to reset the attribute (color) based on the reconstructed (i.e., restored) geometry information. That is, when a scale is applied to the geometry information and the position information value is changed, the color readjustment unit (51009) can predict an attribute value appropriate for the changed position. For example, the color readjustment unit (51009) can transform the attribute of a point at a position based on the position value of the point included in the voxel.

[0291] The attribute information encoding unit (51010) generates an attribute information bitstream by performing inter-prediction or intra-prediction-based encoding on the attribute information output from the color readjustment unit (51009). According to embodiments, the intra-prediction coding method performed by the attribute information encoding unit (51010) may include a Predicting Transform coding method, a Lift Transform coding method, a RAHT coding method, etc. According to embodiments, the attribute information encoding unit (51010) may perform inter-prediction based on a reference frame generated by the reference frame generation unit (51007) to compress color-converted and / or color-readjusted attribute information. That is, the attribute information encoding unit (51010) may receive color-readjusted original attribute information, restored geometry information, and a reference frame as input, perform encoding, and generate an attribute information bitstream.

[0292] The attribute information encoding unit (51010) reconstructs (i.e., restores) attribute information encoded based on inter prediction or intra prediction and stores it in the reference frame buffer (51006). In addition, the attribute information encoding unit (51010) may perform entropy encoding on attribute information encoded based on inter prediction or intra prediction.

[0293] Meanwhile, the geometry information bitstream compressed and output from the geometry information encoding unit (51005) and the attribute information bitstream compressed and output from the attribute information encoding unit (51010) may be transmitted separately, or may be multiplexed into a single bitstream and transmitted. In addition, each bitstream or multiplexed bitstream may be encapsulated into a file or segment (e.g., streaming segment) and then transmitted through various networks such as a broadcasting network and / or a broadband network.

[0294] Fig. 14 is a block diagram showing an example of an attribute information encoding unit according to embodiments. Each element of the attribute information encoding unit of Fig. 14 may correspond to hardware, software, a processor, and / or a combination thereof. That is, in the present disclosure, the attribute information encoding unit (51010) may compress attribute information by combining one or more of RAHT coding, LOD-based lifting transform coding, and LOD-based predictive transform coding as described above.

[0295] In other words, in the present disclosure, the attribute information encoding unit (51010) can compress the attribute information (or segmented point cloud data) segmented in the space segmentation unit (51004) by using one or more of RAHT coding, LOD-based lifting transformation coding, and LOD-based predictive transformation coding.

[0296] According to embodiments, the attribute information encoding unit (51010) of the present disclosure can generate LOD when LOD parameters exist. To this end, the judgment unit (or judgment step) (53001) of the attribute information encoding unit (51010) checks whether LOD parameters exist. That is, if the judgment unit (53001) determines that LOD parameters exist, the color-converted and / or color-rescaled attribute information (or segmented point cloud data) is provided to the LOD generation unit (53002), and if the judgment unit (53001) determines that there are no LOD parameters, it is provided to the RAHT unit (53005).

[0297] The LOD generation unit (53002) above can receive segmented point cloud data (i.e., attribute information) as input and generate one or more LODs, and can determine the nearest neighbors for each point used in lifting transformation and prediction transformation based on the atlas search method described above (e.g., Moulton code scaling-based atlas search method, search area expansion-based atlas search method, axis removal-based atlas search method). At this time, the LOD generation unit (53002) can be performed when there is an LOD parameter. The LOD generated by the LOD generation unit (53002) is provided to the lifting transformation unit (53003) and / or the prediction transformation unit (53004).

[0298] The lifting transformation unit (53003) performs a prediction and update process for each LOD level using the LOD generated by the LOD generation unit (53002), and through this, performs a frequency transformation on the attribute information to generate a transformation coefficient. The transformation coefficient generated by the lifting transformation unit (53003) can be quantized and transmitted to the attribute information entropy encoding unit (53006). In addition, the restored attribute information can be output by internally performing an inverse transformation.

[0299] The above prediction transformation unit (53004) can perform a prediction transformation to predict the current point using the neighboring points determined in the LOD generation process of the LOD generation unit (53002). In the present disclosure, the difference between the predicted attribute information and the original attribute information can be referred to as a transformation coefficient. The prediction transformation unit (53004) can quantize the transformation coefficient and transmit it to the attribute information entropy encoding unit (53006). In addition, the attribute information can be restored and output through inverse quantization and prediction inverse transformation.

[0300] The above RAHT unit (53005) can be performed when there is no LOD parameter. The RAHT unit (53005) can receive segmented point cloud data (i.e., attribute information) and perform RAHT (Region Adaptive Hierarchical Transform) to generate transform coefficients converted to the frequency domain. Thereafter, the transform coefficients can be quantized and passed to the attribute information entropy encoding unit (53006).

[0301] The above attribute information entropy encoding unit (53006) applies any one of RAHT coding, LOD-based lifting transform coding, and LOD-based predictive transform coding to entropy encode compressed attribute information and outputs it in the form of an attribute information bitstream.

[0302] FIG. 15 is a drawing showing another example of a point cloud receiving device according to embodiments.

[0303] The elements of the point cloud receiving device illustrated in FIG. 15 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. 15 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. 15. 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. 15. The execution order of each block in FIG. 15 may be changed, some blocks may be omitted, and some blocks may be newly added.

[0304] The point cloud receiving device of FIG. 15 may correspond to the point cloud video decoder (10006) of FIG. 1, the decoding (20003) of FIG. 2, the point cloud video decoder of FIG. 7, the receiving device of FIG. 9, the device of FIG. 10, etc. 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, a decoder, etc.

[0305] The receiving device of FIG. 15 may further include a receiving processing unit. The receiving processing unit may receive one bitstream, or may separately receive a geometry information bitstream (also referred to as a geometry bitstream) and an attribute information bitstream (also referred to as an attribute bitstream). The receiving processing unit according to the embodiments may, when a file and / or segment is received, decapsulate the received file and / or segment and output it as a bitstream.

[0306] According to the embodiments, when one bitstream is received (or decapsulated), the receiving processing unit can demultiplex a geometry information bitstream and an attribute information bitstream from the one bitstream, and output the demultiplexed geometry information bitstream to a geometry information decoding unit (61001) and the attribute information bitstream to an attribute information decoding unit (61005).

[0307] According to the embodiments, when a geometry information bitstream and an attribute information bitstream are each received (or decapsulated), the receiving processing unit can transmit the geometry information bitstream to a geometry information decoding unit (61001) and the attribute information bitstream to an attribute information decoding unit (61005).

[0308] The above geometry information decoding unit (61001) can receive a geometry information bitstream, decode it, and restore the geometry information. That is, the geometry information decoding unit (61001) can restore the geometry information by performing the reverse process of the geometry information encoding unit (51005) of FIG. 13 based on signaling information (e.g., geometry-related parameters) on the compressed geometry information bitstream. More specifically, the geometry information decoding unit (61001) entropy decodes the geometry information bitstream and then performs inter-prediction-based or intra-prediction-based geometry decoding. According to embodiments, if intra-prediction-based encoding is applied to the geometry information at the transmitting end, the geometry information decoding unit (61001) performs intra-prediction-based restoration on the entropy-decoded geometry information. Conversely, if encoding based on inter prediction is applied to geometry information on the transmitting side, the geometry information decoding unit (61001) performs restoration based on inter prediction on the entropy-decoded geometry information.

[0309] The geometry information restored (or reconstructed) in the above geometry information decoding unit (61001) is provided to the coordinate system inversion unit (61002) and the attribute information decoding unit (61005).

[0310] The above coordinate system inversion unit (61002) can perform the reverse process of the coordinate system transformation performed by the coordinate system transformation unit of the transmission device on the geometry information. For example, the coordinate system inversion unit (61002) can restore the xyz axes changed on the transmission side or inversely transform the transformed coordinate system into the xyz orthogonal coordinate system.

[0311] According to embodiments, the receiving device may further include a geometry information inverse transformation inverse quantization processing unit between the geometry information decoding unit (61001) and the coordinate system inverse transformation unit (61002).

[0312] The above geometry information inverse transformation inverse quantization processing unit can restore the scale (scale = geometry quantization value) signaled and received from the transmitting device and apply it to the geometry location x, y, and z values ​​of the restored point to restore it. That is, the geometry information inverse transformation inverse quantization processing unit can 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 the 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 can 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.

[0313] The above attribute information decoding unit (61005) can restore attribute information by performing the reverse process of the attribute information encoding unit (51010) of FIG. 13 based on signaling information (e.g., attribute-related parameters) and reconstructed (i.e., restored) geometry for the compressed attribute information bitstream. According to embodiments, if point cloud data is divided into tile and / or slice units at the transmitting end, the geometry information decoding unit (61001) and the attribute information decoding unit (61005) can perform geometry decoding and attribute decoding in tile and / or slice units. More specifically, the attribute information decoding unit (61005) entropy decodes the input attribute information bitstream and then performs inter-prediction-based or intra-prediction-based attribute decoding. According to embodiments, if intra prediction-based encoding is applied to attribute information at the transmitting side, the attribute information decoding unit (61005) performs intra prediction-based restoration on the entropy-decoded attribute information. Conversely, if inter prediction-based encoding is applied to the attribute information at the transmitting side, the attribute information decoding unit (61005) performs inter prediction-based restoration on the entropy-decoded attribute information.

[0314] The attribute information restored by the attribute information decoding unit (61005) may be provided to the color inverse conversion processing unit (61006) to be restored to RGB color. That is, the color inverse conversion processing unit (65006) may perform inverse conversion coding to inversely convert the color value (or texture) included in the restored attribute information.

[0315] According to embodiments, the geometry information restored by the geometry information decoding unit (61001) and the attribute information restored by the attribute information decoding unit (61005) are stored in a reference frame buffer (61003). The reference frame buffer (61003) can store a frame reconstructed based on the restored geometry information and the restored attribute information as a reference frame. That is, the restored geometry information and the restored attribute information stored in the reference frame buffer (63009) can be used as a previous reference frame for geometry information inter-prediction and / or attribute information inter-prediction.

[0316] In the present disclosure, a reference frame generation unit (61004) generates a reference frame for inter-prediction-based decoding based on one or more reference frames stored in a reference frame buffer (61003). The reference frame may be provided to a geometry information decoding unit (61001) for inter-prediction decoding of geometry information and / or may be provided to an attribute information decoding unit (61005) for inter-prediction decoding of attribute information. That is, the reference frame generation unit (61004) may store restored geometry information and restored attribute information in the reference frame buffer (61003) and transmit reference frame data to other modules.

[0317] Fig. 16 is a block diagram showing an example of an attribute information decoding unit according to embodiments. Each element of the attribute information decoding unit of Fig. 16 may correspond to hardware, software, a processor, and / or a combination thereof. That is, in the present disclosure, the attribute information decoding unit (61005) may restore attribute information by combining one or more of RAHT decoding, LOD-based lifting transform decoding, and LOD-based predictive transform decoding, as described above.

[0318] According to embodiments, the attribute information entropy decoding unit (63001) can receive an attribute information bitstream, perform entropy decoding, and restore a transform coefficient. The restored transform coefficient can be provided to the LOD generation unit (63003) or the inverse RAHT unit (63006) depending on the presence or absence of an LOD parameter.

[0319] According to embodiments, the attribute information decoding unit (61005) of the present disclosure can generate LOD when LOD parameters exist. To this end, the judgment unit (or judgment step) (63002) of the attribute information decoding unit (61005) checks whether LOD parameters exist. That is, if the judgment unit (63002) determines that LOD parameters exist, the entropy-decoded and restored transform coefficients are provided to the LOD generation unit (63003), and if the judgment unit (63002) determines that LOD parameters do not exist, they are provided to the inverse RAHT unit (63006).

[0320] The LOD generation unit (63003) above can generate one or more LODs based on restored geometry information, restored transformation coefficients, and signaling information, and can determine the nearest neighboring points for each point used in lifting inverse transformation and predictive inverse transformation based on the atlas search method described above (e.g., Moulton code scaling-based atlas search method, search area expansion-based atlas search method, axis removal-based atlas search method). At this time, the LOD generation unit (63003) can be performed when there is an LOD parameter. The LOD generated by the LOD generation unit (63003) is provided to the lifting inverse transformation unit (63004) and / or the predictive inverse transformation unit (63005).

[0321] The above lifting inverse transform unit (63004) inversely transforms the input transform coefficient and performs a prediction and update process for each LOD level using the LOD generated by the LOD generation unit (63003), thereby performing frequency inverse transform on the transform coefficient to restore attribute information.

[0322] The above prediction inverse transformation unit (63005) can perform a prediction transformation to predict the current point using the neighboring points determined in the LOD generation process of the LOD generation unit (63003). That is, the prediction inverse transformation unit (63005) can restore attribute information by combining the value predicted for the current point using the neighboring points determined in the LOD generation process with the value obtained by inversely transforming the input transformation coefficient.

[0323] The above-described reverse RAHT unit (63006) can be performed when there is no LOD parameter. The above-described reverse RAHT unit (63006) can receive restored geometry information and transformation coefficients and perform the reverse process of RAHT to restore attribute information.

[0324] Fig. 17 is a block diagram showing an example of an LOD generation unit according to embodiments. In the present disclosure, the LOD generation unit may be provided in both a transmitting device and a receiving device. That is, the LOD generation unit of Fig. 17 may correspond to the LOD generation unit (53002) of Fig. 14 and / or the LOD generation unit (63003) of Fig. 16. Each element of the LOD generation unit of Fig. 17 may correspond to hardware, software, a processor, and / or a combination thereof.

[0325] In Fig. 17, the LOD generation unit can input restored geometry information and generate and output multi-layer LODs.

[0326] To this end, the LOD generation unit may include a Molton code assignment and alignment unit (65001), a sub-sampling unit (65002), and a nearest neighbor search unit (65003).

[0327] The above-mentioned Molton code assignment and sorting unit (65001) assigns Molton codes to points, and can then sort the points assigned Molton codes according to a predetermined sorting method.

[0328] The above sub-sampling unit (65002) can perform sub-sampling on the LOD index list to distinguish the current points into LODs.

[0329] The nearest neighbor search unit (65003) above can find one or more neighboring points for each point based on geometry and store the index of the point in order to predict and update the lifting transformation for points classified by LOD. The attribute information of the nearest neighbor points found by the nearest neighbor search unit (65003) above is used to predict the attribute of the point to be encoded or decoded.

[0330] The following is a detailed description of the Molton code assignment and alignment section (65001).

[0331] That is, the Molton code assignment and sorting unit (65001) can assign Molton codes to points and sort points according to a set sorting order.

[0332] According to embodiments, the Morton code assignment and sorting unit (65001) can assign Morton codes to points through Morton code scaling described in the above-described Morton code scaling-based atlas search method. That is, the Morton code assignment and sorting unit (65001) can receive information about scaling factors or multiple scaling factors as signaling information through the method described in the above-described Morton code scaling-based atlas search method. Then, the received information and the coordinate values ​​of the points can be utilized to assign Morton codes to the points.

[0333] According to embodiments, the Molton code assignment and sorting unit (65001) may sort points in one of a mixed order (i.e., refer to the point sorting method setting after the Molton code scaling described above), the Molton code order, and the geometry decoder output order. That is, the Molton code assignment and sorting unit (65001) may receive information about the sorting order as signaling information through the method described in the point sorting method setting after the Molton code scaling. In addition, the points may be sorted by utilizing the received information and points.

[0334] Fig. 18 is a flowchart showing an example of a method for assigning and sorting a molton code of a molton code assignment and sorting unit according to embodiments.

[0335] According to embodiments, if the value of aps_morton_code_scaling_enabled included in the signaling information is true (e.g., 1) (67001), it is checked whether the value of multi_scale_factor_enabled is true (i.e., 1) (67002).

[0336] According to the embodiments, if the value of aps_morton_code_scaling_enabled is true and the value of multi_scale_factor_enabled is true, different downscalings are applied to each XYZ of the coordinate values ​​(67003), and then the process proceeds to the Morton code calculation and assignment step (67005).

[0337] According to embodiments, if the value of aps_morton_code_scaling_enabled is true and the value of multi_scale_factor_enabled is false (e.g., 0), the same downscaling is applied to the coordinate values ​​(67004), and then the process proceeds to the Morton code calculation and assignment step (67005).

[0338] In some embodiments, if the value of aps_morton_code_scaling_enabled is false, the process proceeds to the Morton code calculation and assignment step (67005) without applying downscaling.

[0339] Since the detailed description of downscaling in step 67003 or 67004 has been provided above, it will be omitted here to avoid redundant description.

[0340] In the above step 67005, the Molton code is obtained according to the Molton code calculation method described above, and the Molton code is assigned to the points.

[0341] Then, check if the value of different_sorting_enabled is true (i.e. 1) (67006).

[0342] In some embodiments, if the value of different_sorting_enabled is false (e.g., 0), the points are sorted based on the order of the Molton codes if the Molton codes between points are different, and the points are sorted based on the order of the geometry decoder output if the Molton codes between points are the same (67007).

[0343] In some embodiments, if the value of different_sorting_enabled is true, it is checked whether the sorting method is in Molton code order (67008).

[0344] In embodiments, if the value of different_sorting_enabled is true and the sorting method is not a Moulton code order, the points are sorted based on the geometry decoder output order (67009).

[0345] According to embodiments, if the value of different_sorting_enabled is true and the sorting method is Molton code order, points are sorted based on Molton code order (67010).

[0346] The sorted points, i.e., the sorted point information, in one of the above steps 67007, 67009, and 67010 are sub-sampled in the sub-sampling unit (65002) and then provided to the nearest neighbor search unit (65003).

[0347] In the present disclosure, the syntax elements (or fields) of FIG. 18, namely aps_morton_code_scaling_enabled, multi_scale_factor_enabled, and different_sorting_enabled, can be referred to as signaling information related to atlas search. A detailed description of each syntax element will be provided later. In the present disclosure, the signaling information related to atlas search can be referred to as parameter information for a fast nearest neighbor search technique for LOD generation.

[0348] The following is a detailed description of the nearest neighbor search unit (65003).

[0349] According to embodiments, the nearest neighbor search unit (65003) can set an atlas search area by utilizing the Moulton code of each point.

[0350] That is, the nearest neighbor search unit (65003) performs an atlas search by utilizing the set atlas search area, and if three nearest neighbor points are not found in the atlas search, a full search can be performed.

[0351] According to embodiments, the nearest neighbor search unit (65003) may expand the atlas search area using the aforementioned atlas search area method (i.e., refer to the search area expansion-based atlas search method). That is, information regarding the atlas search area expansion coefficient determined through the method described in the aforementioned search area expansion-based atlas search method may be received as signaling information. In addition, the atlas area may be expanded using the received information.

[0352] According to embodiments, the nearest neighbor search unit (65003) may utilize an axis removal method (i.e., refer to an axis removal-based atlas search method) to exclude points within an atlas search area from the neighbor point search target if the values ​​of a specific axis (one of x, y, and z) are different. That is, information on an axis to be removed and information on conditions for performing the axis removal method can be received as signaling information through the method described in the aforementioned axis removal-based atlas search method. In addition, the axis removal method can be used by utilizing the received information.

[0353] Fig. 19 is a flowchart showing an example of a nearest neighbor search method of a nearest neighbor search unit according to embodiments.

[0354] According to embodiments, the nearest neighbor search unit receives sub-sampled point information and sets an atlas search area (69001).

[0355] Then, check whether the value of enlarge_atlas_search_range_enabled included in the signaling information is true (e.g., 1) (69002).

[0356] In some embodiments, if the value of enlarge_atlas_search_range_enabled is true, it is checked whether the value of multi_atlas_enlarge_factor_enabled is true (e.g., 1) (69003).

[0357] In some embodiments, if the value of enlarge_atlas_search_range_enabled is false (e.g., 0), the atlas search range is not expanded and step 69007 is proceeded to.

[0358] According to embodiments, if the value of enlarge_atlas_search_range_enabled is true and the value of multi_atlas_enlarge_factor_enabled is true, the atlas search range is expanded using the received XYZ-specific multi-area expansion factor (69004).

[0359] In some embodiments, if the value of enlarge_atlas_search_range_enabled is true and the value of multi_atlas_enlarge_factor_enabled is false, the atlas search range is expanded by one atlas area expansion factor (69005).

[0360] Then, check if the value of discard_different_coordinate_neighbor_enabled is true (e.g., 1) (69006).

[0361] According to embodiments, if the value of discard_different_coordinate_neighbor_enabled is false, the process of excluding at least one point within the atlas search area from the neighbor point search target based on the axis removal method is omitted.

[0362] In some embodiments, if the value of discard_different_coordinate_neighbor_enabled is true, it is checked whether the number of points within the atlas search range is greater than the value of number_of_points_for_coordinate_discard (69007).

[0363] According to embodiments, if the value of discard_different_coordinate_neighbor_enabled is true and the number of points in the atlas search area is greater than the value of number_of_points_for_coordinate_discard, if the received axis-based coordinate values ​​among the XYZ axes are different, the neighboring point search target is excluded (69008).

[0364] According to embodiments, if the value of discard_different_coordinate_neighbor_enabled is true and the number of points within the atlas search area is not greater than the value of number_of_points_for_coordinate_discard, the process of excluding at least one point within the atlas search area from the neighbor point search target based on the axis removal method is omitted.

[0365] Then, a search is performed within the atlas search area or the extended atlas search area to find the nearest neighbor points (69009).

[0366] At this time, check whether the number of nearest neighbor points found in step 69009 is less than 3 (69010). If it is less than 3, perform a full search (69011). If it is not less than 3, do not perform a full search.

[0367] According to embodiments, a predictor is generated based on nearest neighbor points found by performing an atlas search or nearest neighbor points found by performing both an atlas search and a full search (69012).

[0368] In the present disclosure, the syntax elements (or fields) of FIG. 19, namely enlarge_atlas_search_range_enabled, multi_atlas_enlarge_factor_enabled, discard_different_coordinate_neighbor_enabled, and number_of_points_for_coordinate_discard, may be referred to as signaling information related to atlas search. A detailed description of each syntax element will be provided later. In the present disclosure, the signaling information related to atlas search may be referred to as parameter information for a fast nearest neighbor search technique for LOD generation.

[0369] To perform the aforementioned atlas search embodiments, relevant information may be signaled. The present disclosure may refer to this relevant information as "signaling information related to atlas search." Signaling information according to the embodiments may be used by a transmitter, receiver, or the like.

[0370] According to embodiments, the encoded point cloud configuration is as follows. That is, a point cloud data encoder that performs a geometry encoding and / or attribute encoding process can generate an encoded point cloud (or a bitstream of point cloud data) as in FIG. 20. In addition, signaling information regarding the point cloud data (e.g., including signaling information related to atlas search) can be generated and processed by a metadata processing unit (or signaling processing unit) of a point cloud data transmission device and included in a bitstream of the point cloud data as in FIG. 20 and transmitted to a receiving device.

[0371] Fig. 20 shows an example of a bitstream structure of point cloud data for transmission / reception according to embodiments.

[0372] In some embodiments, the term “slice” in FIG. 20 may be referred to as the term “data unit.”

[0373] Also, in FIG. 20, 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).

[0374] 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.

[0375] The point cloud video encoder according to the embodiments can generate a bitstream as in FIG. 20 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 information encoding unit, an attribute information encoding unit, and a metadata processing unit (or signaling processing unit) of the point cloud video encoder and included in the bitstream.

[0376] 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. 20. 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 a bitstream of the point cloud data as illustrated in FIG. 20.

[0377] Signaling information according to embodiments may be received / obtained by at least one of a geometry information decoding unit, an attribute information decoding unit, and a metadata parser (or signaling processing unit) of a point cloud video decoder.

[0378] The bitstream according to the embodiments may be transmitted / received by being divided into a geometry information bitstream, an attribute information bitstream, and a signaling information bitstream, or may be transmitted / received by being combined into a single bitstream.

[0379] When a geometry information bitstream, an attribute information bitstream, and a signaling information 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).

[0380] 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 (Prediction Unit) data (geom PU data).

[0381] 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).

[0382] Some or all of the signaling information related to atlas search according to embodiments, i.e., parameter information for a fast nearest neighbor search technique for LOD generation, may be signaled by being added to the APS and / or attribute data unit header. In the present disclosure, the attribute data unit header may be referred to as an attribute slice header.

[0383] As shown in Fig. 20, 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.

[0384] The transmitting device according to the embodiments transmits point cloud data according to the bitstream structure as illustrated in FIG. 20, 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.

[0385] The receiving device according to the embodiments receives point cloud data according to the structure of the bitstream as illustrated in FIG. 20, 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.

[0386] 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.

[0387] The term field, used in the syntaxes of the present disclosure described hereinafter, may have the same meaning as a parameter or syntax element.

[0388] FIG. 21 is a diagram illustrating an example of a syntax structure of an attribute parameter set (attribute_parameter_set()) (APS) including parameter information for a fast nearest neighbor search technique for LOD generation according to embodiments. The parameter information for the fast nearest neighbor search technique for LOD generation of the present disclosure can be added to and signaled in the APS. That is, the parameter information for the fast nearest neighbor search technique for LOD generation can be efficiently signaled to the APS to support the fast nearest neighbor search method for LOD generation. The name of the signaling information can be understood within the scope of the meaning and function of the signaling information.

[0389] In Figure 21, the aps_attr_parameter_set_id field indicates the identifier of the APS for reference by other syntax elements. The value of aps_attr_parameter_set_id must be in the range of 0 to 15 (inclusive).

[0390] The aps_seq_parameter_set_id field indicates the sps_seq_parameter_set_id value for the active SPS. The value of aps_seq_parameter_set_id must be in the range 0 to 15, inclusive.

[0391] aps_morton_code_scaling_enabled is a flag that selects whether to perform Morton code scaling. For example, if the value of aps_morton_code_scaling_enabled is 1 (i.e., true), it indicates that Morton code scaling (e.g., downscaling) is performed (or selected), and if it is 0 (i.e., false), it indicates that Morton code scaling is not performed (or selected).

[0392] The attr_coding_type field indicates the coding type for the attribute. For example, a value of 0 for attr_coding_type indicates RAHT, a value of 1 indicates a LOD-based prediction transformation, and a value of 2 indicates a LOD-based lifting transformation.

[0393] You can specify the maximum size of the per-point predictor set by adding 1 to pred_set_size_minus1.

[0394] aps_extension_present specifies whether the aps_extension_data syntax elements are present within the APS syntax structure. aps_extension_present may be 0 in bitstreams conforming to this version of this document.

[0395] prediction_with_distribution_enabled specifies whether prediction coefficients are derived based on the spatial distribution of predictors. For example, a value of 1 (i.e., true) for prediction_with_distribution_enabled indicates that prediction coefficients are derived, while a value of 0 (i.e., false) indicates that prediction coefficients are not derived. If prediction_with_distribution_enabled does not exist, its value is assumed to be 0.

[0396] enlarge_atlas_searchrange_enabled is a flag that selects whether to perform the atlas search range expansion method. For example, a value of enlarge_atlas_searchrange_enabled of 1 indicates that the atlas search range expansion method is performed (or selected), while a value of 0 indicates that it is not performed (or selected).

[0397] discard_different_coordinate_neighbor_enabled is a flag that selects whether to perform the method of removing neighbors with different coordinates. For example, a value of discard_different_coordinate_neighbor_enabled of 1 indicates that the method of removing neighbors with different coordinates is performed (or selected), while a value of 0 indicates that the method of removing neighbors with different coordinates is not performed (or selected).

[0398] In some embodiments, prediction_with_distribution_enabled, enlarge_atlas_searchrange_enabled, and discard_different_coordinate_neighbor_enabled may be signaled to the APS when the value of attr_coding_type is 1 (e.g., LOD-based prediction transformation) or 2 (e.g., LOD-based lifting transformation) and the value of pred_set_size_minus1 is 2 or greater (i.e., the size of the predictor set is 3 or greater).

[0399] FIG. 22 is a diagram illustrating an example of the syntax structure of an attribute data unit header (attribute_data_unit_header()) that includes parameter information for a fast nearest neighbor search method for LOD generation according to embodiments. The name of the signaling information can be understood within the scope of the meaning and function of the signaling information.

[0400] The ash_attr_parameter_set_id field indicates the value of aps_attr_parameter_set_id for the active APS.

[0401] adu_temporal_id specifies the temporal ID of the frame associated with the attribute data unit.

[0402] 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.

[0403] The ash_attr_geom_slice_id field specifies the gsh_slice_id value of the active geometry slice header.

[0404] According to embodiments, the attribute data unit header may further include different_sorting_enabled, multi_scaling_factor_enabled, and morton_code_scaling_denominator if the value of morton_code_scaling_enabled is true.

[0405] different_sorting_enabled is a flag that selects whether to use a different sorting method when performing Moulton code scaling. For example, a value of 1 for different_sorting_enabled indicates that a different sorting method is used, while a value of 0 indicates that it is not used.

[0406] multi_scaling_factor_enabled is a flag that selects whether to apply different scaling factors to the x-axis, y-axis, and z-axis respectively. For example, if the value of multi_scaling_factor_enabled is 1, it indicates that different scaling factors are applied to the x-axis, y-axis, and z-axis respectively, and if it is 0, it indicates that the same scaling factor is applied to all of the x-axis, y-axis, and z-axis.

[0407] morton_code_scaling_denominator is the value corresponding to d in Equations 2 and 3.

[0408] In some embodiments, the attribute data unit header may further include sorting_method_after_morton_code_scaling if the value of different_sorting_enabled is true.

[0409] sorting_method_after_morton_code_scaling specifies the sorting method before subsampling input when performing Morton code scaling. For example, a value of sorting_method_after_morton_code_scaling of 0 indicates a sorting based on the order output from the geometry decoder, and a value of 1 indicates a Morton code-based sorting without applying Morton code scaling.

[0410] According to embodiments, the attribute data unit header may further include multi_morton_code_scaling_factor_xyz[k] if the value of multi_scaling_factor_enabled is true, and may further include single_morton_code_scaling_factor if the value of multi_scaling_factor_enabled is false.

[0411] multi_morton_code_scaling_factor_xyz[k] is the Morton code scaling factor to be applied to the kth xyz coordinate. Here, it means the scaling factor to be applied to the x-coordinate when k==0, to the y-coordinate when k==1, and to the z-coordinate when k==2. For a detailed description, refer to the above-mentioned multi-scaling factor setting method and mathematical expression 3.

[0412] single_morton_code_scaling_factor represents a Morton code scaling factor to be applied commonly to xyz coordinates. For a detailed description, refer to the aforementioned Morton code scaling-based atlas search method and mathematical expression 2.

[0413] In some embodiments, the attribute data unit header may further include multi_atlas_enlarge_factor_enabled if the value of enlarge_atlas_searchrange_enabled is true.

[0414] multi_atlas_enlarge_factor_enabled indicates whether to apply multiple atlas search area expansion factors. For example, if the value of multi_atlas_enlarge_factor_enabled is 1, it is a flag that selects whether to apply different scaling factors to the x-axis, y-axis, and z-axis. For example, if the value of multi_scaling_factor_enabled is 1, it indicates that different atlas search area expansion factors are applied to the x-axis, y-axis, and z-axis, and if it is 0, it indicates that the same atlas search area expansion factor is applied to the x-axis, y-axis, and z-axis.

[0415] According to embodiments, the attribute data unit header may further include multi_atlas_enlarge_factor_xyz[k] if the value of multi_atlas_enlarge_factor_enabled is 1, and may further include single_atlas_enlarge_factor if the value is 0.

[0416] multi_atlas_enlarge_factor_xyz[k] represents the multi-atlas search area expansion factors, which increase the size of the minicube from (2,2,2) to (2multi_atlas_enlarge_factor_xyz[0], 2multi_atlas_enlarge_factor_xyz[1], 2multi_atlas_enlarge_factor_xyz[2]). For detailed description, refer to the multi-area expansion factor setting method described above.

[0417] single_atlas_enlarge_factor represents a single atlas search area expansion factor, which increases the size of a minicube from (2,2,2) to (2single_atlas_enlarge_factor, 2single_atlas_enlarge_factor, 2single_atlas_enlarge_factor). For a detailed description, refer to the aforementioned search area expansion-based atlas search method.

[0418] In some embodiments, the attribute data unit header may further include number_of_points_for_coordinate_discard and coordinate_to_discard if the value of discard_different_coordinate_neighbor_enabled is true.

[0419] number_of_points_for_coordinate_discard specifies the conditions for performing axis discard when using the axis discard method. For example, the axis discard method is performed only when the number of points within the atlas search area is greater than number_of_points_for_coordinate_discard. For a detailed description, see Axis Discard-Based Atlas Search Method.

[0420] coordinate_to_discard specifies which of the xyz axes to remove when using the axis removal method. For example, a value of coordinate_to_discard of 0 indicates removal of the x-axis, 1 indicates removal of the y-axis, and 2 indicates removal of the z-axis.

[0421] Fig. 23 shows a flowchart of a point cloud data encoding method according to embodiments.

[0422] A point cloud data encoding method according to embodiments may include a step of encoding geometry data of point cloud data (S71001) and 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. At this time, the bitstream may be encapsulated and transmitted as a file. In the present disclosure, geometry data is used interchangeably with geometry information and the same meaning as attribute data is used interchangeably with the same meaning as attribute information. In addition, in the present disclosure, a geometry information bitstream is used interchangeably with a geometry bitstream and the same meaning as an attribute bitstream is used interchangeably with the same meaning as an attribute bitstream.

[0423] 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, and the transmitting device of FIG. 13 for encoding geometry data and attribute data.

[0424] 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.

[0425] According to embodiments, the step (S71001) of encoding geometry data of point cloud data uses positional information of a geometry reference frame stored in a reference frame buffer during inter prediction. The geometry information of each point compressed based on the inter prediction or intra prediction is entropy encoded and then output in the form of a geometry bitstream.

[0426] 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 reconstructed (or restored) geometry information to output an attribute bitstream.

[0427] According to embodiments, the attribute information may be coded using one or a combination of one or more of RAHT coding, LOD-based predictive transform coding, and lifting transform coding.

[0428] According to embodiments, in the case of inter prediction, a method of coding residual values ​​based on the difference in attribute prediction values ​​between a current frame and a motion-compensated reference frame may be included. At this time, a reference frame stored in a reference frame buffer may be provided and used for inter prediction. The attribute information compressed based on the intra prediction or inter prediction is entropy-encoded and then output in the form of an attribute bitstream.

[0429] According to embodiments, the step of encoding attribute data of point cloud data (S71002) may include an LOD generation process to perform LOD-based predictive transformation coding or LOD-based lifting transformation coding.

[0430] The LOD generation process generates one or more LODs based on one of the distance-based method, the sampling-based method, and the octree-based method. Then, in order to generate a predictor for the points to be encoded, neighboring points are searched within each existing LOD, and the search is continued until three nearest neighboring points are found. There are two search methods for finding the nearest neighboring points. There is an atlas search that searches only the points existing within the area surrounding each point by specifying the area centered on the point, and a full search that searches all points in the area. The two search methods are performed sequentially, with the atlas search performed first, and if three nearest neighboring points are not found in the atlas search, the full search process is performed.

[0431] In this disclosure, the search to find the nearest neighbor point is performed during the LOD generation process.

[0432] According to embodiments, the search method for finding the nearest neighbor point in the present disclosure may be performed by combining one or two or more of the above-described Moulton code scaling-based atlas search method, the search area expansion-based atlas search method, and the axis removal-based atlas search method.

[0433] The above-described atlas search method based on Molton code scaling is a method of performing an atlas search by downscaling position information when obtaining a Molton code from an encoder / decoder. According to embodiments, the present disclosure provides that when assigning a Molton code to a point whose coordinates are (x, y, z), if the Molton code scaling factor is N, the Molton code can be assigned in a state where the coordinates of the point are downscaled by N. That is, the coordinate values ​​indicating the positions of the actual points do not change, and the downscaled coordinates are used when assigning the Molton code.

[0434] As mentioned above, the atlas search area is set based on the Moulton code. If the coordinate values ​​of each point are downscaled and then assigned a Moulton code, as shown in Equation 2, more points can be included in the atlas search area. Furthermore, if more points are included in the atlas search area, the number of nearest neighbor point candidates increases, increasing the possibility of finding the three nearest neighbor points without proceeding to a full search. In addition, since the time-consuming full search process is not performed, the overall encoding / decoding time can be shortened. Furthermore, by compensating for the problem of selecting inaccurate neighbor points in situations where there are insufficient nearest neighbor point candidates, the accuracy of the predictor generated subsequently can be improved, thereby enhancing the performance of attribute compression.

[0435] In addition, when performing downscaling, the present disclosure may use different scaling factors for each axis, rather than using the same scaling factor for the coordinates of all axes of each point. For example, when assigning a Moulton code to a point whose coordinates are (x, y, z) by setting the scaling factors to (a, b, c), scaling (e.g., downscaling) by a may be applied to x, scaling (e.g., downscaling) by b may be applied to y, and scaling (e.g., downscaling) by c may be applied to z.

[0436] In addition, the present disclosure can sort points after downscaling the Moulton code based on a point sorting method. In the present disclosure, the sorting order can be set to the output order of the geometry decoder or to the Moulton code order.

[0437] According to embodiments, in the step (S71002) of encoding attribute data of point cloud data, the LOD generation and nearest neighbor point search methods are described based on the above-described Moulton code scaling-based atlas search method, search area expansion-based atlas search method, and axis removal-based atlas search method based on FIGS. 11 to 14, FIGS. 17 to 20, and mathematical expressions 1 to 3, and detailed descriptions thereof are omitted here to avoid redundant descriptions.

[0438] In the present disclosure, signaling information includes parameter information for a fast nearest neighbor search technique for LOD generation, and part or all of the parameter information for the fast nearest neighbor search technique for LOD generation can be signaled in an APS and / or attribute data unit header and transmitted to a receiving device. The parameter information for the fast nearest neighbor search technique for LOD generation is omitted here, with reference to the descriptions of FIGS. 21 and 22.

[0439] Figure 24 shows a flowchart of a point cloud data decoding method according to embodiments.

[0440] 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 has the same meaning as attribute information, and attribute data is used interchangeably with the same meaning as attribute information. In addition, in the present disclosure, a geometry information bitstream is used interchangeably with a geometry bitstream and has the same meaning as attribute bitstream, and an attribute information bitstream is used interchangeably with a same meaning as attribute bitstream.

[0441] 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.

[0442] In order to decode geometry information and attribute information, 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, or the receiving device of FIG. 15 may be performed 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.

[0443] According to embodiments, the step (S81001) of decoding geometry data of the point cloud data entropy decodes the input geometry bitstream.

[0444] 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.

[0445] 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.

[0446] 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.

[0447] In one embodiment, the attribute information may be decoded using one or more of RAHT coding, LOD-based predictive transform coding, and lifting transform coding. In another embodiment, in the case of inter prediction, a reference frame stored in a reference frame buffer may be provided and used for inter prediction.

[0448] According to embodiments, the step (S81002) of decoding attribute data of point cloud data may perform an LOD generation process and a nearest neighbor point search process based on signaling information including parameter information for a fast nearest neighbor search technique for LOD generation, in order to perform LOD-based predictive inverse transform decoding or LOD-based lifting inverse transform decoding.

[0449] In the step (S81002) of decoding attribute data of point cloud data of the present disclosure, the LOD generation process and the nearest neighbor point search process can be applied in the same manner as in the step (S71002) of encoding attribute data of point cloud data of the transmitting device.

[0450] Therefore, in the step of decoding attribute data of point cloud data (S81002), the LOD generation and nearest neighbor point search methods are described based on the above-described Moulton code scaling-based atlas search method, search area expansion-based atlas search method, and axis removal-based atlas search method based on FIGS. 11 to 12, FIGS. 15 to 20, and mathematical expressions 1 to 3, and detailed descriptions are omitted here to avoid redundant descriptions.

[0451] In the present disclosure, signaling information includes parameter information for a fast nearest neighbor search technique for LOD generation, and part or all of the parameter information for the fast nearest neighbor search technique for LOD generation can be signaled in an APS and / or attribute data unit header and transmitted to a receiving device. The parameter information for the fast nearest neighbor search technique for LOD generation is omitted here, with reference to the descriptions of FIGS. 21 and 22.

[0452] The method for decoding point cloud data according to embodiments may further include a rendering step.

[0453] 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.

[0454] As described above, the present disclosure can optimize nearest neighbor point search using atlas search area expansion and / or Moulton code scaling. Specifically, the present disclosure can support optimized neighbor point search by modifying the atlas search area based on the density of the point cloud. This reduces the time required for nearest neighbor point search, thereby accelerating attribute compression.

[0455] As described above, the PCC encoding method, PCC decoding method, and signaling method of the above-described embodiments can provide the following effects.

[0456] In other words, attribute compression of point clouds containing massive amounts of information is time-consuming. In particular, the LOD generation process performed during attribute compression involves searching for nearest neighbor points, significantly slowing down encoding / decoding speeds. This slow encoding / decoding speed becomes a problem in environments such as autonomous driving, which require accurate and low-latency encoding / decoding.

[0457] As described above, the present disclosure provides a high-speed nearest neighbor point search method that can be utilized in the LOD generation process of attribute encoding / decoding of point cloud data, thereby increasing attribute compression efficiency and reducing the time required for the encoding / decoding process. The present disclosure proposes a Moulton code scaling method, an atlas search area expansion method, and an axis removal method as the nearest neighbor point search method, thereby increasing the speed and accuracy of LOD generation.

[0458] In this way, the transmission method / device of the present disclosure 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.

[0459] 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.

[0460] 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.

[0461] 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.

[0462] 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.

[0463] 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.

[0464] 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.

[0465] 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".

[0466] 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.

[0467] 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.

[0468] 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.

[0469] 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.

[0470] The best mode for carrying out the invention has been specifically described.

[0471] 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 decoded geometry data includes location information of points, A decoding method wherein the above decoded attribute data includes attributes of the above points.

3. In the first paragraph, the step of decoding the attribute data comprises: A step of generating LODs (Level Of Detail) based on the above points, A step of performing a search for the nearest neighbor point of a point to be attribute decoded within a first search area including a point to be attribute decoded, and A decoding method comprising the step of additionally performing a nearest neighbor point search within a second search area according to the number of nearest neighbor points searched in the above step.

4. In the first paragraph, the step of performing the nearest neighbor point search is A decoding method further comprising a step of calculating a Molton code based on the three-dimensional coordinates of each point and assigning the Molton code to each point.

5. In the fourth paragraph, the step of assigning the Molton code is A decoding method for downscaling the three-dimensional coordinates of each point by applying a scaling factor, and then assigning a Morton code to the three-dimensional coordinates of each downscaled point.

6. In the fifth paragraph, the step of assigning the Molton code is A decoding method that performs downscaling by applying different scaling factors to each axis of the three-dimensional coordinates of each point above.

7. In the fifth paragraph, the step of assigning the Molton code is A decoding method that performs downscaling by applying the same scaling factor to each axis of the three-dimensional coordinates of each point above.

8. In paragraph 5, A decoding method wherein the bitstream includes parameter information related to the downscaling.

9. 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.

10. 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.

11. In paragraph 10, The above geometry data includes location information of points, and the above attribute data includes attributes of the points, The step of decoding the above attribute data is: A step of generating LODs (Level Of Detail) based on the above points, A step of performing a search for the nearest neighbor point of a point to be attribute decoded within a first search area including a point to be attribute decoded, and An encoding method comprising the step of additionally performing a nearest neighbor point search within a second search area according to the number of nearest neighbor points searched in the above step.

12. In the 11th paragraph, the step of performing the nearest neighbor point search is Further comprising a step of calculating a Molton code based on the three-dimensional coordinates of each point and assigning the Molton code to each point, The step of assigning the above Molton code is: An encoding method for downscaling the three-dimensional coordinates of each point by applying a scaling factor, and then assigning a Moulton code to the three-dimensional coordinates of each downscaled point.

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 10.

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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