Point cloud data processing device and method

Abstract

The point cloud data processing method according to the embodiment may include the following steps: encoding the point cloud data including the geometric information and the attribute information; and transmitting a bit stream including the encoded point cloud data.

Description

Technical Field

[0001] The present disclosure provides a method for providing point cloud content to provide various services such as virtual reality (VR), augmented reality (AR), mixed reality (MR), and self-driving services to users. Background Art

[0002] Point cloud content is content represented by a point cloud, which is a collection of points belonging to a coordinate system representing a three-dimensional space. Point cloud content can express media configured in three dimensions and is used to provide various services such as virtual reality (VR), augmented reality (AR), mixed reality (MR), and self-driving services. However, tens of thousands to hundreds of thousands of point data are required to represent point cloud content. Therefore, a method for efficiently processing a large amount of point data is needed. Summary of the invention

[0003] Technical issues

[0004] The embodiment provides an apparatus and method for efficiently processing point cloud data. The embodiment provides a method and apparatus for processing point cloud data to solve delay and encoding / decoding complexity.

[0005] The technical scope of the embodiments is not limited to the above-mentioned technical objectives, and can be extended to other technical objectives that can be inferred by those skilled in the art based on the entire content disclosed in this document.

[0006] Technical Solution

[0007] To achieve these objectives and other advantages and in accordance with the purposes of the present disclosure, in some embodiments, a method for transmitting point cloud data may include: encoding point cloud data including geometric information and attribute information, and transmitting a bitstream including the encoded point cloud data. In some embodiments, the geometric information represents the positions of points of the point cloud data, and the attribute information represents the attributes of the points of the point cloud data.

[0008] In some embodiments, a method for processing point cloud data may include: receiving a bitstream including point cloud data. In some embodiments, the point cloud data includes geometric information and attribute information, wherein the geometric information represents the location of points of the point cloud data, and the attribute information represents one or more attributes of the points of the point cloud data. The point cloud processing method may include decoding the point cloud data.

[0009] In some embodiments, a method for processing point cloud data may include receiving a bitstream including point cloud data and decoding the point cloud data. In some embodiments, the point cloud data includes geometric information and attribute information, wherein the geometric information represents the location of points of the point cloud data, and the attribute information indicates one or more attributes of the points of the point cloud data.

[0010] In some embodiments, an apparatus for processing point cloud data may include a receiver configured to receive a bitstream including point cloud data and a decoder configured to decode the point cloud data. In some embodiments, the point cloud data includes geometric information and attribute information, wherein the geometric information indicates the location of points of the point cloud data, and the attribute information indicates one or more attributes of the points of the point cloud data.

[0011] Beneficial Effects

[0012] The apparatus and method according to the embodiments can efficiently process point cloud data.

[0013] The apparatus and method according to the embodiments can provide high-quality point cloud services.

[0014] The device and method according to the embodiment may provide point cloud content for providing general services such as VR service and self-driving service. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this application, illustrate embodiments of the disclosure and together with the description serve to explain the principle of the disclosure.

[0016] In order to better understand the various embodiments described below, the following description of the embodiments should be referred to in conjunction with the accompanying drawings. In the accompanying drawings:

[0017] Figure 1 An exemplary point cloud content providing system according to an embodiment is shown.

[0018] Figure 2 is a block diagram illustrating point cloud content providing operations according to an embodiment.

[0019] Figure 3 An exemplary process for capturing point cloud video according to an implementation is shown.

[0020] Figure 4 An exemplary point cloud encoder according to an implementation is shown.

[0021] Figure 5 An example of a voxel according to an embodiment is shown.

[0022] Figure 6 An example of an octree and occupancy code according to an embodiment is shown.

[0023] Figure 7 An example of a neighbor node pattern according to an embodiment is shown.

[0024] Figure 8 An example of point configuration in each LOD according to an embodiment is shown.

[0025] Fig. 9 An example of point configuration in each LOD according to an embodiment is shown.

[0026] Fig.10 An exemplary point cloud decoder according to an implementation is shown.

[0027] Fig.11 An exemplary point cloud decoder according to an implementation is shown.

[0028] Fig.12 An exemplary transmitting device according to an embodiment is shown.

[0029] Fig.13 An exemplary receiving device according to an embodiment is shown.

[0030] Fig.14 An architecture for streaming G-PCC based point cloud data according to an embodiment is shown.

[0031] Fig.15 An exemplary point cloud transmitting device according to an embodiment is shown.

[0032] Fig.16 An exemplary point cloud receiving device according to an embodiment is shown.

[0033] Fig.17 An exemplary structure operatively connectable to a method / apparatus for transmitting and receiving point cloud data according to an embodiment is shown.

[0034] Fig.18 is a block diagram illustrating an exemplary point cloud encoder.

[0035] Fig.19 is a block diagram illustrating an exemplary geometry information encoder.

[0036] Fig. 20 An exemplary attribute information encoder according to an embodiment is shown.

[0037] Fig.21 An exemplary attribute information encoder according to an embodiment is shown.

[0038] Fig. 22 An exemplary attribute information predictor according to an embodiment is shown.

[0039] Fig.23 An exemplary setting of a neighbor point search range is shown.

[0040] Fig.24 An exemplary neighbor point search process according to an embodiment is shown.

[0041] Fig.25 An exemplary neighbor point search process according to an embodiment is shown.

[0042] Fig.26 An exemplary structural diagram of a point cloud compression (PCC) bitstream is shown.

[0043] Fig. 27 An exemplary syntax of the APS according to an embodiment is shown.

[0044] Fig.28 An exemplary syntax of TPS according to an embodiment is shown.

[0045] Fig.29 An exemplary syntax of an attribute header according to an embodiment is shown.

[0046] Fig.30 is a block diagram illustrating an exemplary point cloud decoder.

[0047] Fig.31 is a block diagram illustrating an example of a geometry information decoder.

[0048] Fig.32 is a block diagram illustrating an exemplary attribute information decoder.

[0049] Fig.33 is an exemplary flow chart of a point cloud data processing method according to an embodiment; and

[0050] Fig.34 is an exemplary flow chart of a point cloud data processing method according to an embodiment. DETAILED DESCRIPTION

[0051] Reference will now be made in detail to the preferred embodiments of the present disclosure, examples of which are shown in the accompanying drawings. The detailed description given below with reference to the accompanying drawings is intended to illustrate exemplary embodiments of the present disclosure, rather than to illustrate the only embodiments that can be implemented according to the present disclosure. The following detailed description includes specific details in order to provide a thorough understanding of the present disclosure. However, it will be apparent to those skilled in the art that the present disclosure can be practiced without these specific details.

[0052] Although most of the terms used in the present disclosure are selected from common terms widely used in the art, some terms are arbitrarily selected by the applicant and their meanings are explained in detail in the following description as needed. Therefore, the present disclosure should be understood based on the intended meanings of the terms rather than their simple names or meanings.

[0053] Figure 1 An exemplary point cloud content providing system according to an embodiment is shown.

[0054] Figure 1 The point cloud content providing system shown may include a transmitting device 10000 and a receiving device 10004. The transmitting device 10000 and the receiving device 10004 can transmit and receive point cloud data via wired or wireless communication.

[0055] According to an embodiment, the point cloud data sending device 10000 can obtain and process point cloud video (or point cloud content) and send it. According to an embodiment, the sending device 10000 may 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. According to an embodiment, the sending device 10000 may include a device configured to communicate with a base station and / or other wireless devices using a radio access technology (e.g., 5G new RAT (NR), long term evolution (LTE)), a robot, a vehicle, an AR / VR / XR device, a portable device, a home appliance, an Internet of Things (IoT) device, and an AI device / server.

[0056] According to the implementation manner, the sending device 10000 includes a point cloud video acquirer 10001, a point cloud video encoder 10002 and / or a transmitter (or communication module) 10003.

[0057] The point cloud video acquirer 10001 according to the embodiment acquires the point cloud video through a processing process such as capture, synthesis or generation. The point cloud video is a point cloud content represented by a point cloud, which is a set of points located in a 3D space and can be referred to as point cloud video data. The point cloud video according to the embodiment may include one or more frames. A 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 a point cloud image, frame or picture.

[0058] The point cloud video encoder 10002 according to the embodiment encodes the acquired point cloud video data. The point cloud video encoder 10002 may encode the point cloud video data based on point cloud compression coding. The point cloud compression coding according to the embodiment may include geometry-based point cloud compression (G-PCC) coding and / or video-based point cloud compression (V-PCC) coding or next generation coding. The point cloud compression coding according to the embodiment is not limited to the above-mentioned embodiment. The point cloud video encoder 10002 may output a bit stream containing the encoded point cloud video data. The bit stream may include not only the encoded point cloud video data, but also signaling information related to the encoding of the point cloud video data.

[0059] The transmitter 10003 according to the embodiment transmits a bitstream containing encoded point cloud video data. The bitstream according to the embodiment is encapsulated in a file or a fragment (e.g., a stream fragment) and is transmitted via various networks such as a broadcast network and / or a broadband network. Although not shown in the figure, the transmitting device 10000 may include a packager (or a package module) configured to perform a packaging operation. According to an embodiment, the packager may be included in the transmitter 10003. According to an embodiment, the file or fragment may be sent to the receiving device 10004 via a network, or stored in a digital storage medium (e.g., USB, SD, CD, DVD, Blu-ray, HDD, SSD, etc.). The transmitter 10003 according to the embodiment can communicate with the receiving device 10004 (or the receiver 10005) via a 4G, 5G, 6G or other network by wire / wireless communication. In addition, the transmitter can perform necessary data processing operations according to the network system (e.g., a 4G, 5G or 6G communication network system). The transmitting device 10000 can send encapsulated data in an on-demand manner.

[0060] The receiving device 10004 according to an embodiment includes a receiver 10005, a point cloud video decoder 10006 and / or a renderer 10007. According to an embodiment, the receiving device 10004 may include a device configured to communicate with a base station and / or other wireless devices using a radio access technology (e.g., 5G New RAT (NR), Long Term Evolution (LTE)), a robot, a vehicle, an AR / VR / XR device, a portable device, a home appliance, an Internet of Things (IoT) device, and an AI device / server.

[0061] The receiver 10005 according to the embodiment receives a bitstream containing point cloud video data or a file / segment encapsulated with a bitstream from a network or a storage medium. The receiver 10005 can perform necessary data processing according to a network system (e.g., a communication network system of 4G, 5G, 6G, etc.). The receiver 10005 according to the embodiment can decapsulate the received file / segment and output a bitstream. According to the embodiment, the receiver 10005 may include a decapsulator (or decapsulation module) configured to perform a decapsulation operation. The decapsulator can be implemented as an element (or component) separated from the receiver 10005.

[0062] The point cloud video decoder 10006 decodes the bitstream containing the point cloud video data. The point cloud video decoder 10006 can decode the point cloud video data according to the method in which the point cloud video data is encoded (for example, in the reverse process of the operation of the point cloud video encoder 10002). Therefore, the point cloud video decoder 10006 can decode the point cloud video data by performing point cloud decompression coding (the reverse process of point cloud compression). Point cloud decompression coding includes G-PCC coding.

[0063] The renderer 10007 renders the decoded point cloud video data. The renderer 10007 may output the point cloud content by rendering not only the point cloud video data but also the audio data. According to an embodiment, the renderer 10007 may include a display configured to display the point cloud content. According to an embodiment, the display may be implemented as a separate device or component rather than being included in the renderer 10007.

[0064] The arrow indicated by the dotted line in the figure represents the transmission path of the feedback information obtained by the receiving device 10004. Feedback information is information that reflects the interactivity with the user consuming the point cloud content, and includes information about the user (e.g., head orientation information, viewport information, etc.). Specifically, when the point cloud content is content for a service that requires interaction with the user (e.g., self-driving service, etc.), the feedback information may be provided to the content sender (e.g., sending device 10000) and / or the service provider. Depending on the embodiment, the feedback information may be used in the receiving device 10004 as well as the sending device 10000, or may not be provided.

[0065] The head orientation information according to the embodiment is information about the user's head position, orientation, angle, movement, etc. The receiving device 10004 according to the embodiment can calculate the viewport information based on the head orientation information. The viewport information can be information about the area of ​​the point cloud video that the user is watching. The viewpoint is the point through which the user watches the point cloud video, and can refer to the center point of the viewport area. That is, the viewport is an area centered on the viewpoint, and the size and shape of the area can be determined by the field of view (FOV). Therefore, in addition to the head orientation information, the receiving device 10004 can also extract the viewport information based on the vertical or horizontal FOV supported by the device. In addition, the receiving device 10004 performs gaze analysis, etc. to check the way the user consumes the point cloud, the area where the user gazes in the point cloud video, the gaze time, etc. According to the embodiment, the receiving device 10004 can send feedback information including the gaze analysis result to the sending device 10000. The feedback information according to the embodiment can be obtained during the rendering and / or display process. The feedback information according to the embodiment can be obtained by one or more sensors included in the receiving device 10004. According to an implementation, the feedback information may be obtained by the renderer 10007 or a separate external element (or device, component, etc.). Figure 1The dotted line in represents the process of sending the feedback information obtained by the renderer 10007. The point cloud content providing system can process (encode / decode) the point cloud data based on the feedback information. Therefore, the point cloud video data decoder 10006 can perform a decoding operation based on the feedback information. The receiving device 10004 can send the feedback information to the sending device 10000. The sending device 10000 (or the point cloud video data encoder 10002) can perform an encoding operation based on the feedback information. Therefore, the point cloud content providing system can effectively process necessary data (for example, point cloud data corresponding to the user's head position) based on the feedback information instead of processing (encoding / decoding) the entire point cloud data, and provide the point cloud content to the user.

[0066] According to an embodiment, the transmitting device 10000 may be referred to as an encoder, a transmitting device, a transmitter, etc., and the receiving device 10004 may be referred to as a decoder, a receiving device, a receiver, etc.

[0067] According to the implementation mode Figure 1 The point cloud data processed in the point cloud content providing system (through a series of processes of acquisition / encoding / transmission / decoding / rendering) may be referred to as point cloud content data or point cloud video data. According to an embodiment, point cloud content data may be used as a concept covering metadata or signaling information related to point cloud data.

[0068] Figure 1 The elements of the point cloud content providing system shown may be implemented by hardware, software, a processor and / or a combination thereof.

[0069] Figure 2 is a block diagram illustrating point cloud content providing operations according to an embodiment.

[0070] Figure 2 The block diagram shows Figure 1 The operation of the point cloud content providing system described in . As described above, the point cloud content providing system can process point cloud data based on point cloud compression compilation (e.g., G-PCC).

[0071] According to the embodiment, the point cloud content providing system (e.g., the point cloud sending device 10000 or the point cloud video acquirer 10001) can acquire a point cloud video (20000). The point cloud video is represented by a point cloud belonging to a coordinate system for expressing a 3D space. According to the embodiment, the point cloud video may include a Ply (Polygon file format or Stanford Triangle format) file. When the point cloud video has one or more frames, the acquired point cloud video may include one or more Ply files. The Ply file contains point cloud data such as point geometry and / or attributes. The geometry includes the position of the point. The position of each point can be represented by a parameter (e.g., the value of the X, Y and Z axes) representing a three-dimensional coordinate system (e.g., a coordinate system consisting of X, Y and Z axes). Attributes include attributes of the point (e.g., information about the texture, color (YCbCr or RGB), reflectivity r, transparency, etc. of each point). A point has one or more attributes. For example, a point may have a color attribute or two attributes of color and reflectivity. According to an embodiment, geometry may be referred to as position, geometry information, geometry data, etc., and attributes may be referred to as attributes, attribute information, attribute data, etc. The point cloud content providing system (e.g., the point cloud transmitting device 10000 or the point cloud video acquirer 10001) may obtain point cloud data from information related to a point cloud video acquisition process (e.g., depth information, color information, etc.).

[0072] According to an embodiment, a point cloud content providing system (e.g., a transmitting 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 compilation. As described above, point cloud data may include geometry and attributes of points. Therefore, the point cloud content providing system can perform geometry encoding for encoding geometry and output a geometry bitstream. The point cloud content providing system can perform attribute encoding for encoding attributes and output an attribute bitstream. According to an embodiment, the point cloud content providing system can perform attribute encoding based on geometry encoding. The geometry bitstream and the attribute bitstream according to the embodiment can be multiplexed and output as one bitstream. The bitstream according to the embodiment may also include signaling information related to geometry encoding and attribute encoding.

[0073] According to the embodiment, the point cloud content providing system (eg, the transmitting device 10000 or the transmitter 10003) may transmit the encoded point cloud data (20002). Figure 1As shown, the encoded point cloud data can be represented by a geometry bitstream and an attribute bitstream. In addition, the encoded point cloud data can be sent in the form of a bitstream together with signaling information related to the encoding of the point cloud data (e.g., signaling information related to geometry encoding and attribute encoding). The point cloud content providing system can encapsulate the bitstream carrying the encoded point cloud data and send it in the form of a file or a fragment.

[0074] The point cloud content providing system (eg, receiving device 10004 or receiver 10005) according to the embodiment may receive a bit stream containing encoded point cloud data. In addition, the point cloud content providing system (eg, receiving device 10004 or receiver 10005) may demultiplex the bit stream.

[0075] The point cloud content providing system (e.g., the receiving device 10004 or the point cloud video decoder 10005) may decode the encoded point cloud data (e.g., geometry bitstream, attribute bitstream) sent in the bitstream. The point cloud content providing system (e.g., the receiving device 10004 or the point cloud video decoder 10005) may decode the point cloud video data based on the signaling information related to the encoding of the point cloud video data contained in the bitstream. The point cloud content providing system (e.g., the receiving device 10004 or the point cloud video decoder 10005) may decode the geometry bitstream to reconstruct the position (geometry) of the point. The point cloud content providing system may reconstruct the attributes of the point by decoding the attribute bitstream based on the reconstructed geometry. The point cloud content providing system (e.g., the receiving device 10004 or the point cloud video decoder 10005) may reconstruct the point cloud video based on the position according to the reconstructed geometry and the decoded attributes.

[0076] According to an embodiment, a point cloud content providing system (e.g., receiving device 10004 or renderer 10007) can render the decoded point cloud data (20004). The point cloud content providing system (e.g., receiving device 10004 or renderer 10007) can use various rendering methods to render the geometry and attributes decoded by the decoding process. The points in the point cloud content can be rendered as vertices with a specific thickness, a cube with a specific minimum size centered on the corresponding vertex position, or a circle centered on the corresponding vertex position. All or part of the rendered point cloud content is provided to the user through a display (e.g., a VR / AR display, a general display, etc.).

[0077] The point cloud content providing system (e.g., receiving device 10004) according to the embodiment may obtain feedback information (20005). The point cloud content providing system may encode and / or decode the point cloud data based on the feedback information. Figure 1 The feedback information and operations described are the same, so their detailed description is omitted.

[0078] Figure 3 An exemplary process of capturing point cloud video according to an embodiment is shown.

[0079] Figure 3 Show reference Figure 1 to Figure 2 An exemplary point cloud video capture process of a point cloud content providing system is described.

[0080] Point cloud content includes point cloud videos (images and / or videos) representing objects and / or environments located in various 3D spaces (e.g., 3D spaces representing real environments, 3D spaces representing virtual environments, etc.). Therefore, a point cloud content providing system according to an embodiment may capture point cloud videos using one or more cameras (e.g., an infrared camera capable of obtaining depth information, an RGB camera capable of extracting color information corresponding to the depth information, etc.), a projector (e.g., an infrared pattern projector that obtains depth information), LiDAR, etc. The point cloud content providing system according to an embodiment may extract a geometric shape composed of points in a 3D space from the depth information and extract the attributes of each point from the color information to obtain point cloud data. Images and / or videos according to an embodiment may be captured based on at least one of an inward-facing technique and an outward-facing technique.

[0081] Figure 3 The left portion of shows inward-facing techniques. Inward-facing techniques refer to techniques that utilize one or more cameras (or camera sensors) positioned around a central object to capture an image of the central object. Inward-facing techniques may be used to generate point cloud content that provides a 360-degree image of a key object to a user (e.g., VR / AR content that provides a 360-degree image of an object (e.g., a key object such as a character, player, object, or actor) to a user).

[0082] Figure 3 The right portion of shows outward-facing techniques. Outward-facing techniques refer to techniques that utilize one or more cameras (or camera sensors) positioned around a central object to capture the environment of the central object rather than an image of the central object. Outward-facing techniques can be used to generate point cloud content that provides the surrounding environment as it appears from the user's perspective (e.g., content representing the external environment that can be provided to a user of a self-driving vehicle).

[0083] As shown, point cloud content may be generated based on a capture operation of one or more cameras. In this case, the coordinate system may be different between the cameras, so the point cloud content providing system may calibrate one or more cameras to set a global coordinate system before the capture operation. In addition, the point cloud content providing system may generate point cloud content by synthesizing any image and / or video with the image and / or video captured by the above-mentioned capture technology. The point cloud content providing system may not perform a point cloud content generation operation when generating point cloud content representing a virtual space. Figure 3The point cloud content providing system according to the embodiment may perform post-processing on the captured image and / or video. In other words, the point cloud content providing system may remove unwanted areas (e.g., background), identify the space to which the captured image and / or video is connected, and when there is a space hole, perform an operation to fill the space hole.

[0084] The point cloud content providing system can generate a point cloud content by performing coordinate transformation on the points of the point cloud video obtained from each camera. The point cloud content providing system can perform coordinate transformation on the points based on the position coordinates of each camera. Therefore, the point cloud content providing system can generate content representing a wide range, or can generate point cloud content with high density points.

[0085] Figure 4 An exemplary point cloud encoder according to an implementation is shown.

[0086] Figure 4 Show Figure 1 An example of a point cloud video encoder 10002. The point cloud encoder reconstructs and encodes point cloud data (e.g., the location and / or attributes of a point) to adjust the quality of the point cloud content (e.g., lossless, lossy, or near-lossless) according to network conditions or applications. When the total size of the point cloud content is large (e.g., 60 Gbps of point cloud content given for 30 fps), the point cloud content providing system may not be able to stream the content in real time. Therefore, the point cloud content providing system can reconstruct the point cloud content based on the maximum target bit rate to provide the point cloud content according to the network environment, etc.

[0087] As reference Figure 1 and Figure 2 As described, the point cloud encoder can perform both geometry encoding and attribute encoding. Geometry encoding is performed before attribute encoding.

[0088] According to an embodiment, the point cloud encoder includes a coordinate transformer (transforming coordinates) 40000, a quantizer (quantizing and removing points (voxelization)) 40001, an octree analyzer (analyzing octree) 40002 and a surface approximation analyzer (analyzing surface approximation) 40003, an arithmetic encoder (arithmetic coding) 40004, a geometry reconstructor (reconstructing geometry) 40005, a color transformer (transforming color) 40006, an attribute transformer (transforming attributes) 40007, a RAHT transformer (RAHT) 40008, an LOD generator (generating LOD) 40009, a lifting transformer (lifting) 40010, a coefficient quantizer (quantizing coefficients) 40011 and / or an arithmetic encoder (arithmetic coding) 40012.

[0089] The coordinate transformer 40000, the quantizer 40001, the octree analyzer 40002, the surface approximation analyzer 40003, the arithmetic encoder 40004 and the geometry reconstructor 40005 may perform geometry coding. The geometry coding according to the embodiment may include octree geometry coding, direct coding, triplet geometry coding and entropy coding. Direct coding and triplet geometry coding are applied selectively or in combination. The geometry coding is not limited to the above examples.

[0090] As shown in the figure, the coordinate transformer 40000 according to the embodiment receives the position and transforms it into coordinates. For example, the position can be transformed into position information in a three-dimensional space (e.g., a three-dimensional space represented by an XYZ coordinate system). The position information in the three-dimensional space according to the embodiment can be referred to as geometric information.

[0091] The quantizer 40001 according to the embodiment quantizes the geometry. For example, the quantizer 40001 can quantize the points based on the minimum position value of all points (e.g., the minimum value on each of the X, Y, and Z axes). The quantizer 40001 performs a quantization operation: the difference between the minimum position value and the position value of each point is multiplied by a preset quantization scale value, and then the nearest integer value is found by rounding the value obtained by the multiplication. Therefore, one or more points may have the same quantized position (or position value). The quantizer 40001 according to the embodiment performs voxelization based on the quantized position to reconstruct the quantized point. As in the case of a pixel (the smallest unit containing 2D image / video information), a point of a point cloud content (or 3D point cloud video) according to the embodiment may be included in one or more voxels. As a composite of volume and pixel, the term voxel refers to a 3D cubic space generated when a 3D space is divided into units (unit = 1.0) based on an axis representing the 3D space (e.g., X-axis, Y-axis, and Z-axis). Quantizer 40001 can match a point group in a 3D space with a voxel. According to an embodiment, a voxel may include only one point. According to an embodiment, a voxel may include one or more points. In order to represent a voxel as a point, the position of the center of the voxel can be set based on the position of one or more points included in the voxel. In this case, the attributes of all positions included in a voxel can be combined and assigned to the voxel.

[0092] The octree analyzer 40002 according to the embodiment performs octree geometry compilation (or octree compilation) to present voxels in an octree structure. The octree structure represents points matched to voxels based on an octal tree structure.

[0093] The surface approximation analyzer 40003 according to an embodiment may analyze and approximate an octree. The octree analysis and approximation according to an embodiment is a process of analyzing a region including a plurality of points to efficiently provide an octree and voxelization.

[0094] The arithmetic encoder 40004 according to the embodiment performs entropy coding on the octree and / or the approximate octree. For example, the coding scheme includes arithmetic coding. As a result of the coding, a geometry bitstream is generated.

[0095] The color transformer 40006, the attribute transformer 40007, the RAHT transformer 40008, the LOD generator 40009, the lifting transformer 40010, the coefficient quantizer 40011 and / or the arithmetic encoder 40012 perform attribute coding. As described above, a point may have one or more attributes. The attribute coding according to the embodiment is also applied to the attributes of a point. However, when the attribute (e.g., color) includes one or more elements, the attribute coding is applied independently to each element. The attribute coding according to the embodiment includes color transform coding, attribute transform coding, regional adaptive hierarchical transform (RAHT) coding, interpolation-based hierarchical nearest neighbor prediction (prediction transform) coding, and interpolation-based hierarchical nearest neighbor prediction (lifting transform) coding with an update / lifting step. Depending on the point cloud content, the above-mentioned RAHT coding, prediction transform coding and lifting transform coding can be selectively used, or a combination of one or more coding schemes can be used. The attribute coding according to the embodiment is not limited to the above examples.

[0096] The color converter 40006 according to the embodiment performs color conversion compilation to convert the color value (or texture) included in the attribute. For example, the color converter 40006 can convert the format of the color information (e.g., from RGB to YCbCr). Optionally, the operation of the color converter 40006 according to the embodiment can be applied according to the color value included in the attribute.

[0097] The geometry reconstructor 40005 according to the embodiment reconstructs (decompresses) the octree and / or the approximate octree. The geometry reconstructor 40005 reconstructs the octree / voxel based on the result of analyzing the point distribution. The reconstructed octree / voxel may be referred to as reconstructed geometry (restored geometry).

[0098] The attribute transformer 40007 according to the embodiment performs attribute transformation to transform the attribute based on the reconstructed geometry and / or the position where the geometry encoding is not performed. As described above, since the attribute depends on the geometry, the attribute transformer 40007 can transform the attribute based on the reconstructed geometry information. For example, based on the position value of the point included in the voxel, the attribute transformer 40007 can transform the attribute of the point at the position. As described above, when the center position of the voxel is set based on the position of one or more points included in the voxel, the attribute transformer 40007 transforms the attributes of one or more points. When triplet geometry encoding is performed, the attribute transformer 40007 can transform the attribute based on the triplet geometry encoding.

[0099] The attribute transformer 40007 can perform attribute transformation by calculating the average of the attributes or attribute values ​​(e.g., the color or reflectivity of each point) of the neighboring points within a specific position / radius from the center position (or position value) of each voxel. The attribute transformer 40007 can apply weights according to the distance from the center to each point when calculating the average. Therefore, each voxel has a position and a calculated attribute (or attribute value).

[0100] Attribute converter 40007 can search for neighbor points within a specific position / radius from the center position of each voxel based on a KD tree or a Morton code. KD tree is a binary search tree and supports a data structure capable of managing points based on position, so that a nearest neighbor search (NNS) can be performed quickly. Morton code is generated by presenting the coordinates (e.g., (x, y, z)) representing the 3D positions of all points as bit values ​​and mixing the bits. For example, when the coordinates representing the point position are (5, 9, 1), the bit values ​​of the coordinates are (0101, 1001, 0001). Mixing the bit values ​​in the order of z, y, and x according to the bit index produces 010001000111. This value is represented as a decimal number 1095. That is, the Morton code value of the point with coordinates (5, 9, 1) is 1095. Attribute converter 40007 can sort the points based on the Morton code value and perform NNS through a depth-first traversal process. After the attribute transformation operation, KD-tree or Morton code is used when NNS is needed in another transformation process for attribute compilation.

[0101] As shown, the transformed attributes are input to the RAHT transformer 40008 and / or the LOD generator 40009.

[0102] The RAHT transformer 40008 according to the embodiment performs RAHT coding for predicting attribute information based on the reconstructed geometric information. For example, the RAHT transformer 40008 may predict attribute information of a higher-level node in the octree based on attribute information associated with a lower-level node in the octree.

[0103] The LOD generator 40009 according to an embodiment generates a level of detail (LOD) to perform predictive transform compilation. The LOD according to an embodiment is the degree of detail of the point cloud content. As the LOD value decreases, the details of the point cloud content are degraded. As the LOD value increases, the details of the point cloud content are enhanced. Points can be classified by LOD.

[0104] The lifting transformer 40010 according to an embodiment performs lifting transformation compilation to transform point cloud attributes based on weights. As described above, lifting transformation compilation can be optionally applied.

[0105] The coefficient quantizer 40011 according to the embodiment quantizes the attribute of the attribute encoding based on the coefficient.

[0106] The arithmetic encoder 40012 according to the embodiment encodes the quantized properties based on arithmetic coding.

[0107] Although not shown in the figure, Figure 4 The elements of the point cloud encoder 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. The one or more processors may perform the above Figure 4 At least one of the operations and / or functions of the elements of the point cloud encoder. In addition, one or more processors may be operable or executable to perform Figure 4 The software program and / or instruction set for the operation and / or functionality of the elements of the point cloud encoder. One or more memories according to the embodiment may include high-speed random access memory, or include non-volatile memory (e.g., one or more disk storage devices, flash memory devices, or other non-volatile solid-state memory devices).

[0108] Figure 5 An example of a voxel according to an embodiment is shown.

[0109] Figure 5 , which is a voxel located in a 3D space represented by a coordinate system consisting of three axes (X axis, Y axis and Z axis). Figure 4 As described above, the point cloud encoder (eg, quantizer 40001) may perform voxelization. A voxel refers to a 3D cubic space generated when a 3D space is divided into units (unit=1.0) based on axes representing the 3D space (eg, X-axis, Y-axis, and Z-axis). Figure 5 An example of voxels generated by an octree structure is shown, where a cubic axis-aligned bounding box defined by two poles (0,0,0) and (2d,2d,2d) is recursively subdivided. A voxel includes at least one point. The spatial coordinates of the voxel can be estimated from the positional relationship with the voxel group. As described above, the voxel has properties similar to the pixels of a 2D image / video (e.g., color or reflectivity). The details of the voxel are similar to those of the reference Figure 4 Those described are the same, so their description is omitted.

[0110] Figure 6 An example of an octree and occupancy code according to an embodiment is shown.

[0111] As reference Figures 1 to 4As described, the point cloud content providing system (point cloud video encoder 10002) or the point cloud encoder (e.g., octree analyzer 40002) performs octree geometry encoding (or octree coding) based on the octree structure to effectively manage the area and / or position of voxels.

[0112] Figure 6 The upper part of the octree structure is shown. The 3D space of the point cloud content according to the embodiment is represented by the axes of the coordinate system (e.g., X-axis, Y-axis, and Z-axis). The octree structure is created by recursive subdivision of a cubic axis-aligned bounding box defined by two poles (0,0,0) and (2d,2d,2d). Here, 2d can be set to the value of the minimum bounding box constituting all points around the point cloud content (or point cloud video). Here, d represents the depth of the octree. The value of d is determined in the following formula. In the following formula, (x int n ,y int n ,z int n ) represents the position (or position value) of the quantized point.

[0113]

[0114] like Figure 6 As shown in the middle of the upper part, the entire 3D space can be divided into eight spaces according to the partition. Each divided space is represented by a cube with six faces. Figure 6 As shown in the upper right part of , each of the eight spaces is divided again based on the axis of the coordinate system (e.g., X-axis, Y-axis, and Z-axis). Therefore, each space is divided into eight smaller spaces. The divided smaller spaces are also represented by cubes with six faces. This partitioning scheme is applied until the leaf nodes of the octree become voxels.

[0115] Figure 6 The lower part shows the octree occupancy code. The occupancy code of the octree is generated to indicate whether each of the eight divided spaces generated by dividing one space contains at least one point. Therefore, a single occupancy code is represented by eight child nodes. Each child node represents the occupancy of the divided space, and the child node has a 1-bit value. Therefore, the occupancy code is represented as an 8-bit code. That is, when the space corresponding to the child node contains at least one point, the node is assigned a value of 1. When the space corresponding to the child node does not contain a point (the space is empty), the node is assigned a value of 0. Since Figure 6The occupancy code shown is 00100001, so it indicates that the spaces corresponding to the third subnode and the eighth subnode among the eight subnodes each contain at least one point. As shown in the figure, each of the third subnode and the eighth subnode has eight subnodes, and the subnodes are represented by an 8-bit occupancy code. The accompanying drawings show that the occupancy code of the third subnode is 10000111, and the occupancy code of the eighth subnode is 01001111. The point cloud encoder (e.g., arithmetic encoder 40004) according to the embodiment can perform entropy encoding on the occupancy code. In order to increase the compression efficiency, the point cloud encoder can perform intra-frame / inter-frame coding on the occupancy code. The receiving device (e.g., receiving device 10004 or point cloud video decoder 10006) according to the embodiment reconstructs the occupancy code based on the occupancy code.

[0116] According to an embodiment, a point cloud encoder (e.g., Figure 4 The point cloud encoder or octree analyzer 40002) can perform voxelization and octree compilation to store point locations. However, points are not always evenly distributed in the 3D space, so there may be specific areas where there are fewer points. Therefore, it is inefficient to perform voxelization on the entire 3D space. For example, when a specific area contains few points, voxelization does not need to be performed in the specific area.

[0117] Therefore, for the above-mentioned specific area (or nodes other than the leaf nodes of the octree), the point cloud encoder according to the embodiment may skip voxelization and perform direct coding to directly encode the point positions included in the specific area. The coordinates of the directly coded points according to the embodiment are called direct coding mode (DCM). The point cloud encoder according to the embodiment may also perform triplet geometry coding based on the surface model, which is to reconstruct the point positions in the specific area (or node) based on voxels. Triplet geometry coding is a geometry coding that represents an object as a series of triangular meshes. Therefore, the point cloud decoder can generate a point cloud from the mesh surface. Direct coding and triplet geometry coding according to the embodiment can be selectively performed. In addition, direct coding and triplet geometry coding according to the embodiment can be performed in combination with octree geometry coding (or octree coding).

[0118] In order to perform direct coding, the option of using direct mode to apply direct coding should be enabled. The node to which direct coding is to be applied is not a leaf node, and there should be points less than a threshold within a specific node. In addition, the total number of points to which direct coding is to be applied should not exceed a preset threshold. When the above conditions are met, the point cloud encoder (or arithmetic encoder 40004) according to the implementation method can perform entropy coding on the point position (or position value).

[0119] A point cloud encoder (e.g., surface approximation analyzer 40003) according to an embodiment may determine a specific level of the octree (a level less than the depth d of the octree), and may use a surface model starting from this level to perform triplet geometry encoding to reconstruct point positions in the node area based on voxels (triplet mode). A point cloud encoder according to an embodiment may specify a level to which triplet geometry encoding is to be applied. For example, when a specific level is equal to the depth of the octree, the point cloud encoder does not operate in triplet mode. In other words, a point cloud encoder according to an embodiment may operate in triplet mode only when the specified level is less than the depth value of the octree. A 3D cubic area of ​​a node at a specified level according to an embodiment is referred to as a block. A block may include one or more voxels. A block or voxel may correspond to a block. Geometry is represented as a surface within each block. A surface according to an embodiment may intersect each edge of a block at most once.

[0120] A block has 12 edges, so there are at least 12 intersections in a block. Each intersection is called a vertex. When there is at least one occupied voxel adjacent to the edge among all blocks sharing the edge, the vertex existing along the edge is detected. An occupied voxel according to an embodiment refers to a voxel containing a point. The vertex position detected along the edge is the average position of the edge of all voxels adjacent to the edge among all blocks along the shared edge.

[0121] Once the vertex is detected, the point cloud encoder according to the embodiment can perform entropy encoding on the starting point (x, y, z) of the edge, the direction vector (Δx, Δy, Δz) of the edge, and the vertex position value (relative position value within the edge). When triplet geometry encoding is applied, the point cloud encoder according to the embodiment (e.g., geometry reconstructor 40005) can generate recovered geometry (reconstructed geometry) by performing triangle reconstruction, upsampling, and voxelization processes.

[0122] The vertices located at the edges of the block determine the surface that passes through the block. The surface according to an embodiment is a non-planar polygon. In the triangle reconstruction process, the surface represented by the triangle is reconstructed 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 performed by: 1) calculating the centroid value of each vertex, 2) subtracting the center value from each vertex value, and 3) estimating the sum of the squares of the values ​​obtained by the subtraction.

[0123]

[0124] The minimum value of the sum is estimated, and the projection process is performed according to the axis with the minimum value. For example, when the element x is the smallest, each vertex is projected on the x-axis relative to the center of the block and projected on the (y,z) plane. When the value obtained by the projection on the (y,z) plane is (ai,bi), the value of θ is estimated by atan2(bi,ai), and the vertices are sorted based on the value of θ. The following table shows the vertex combination for creating triangles according to the number of vertices. The vertices are sorted from 1 to n. The following table shows that for four vertices, two triangles can be constructed according to the vertex combination. The first triangle can be composed of vertices 1, 2, and 3 among the sorted vertices, and the second triangle can be composed of vertices 3, 4, and 1 among the sorted vertices.

[0125] Table. Triangles formed from vertices sorted 1

[0126] [Table 1]

[0127] n triangle 3 (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)

[0128] An upsampling process is performed to add points in the middle along the edges of the triangle, and voxelization is performed. The added points are generated based on the upsampling factor and the width of the block. The added points are called refined vertices. The point cloud encoder according to an embodiment may voxelize the refined vertices. In addition, the point cloud encoder may perform attribute encoding based on the voxelized position (or position value).

[0129] Figure 7 An example of a neighbor node pattern according to an embodiment is shown.

[0130] In order to increase the compression efficiency of the point cloud video, the point cloud encoder according to the embodiment may perform entropy coding based on context adaptive arithmetic coding.

[0131] As reference Figures 1 to 6 As described, the point cloud content providing system or point cloud encoder (eg, point cloud video encoder 10002, Figure 4 The point cloud encoder or arithmetic encoder 40004) can immediately perform entropy coding on the occupancy code. In addition, the point cloud content providing system or the point cloud encoder can perform entropy coding (intra-frame coding) based on the occupancy code of the current node and the occupancy of the neighboring nodes, or perform entropy coding (inter-frame coding) based on the occupancy code of the previous frame. The frame representation according to the embodiment is a collection of point cloud videos generated simultaneously. The compression efficiency of the intra-frame coding / inter-frame coding according to the embodiment may depend on the number of neighboring nodes referenced. When the number of bits increases, the operation becomes complicated, but the encoding can be biased to one side, which can increase the compression efficiency. For example, when a 3-bit context is given, 2 bits need to be used. 3 = 8 ways to perform compilation. The division of parts for compilation affects the implementation complexity. Therefore, it is necessary to meet the appropriate level of compression efficiency and complexity.

[0132] Figure 7 The process of obtaining an occupancy pattern based on the occupancy of neighbor nodes is shown. The point cloud encoder according to the embodiment determines the occupancy of the neighbor nodes of each node of the octree and obtains the value of the neighbor pattern. The neighbor node pattern is used to infer the occupancy pattern of the node. Figure 7 The left part of the diagram shows a cube corresponding to a node (the cube in the middle) and six cubes (neighboring nodes) that share at least one face with the cube. The nodes shown in the diagram are nodes at the same depth. The numbers shown in the diagram represent the weights (1, 2, 4, 8, 16, and 32) associated with the six nodes, respectively. The weights are assigned in sequence according to the positions of the neighboring nodes.

[0133] Figure 7 The right part of shows the neighbor node pattern value. The neighbor node pattern value is the sum of the values ​​multiplied by the weights of the occupied neighbor nodes (neighbor nodes with points). Therefore, the neighbor node pattern value is 0 to 63. When the neighbor node pattern value is 0, it indicates that there is no node with a point among the neighbor nodes of the node (no occupied node). When the neighbor node pattern value is 63, it indicates that all neighbor nodes are occupied nodes. As shown in the figure, since the neighbor nodes assigned weights 1, 2, 4 and 8 are occupied nodes, the neighbor node pattern value is 15 (the sum of 1, 2, 4 and 8). The point cloud encoder can perform coding according to the neighbor node pattern value (for example, when the neighbor node pattern value is 63, 64 types of coding can be performed). According to an embodiment, the point cloud encoder can reduce the coding complexity by changing the neighbor node pattern value (for example, based on a table that changes 64 to 10 or 6).

[0134] Figure 8 An example of point configuration in each LOD according to an embodiment is shown.

[0135] As reference Figures 1 to 7 As described, the encoded geometry is reconstructed (decompressed) before performing attribute encoding. When direct coding is applied, the geometry reconstruction operation may include changing the placement of directly coded points (e.g., placing directly encoded points in front of the point cloud data). When triplet geometry coding is applied, the geometry reconstruction process is performed by triangle reconstruction, upsampling, and voxelization. Since the attributes depend on the geometry, attribute encoding is performed based on the reconstructed geometry.

[0136] The point cloud encoder (e.g., LOD generator 40009) can classify (reorganize) the points by LOD. The figure shows the point cloud content corresponding to the LOD. The leftmost screen in the figure shows the original point cloud content. The second screen from the left in the figure shows the distribution of points in the lowest LOD, and the rightmost screen in the figure shows the distribution of points in the highest LOD. That is, the points in the lowest LOD are sparsely distributed, and the points in the highest LOD are densely distributed. That is, as the LOD increases in the direction indicated by the arrow indicated at the bottom of the figure, the space (or distance) between the points becomes narrower.

[0137] Fig. 9 An example of point configuration for each LOD according to an embodiment is shown.

[0138] As reference Figures 1 to 8 As described, the point cloud content providing system or point cloud encoder (eg, point cloud video encoder 10002, Figure 4 The point cloud encoder or LOD generator 40009 can generate LOD. LOD is generated by reorganizing points into a set of refinement levels according to a set LOD distance value (or Euclidean distance set). The LOD generation process is performed not only by the point cloud encoder, but also by the point cloud decoder.

[0139] Fig. 9 The upper part of shows examples of points (P0 to P9) of point cloud content distributed in 3D space. Fig. 9 In , the original order represents the order of points P0 to P9 before LOD generation. Fig. 9 In the LOD-based order, the order of points generated according to the LOD is shown. Points are reorganized by LOD. In addition, a high LOD contains points belonging to a lower LOD. Fig. 9 As shown, LOD0 contains P0, P5, P4, and P2. LOD1 contains the points of LOD0, P1, P6, and P3. LOD2 contains the points of LOD0, the points of LOD1, P9, P8, and P7.

[0140] As reference Figure 4 As described, the point cloud encoder according to the embodiment may selectively or in combination perform prediction transform coding, lifting transform coding, and RAHT transform coding.

[0141] The point cloud encoder according to the embodiment may generate a predictor for a point to perform prediction transform coding for setting a prediction attribute (or prediction attribute value) for each point. That is, N predictors may be generated for N points. The predictor according to the embodiment may calculate a weight (=1 / distance) based on the LOD value of each point, index information about neighboring points that exist within a set distance of each LOD, and the distance to the neighboring points.

[0142] The predicted attribute (or attribute value) according to the embodiment is set to the average of the values ​​obtained by multiplying the attribute (or attribute value) (e.g., color, reflectivity, etc.) of the neighboring points set in the predictor of each point by the weight (or weight value) calculated based on the distance to each neighboring point. The point cloud encoder (e.g., coefficient quantizer 40011) according to the embodiment may quantize and inverse quantize the residual (which may be referred to as residual attribute, residual attribute value, or attribute prediction residual) obtained by subtracting the predicted attribute (attribute value) from the attribute (attribute value) of each point. The quantization process is configured as shown in the following table.

[0143] Attribute prediction residual quantization pseudocode

[0144] [Table 2]

[0145] 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); } }

[0146] Attribute prediction residual inverse quantization pseudo code

[0147] [Table 3]

[0148] int PCCInverseQuantization(int value,int quantStep){ if(quantStep==0){ return value; }else{ return value*quantStep; } }

[0149] When the predictor of each point has neighboring points, the point cloud encoder (e.g., arithmetic encoder 40012) according to the embodiment may perform entropy coding on the quantized and inverse quantized residual values ​​as described above. When the predictor of each point has no neighboring points, the point cloud encoder (e.g., arithmetic encoder 40012) according to the embodiment may perform entropy coding on the attributes of the corresponding point without performing the above operation.

[0150] A point cloud encoder (e.g., a lifting transformer 40010) according to an embodiment may generate a predictor for each point, set the calculated LOD and register neighbor points in the predictor, and set weights according to the distance to the neighbor points to perform lifting transformation coding. The lifting transformation coding according to an embodiment is similar to the above-mentioned prediction transformation coding, but the difference is that the weights are cumulatively applied to the attribute values. The process of cumulatively applying weights to the attribute values ​​according to an embodiment is configured as follows.

[0151] 1) Create an array quantized weight (QW) for storing the weight value of each point. The initial value of all elements of QW is 1.0. Multiply the QW value of the predictor index of the neighbor node registered in the predictor by the weight of the predictor of the current point, and add the values ​​obtained by multiplication.

[0152] 2) Boosting prediction process: The value obtained by multiplying the attribute value of the point by the weight is subtracted from the existing attribute value to calculate the predicted attribute value.

[0153] 3) Create temporary arrays called updateweight and update, and initialize the temporary arrays to zero.

[0154] 4) Accumulate the weight calculated by multiplying the weight calculated for all predictors by the weight stored in the QW corresponding to the predictor index with the updateweight array as the index of the neighbor node. Accumulate the value obtained by multiplying the attribute value of the neighbor node index by the calculated weight with the update array.

[0155] 5) Boosting update process: Divide the attribute values ​​of the update array of all predictors by the weight value of the updateweight array of the predictor index, and add the existing attribute value to the value obtained by the division.

[0156] 6) For all predictors, the predicted attribute is calculated by multiplying the attribute value updated by the lifting update process by the weight updated by the lifting prediction process (stored in QW). The point cloud encoder (e.g., coefficient quantizer 40011) according to the embodiment quantizes the predicted attribute value. In addition, the point cloud encoder (e.g., arithmetic encoder 40012) performs entropy encoding on the quantized attribute value.

[0157] A point cloud encoder according to an embodiment (e.g., RAHT transformer 40008) may perform RAHT transform coding, in which attributes associated with lower-level nodes in an octree are used to predict attributes of higher-level nodes. RAHT transform coding is an example of attribute intra-frame coding by scanning backward through an octree. A point cloud encoder according to an embodiment scans the entire area starting from voxels and repeats a merging process of merging voxels into larger blocks at each step until the root node is reached. The merging process according to an embodiment is performed only on occupied nodes. The merging process is not performed on empty nodes. The merging process is performed on the upper node directly above the empty node.

[0158] The following formula represents the RAHT transformation matrix. In this formula, Represents the average attribute value of voxels at level l. Can be based on and to calculate. and The weight is and

[0159]

[0160] here, Is the low-pass value and is used during the next highest level of merging. Denotes the high-pass coefficient. The high-pass coefficient of each step is quantized and subjected to entropy coding (eg, encoded by arithmetic encoder 400012). The weight is calculated as pass and Create the root node as follows.

[0161]

[0162] Similar to the high-pass coefficients, the values ​​of gDC are also quantized and subjected to entropy coding.

[0163] Fig.10 A point cloud decoder according to an embodiment is shown.

[0164] Fig.10 The point cloud decoder shown is Figure 1 An example of a point cloud video decoder 10006 described in Figure 1 The operations of the point cloud video decoder 10006 shown are the same or similar operations. As shown, the point cloud decoder can receive a geometry bitstream and an attribute bitstream contained in one or more bitstreams. The point cloud decoder includes a geometry decoder and an attribute decoder. The geometry decoder performs geometry decoding on the geometry bitstream and outputs the decoded geometry. The attribute decoder performs attribute decoding based on the decoded geometry and attribute bitstreams, and outputs the decoded attributes. The decoded geometry and the decoded attributes are used to reconstruct the point cloud content (decoded point cloud).

[0165] Fig.11 A point cloud decoder according to an embodiment is shown.

[0166] Fig.11 The point cloud decoder shown is Fig.10 An example of a point cloud decoder is shown, and a decoding operation can be performed, which is Figures 1 to 9 The inverse process of the encoding operation of the point cloud encoder shown.

[0167] As reference Figure 1 and Fig.10 As described, the point cloud decoder can perform geometry decoding and attribute decoding. Geometry decoding is performed before attribute decoding.

[0168] According to an embodiment, a point cloud decoder includes an arithmetic decoder (arithmetic decoding) 11000, an octree synthesizer (synthesized octree) 11001, a surface approximation synthesizer (synthesized surface approximation) 11002 and a geometry reconstructor (reconstructed geometry) 11003, an inverse coordinate transformer (inverse transformed coordinates) 11004, an arithmetic decoder (arithmetic decoding) 11005, an inverse quantizer (inverse quantization) 11006, a RAHT transformer 11007, an LOD generator (generate LOD) 11008, an inverse lifting (inverse lifting) 11009 and / or an inverse color transformer (inverse transformed color) 11010.

[0169] The arithmetic decoder 11000, the octree synthesizer 11001, the surface approximation synthesizer 11002, the geometry reconstructor 11003, and the coordinate inverse transformer 11004 may perform geometry decoding. The geometry decoding according to the embodiment may include direct encoding and triplet geometry decoding. Direct encoding and triplet geometry decoding are selectively applied. The geometry decoding is not limited to the above examples, and as a reference Figures 1 to 9 The inverse process of the geometric encoding described is performed.

[0170] The arithmetic decoder 11000 according to the embodiment decodes the received geometry bitstream based on arithmetic coding. The operation of the arithmetic decoder 11000 corresponds to the inverse process of the arithmetic encoder 40004.

[0171] The octree synthesizer 11001 according to the embodiment may generate an octree by acquiring an occupancy code (or information about the geometry obtained as a result of decoding) from a decoded geometry bitstream. Figures 1 to 9 Describe the configuration in detail.

[0172] When triplet geometry encoding is applied, a surface approximation synthesizer 11002 according to an embodiment may synthesize a surface based on the decoded geometry and / or the generated octree.

[0173] According to an embodiment, the geometry reconstructor 11003 can regenerate the geometry based on the surface and / or the decoded geometry. Figures 1 to 9 As described, direct coding and triplet geometry coding are selectively applied. Therefore, the geometry reconstructor 11003 directly imports the position information about the points to which direct coding is applied and adds them. When triplet geometry coding is applied, the geometry reconstructor 11003 can reconstruct the geometry by performing the reconstruction operations (e.g., triangle reconstruction, upsampling, and voxelization) of the geometry reconstructor 40005. Details and References Figure 6 The reconstructed geometry may include a point cloud screen or frame that does not contain attributes.

[0174] The coordinate inverse transformer 11004 according to an embodiment can obtain the point position by transforming the coordinates based on the reconstructed geometry.

[0175] The arithmetic decoder 11005, the inverse quantizer 11006, the RAHT transformer 11007, the LOD generator 11008, the inverse lifting 11009 and / or the color inverse transformer 11010 may perform the reference Fig.10Attribute decoding described. Attribute decoding according to an embodiment includes regional adaptive hierarchical transform (RAHT) decoding, hierarchical nearest neighbor prediction (prediction transform) decoding based on interpolation, and hierarchical nearest neighbor prediction (lifting transform) decoding based on interpolation with an update / lifting step. The above three decoding schemes can be used selectively, or a combination of one or more decoding schemes can be used. Attribute decoding according to an embodiment is not limited to the above examples.

[0176] The arithmetic decoder 11005 according to the embodiment decodes the attribute bit stream through arithmetic coding.

[0177] The inverse quantizer 11006 according to the embodiment inversely quantizes information about the decoded attribute bitstream or the attribute obtained as a result of decoding, and outputs the inversely quantized attribute (or attribute value). The inverse quantization may be selectively applied based on the attribute encoding of the point cloud encoder.

[0178] According to an embodiment, the RAHT transformer 11007, the LOD generator 11008 and / or the inverse lifter 11009 may process the reconstructed geometry and inverse quantized attributes. As described above, the RAHT transformer 11007, the LOD generator 11008 and / or the inverse lifter 11009 may selectively perform a decoding operation corresponding to the encoding of the point cloud encoder.

[0179] The color inverse transformer 11010 according to an embodiment performs inverse transform encoding to inversely transform a color value (or texture) included in the decoded attribute. The operation of the color inverse transformer 11010 may be selectively performed based on the operation of the color transformer 40006 of the point cloud encoder.

[0180] Although not shown in the figure, Fig.11 The elements of the point cloud decoder 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. The one or more processors may perform the above Fig.11 At least one or more of the operations and / or functions of the elements of the point cloud decoder. In addition, one or more processors may be operable or executable to perform Fig.11 A software program and / or instruction set for the operation and / or functionality of the elements of a point cloud decoder.

[0181] Fig.12 An exemplary transmitting device according to an embodiment is shown.

[0182] Fig.12 The sending device shown is Figure 1 The sending device 10000 (or Figure 4 Example of a point cloud encoder). Fig.12The sending device shown can be implemented with reference to Figures 1 to 9 One or more operations and methods that are the same or similar to those of the point cloud encoder described. The sending device according to the embodiment may include a data input unit 12000, a quantization processor 12001, a voxelization processor 12002, an octree occupancy code generator 12003, a surface model processor 12004, an intra-frame / inter-frame coding processor 12005, an arithmetic encoder 12006, a metadata processor 12007, a color transformation processor 12008, an attribute transformation processor 12009, a prediction / lifting / RAHT transformation processor 12010, an arithmetic encoder 12011 and / or a transmission processor 12012.

[0183] The data input unit 12000 according to the embodiment receives or acquires point cloud data. The data input unit 12000 may perform the same operation and / or acquisition method as the point cloud video acquirer 10001 (or refer to Figure 2 The acquisition process 20000) described above has the same or similar operations and / or acquisition methods.

[0184] The data input unit 12000, the quantization processor 12001, the voxelization processor 12002, the octree occupancy code generator 12003, the surface model processor 12004, the intra / inter coding processor 12005 and the arithmetic encoder 12006 perform geometric coding. Figures 1 to 9 The geometrical encodings described are the same or similar, so their detailed description is omitted.

[0185] The quantization processor 12001 according to the embodiment quantizes the geometry (e.g., the position value of a point). The operation and / or quantization of the quantization processor 12001 is similar to the reference Figure 4 The operation and / or quantization of the quantizer 40001 described above are the same or similar. Figures 1 to 9 Same as those described.

[0186] The voxelization processor 12002 according to the embodiment voxelizes the quantized position value of the point. The voxelization processor 120002 may perform the same operation as the reference. Figure 4 The operation and / or voxelization process of the quantizer 40001 described above is the same or similar to the operation and / or process described above. Figures 1 to 9 Same as those described.

[0187] The octree occupancy code generator 12003 according to the embodiment performs octree encoding on the voxelized position of the point based on the octree structure. The octree occupancy code generator 12003 can generate an occupancy code. The octree occupancy code generator 12003 can perform the same as the reference Figure 4 and Figure 6The operations and / or methods of the point cloud encoder (or octree analyzer 40002) described above are the same or similar to the operations and / or methods described above. Figures 1 to 9 Same as those described.

[0188] According to an embodiment, the surface model processor 12004 may perform triplet geometry encoding based on the surface model to reconstruct the point position in a specific area (or node) based on voxels. Figure 4 The operations and / or methods described herein are the same or similar to the operations and / or methods of the point cloud encoder (e.g., surface approximation analyzer 40003). Figures 1 to 9 Same as those described.

[0189] According to the embodiment, the intra-frame / inter-frame encoding processor 12005 can perform intra-frame / inter-frame encoding on the point cloud data. The intra-frame / inter-frame encoding processor 12005 can perform the same as the reference Figure 7 The same or similar encoding as described for intra / inter encoding. Details and references Figure 7 The intra / inter encoding processor 12005 may be included in the arithmetic encoder 12006 according to an embodiment.

[0190] According to an embodiment, the arithmetic encoder 12006 performs entropy coding on the octree and / or approximate octree of the point cloud data. For example, the coding scheme includes arithmetic coding. The arithmetic encoder 12006 performs the same or similar operations and / or methods as the operations and / or methods of the arithmetic encoder 40004.

[0191] The metadata processor 12007 according to an embodiment processes metadata (e.g., set values) about the point cloud data and provides it to necessary processing processes such as geometry coding and / or attribute coding. In addition, the metadata processor 12007 according to an embodiment can generate and / or process signaling information related to geometry coding and / or attribute coding. The signaling information according to an embodiment can be encoded separately from the geometry coding and / or attribute coding. The signaling information according to an embodiment can be interleaved.

[0192] The color conversion processor 12008, the attribute conversion processor 12009, the prediction / lifting / RAHT conversion processor 12010, and the arithmetic encoder 12011 perform attribute encoding. Figures 1 to 9 The attribute codes described are the same or similar, so their detailed description is omitted.

[0193] The color transform processor 12008 according to an embodiment performs color transform compilation to transform the color value included in the attribute. The color transform processor 12008 may perform color transform compilation based on the reconstructed geometry. The reconstructed geometry is compared with the reference Figures 1 to 9 In addition, its execution is the same as that of reference Figure 4 The operations and / or methods of the color converter 40006 described above are the same as or similar to the operations and / or methods described above, and their detailed description is omitted.

[0194] The attribute transformation processor 12009 according to an embodiment performs attribute transformation to transform attributes based on reconstructed geometry and / or locations where geometry encoding is not performed. The attribute transformation processor 12009 performs the attribute transformation with reference to Figure 4 The operations and / or methods of the attribute transformer 40007 described in the foregoing description are the same as or similar to those of the attribute transformer 40007. Detailed description thereof is omitted. The prediction / lifting / RAHT transformation processor 12010 according to the embodiment may encode the transformed attributes by any one or a combination of RAHT coding, prediction transformation coding, and lifting transformation coding. The prediction / lifting / RAHT transformation processor 12010 performs the same as the reference. Figure 4 The operations of the RAHT transformer 40008, the LOD generator 40009 and the lifting transformer 40010 described above are the same or similar to at least one operation. In addition, the prediction transformation compilation, the lifting transformation compilation and the RAHT transformation compilation are the same as the reference Figures 1 to 9 Those described are the same, so their detailed description is omitted.

[0195] The arithmetic encoder 12011 according to the embodiment may encode the properties of the encoding based on arithmetic coding. The arithmetic encoder 12011 performs the same or similar operations and / or methods as those of the arithmetic encoder 400012.

[0196] The transmission processor 12012 according to the embodiment may send each bitstream containing the encoded geometry and / or the encoded attribute and metadata information, or send a bitstream configured with the encoded geometry and / or the encoded attribute and metadata information. When the encoded geometry and / or the encoded attribute and metadata information according to the embodiment is configured as a bitstream, the bitstream may include one or more sub-bitstreams. The bitstream according to the embodiment may include signaling information and slice data, and the signaling information includes a sequence parameter set (SPS) for sequence level signaling, a geometry parameter set (GPS) for geometry information coding signaling, an attribute parameter set (APS) for attribute information coding signaling, and a patch parameter set (TPS) for patch level signaling. The slice data may include information about one or more slices. A slice according to the embodiment may include a geometry bitstream Geom00 and one or more attribute bitstreams Attr00 and Attr10. The TPS according to the embodiment may include information about each patch in one or more patches (e.g., coordinate information and height / size information about a bounding box). The geometry bitstream may include a header and a payload. The header of the geometry bitstream according to an embodiment may include a parameter set identifier (geom_parameter_set_id), a tile identifier (geom_tile_id) and a slice identifier (geom_slice_id) included in the GPS and information about the data contained in the payload. As described above, the metadata processor 12007 according to an embodiment may generate and / or process signaling information and send it to the transmission processor 12012. According to an embodiment, the elements performing geometry encoding and the elements performing attribute encoding may share data / information with each other as indicated by the dotted lines. The transmission processor 12012 according to an embodiment may perform operations and / or transmission methods that are the same or similar to those of the transmitter 10003. Details and References Figure 1 and Figure 2 Those described are the same, so their description is omitted.

[0197] Fig.13 An exemplary receiving device according to an embodiment is shown.

[0198] Fig.13 The receiving device shown is Figure 1 The receiving device 10004 (or Fig.10 and Fig.11 Example of a point cloud decoder. Fig.13 The receiving device shown can be implemented with reference to Figures 1 to 11 One or more operations and methods that are the same or similar to those of the described point cloud decoder.

[0199] The receiving apparatus according to the embodiment includes a receiver 13000, a receiving processor 13001, an arithmetic decoder 13002, an octtree reconstruction processor 13003 based on an occupancy code, a surface model processor (triangle reconstruction, upsampling, voxelization) 13004, an inverse quantization processor 13005, a metadata parser 13006, an arithmetic decoder 13007, an inverse quantization processor 13008, a prediction / lifting / RAHT inverse transform processor 13009, a color inverse transform processor 13010 and / or a renderer 13011. Each decoding element according to the embodiment may perform an inverse process of the operation of the corresponding encoding element according to the embodiment.

[0200] The receiver 13000 according to the embodiment receives the point cloud data. The receiver 13000 may perform the same Figure 1 The operation and / or receiving method of the receiver 10005 is the same as or similar to the operation and / or receiving method of the receiver 10005. The detailed description thereof is omitted.

[0201] The reception processor 13001 according to the embodiment may obtain a geometry bitstream and / or an attribute bitstream from the received data. The reception processor 13001 may be included in the receiver 13000.

[0202] The arithmetic decoder 13002, the octtree reconstruction processor 13003 based on the occupancy code, the surface model processor 13004 and the inverse quantization processor 13005 can perform geometric decoding. Figures 1 to 10 The geometric decoding described is the same or similar, so its detailed description is omitted.

[0203] The arithmetic decoder 13002 according to the embodiment may decode the geometry bitstream based on arithmetic coding. The arithmetic decoder 13002 performs the same or similar operations and / or coding as those of the arithmetic decoder 11000.

[0204] The octree reconstruction processor 13003 based on the occupancy code according to the embodiment can reconstruct the octree by obtaining the occupancy code from the decoded geometry bitstream (or information about the geometry obtained as a result of decoding). The octree reconstruction processor 13003 based on the occupancy code performs the same or similar operations and / or methods as the operations of the octree synthesizer 11001 and / or the octree generation method. When triplet geometry coding is applied, the surface model processor 13004 according to the embodiment can perform triplet geometry decoding and related geometry reconstruction (e.g., triangle reconstruction, upsampling, voxelization) based on the surface model method. The surface model processor 13004 performs the same or similar operations as the operations of the surface approximation synthesizer 11002 and / or the geometry reconstructor 11003.

[0205] The inverse quantization processor 13005 according to an embodiment may inversely quantize the decoded geometry.

[0206] According to an embodiment, the metadata parser 13006 may parse metadata (eg, setting values) contained in the received point cloud data. The metadata parser 13006 may pass the metadata to the geometry decoding and / or attribute decoding. Fig.12 The metadata described is the same, so a detailed description thereof is omitted.

[0207] The arithmetic decoder 13007, the inverse quantization processor 13008, the prediction / lifting / RAHT inverse transform processor 13009 and the color inverse transform processor 13010 perform attribute decoding. Figures 1 to 10 The attribute decoding described is the same or similar, so the detailed description thereof is omitted.

[0208] The arithmetic decoder 13007 according to the embodiment can decode the attribute bitstream by arithmetic coding. The arithmetic decoder 13007 can decode the attribute bitstream based on the reconstructed geometry. The arithmetic decoder 13007 performs the same or similar operation and / or coding as the operation and / or coding of the arithmetic decoder 11005.

[0209] The inverse quantization processor 13008 according to an embodiment may inversely quantize the decoded attribute bitstream. The inverse quantization processor 13008 performs the same or similar operation and / or method as the inverse quantizer 11006 and / or the inverse quantization method.

[0210] The prediction / lifting / RAHT inverse transformer 13009 according to an embodiment can process the reconstructed geometry and inverse quantized attributes. The prediction / lifting / RAHT inverse transformer 13009 performs one or more operations and / or decoding that are the same or similar to the operations and / or decoding of the RAHT transformer 11007, the LOD generator 11008 and / or the inverse transformer 11009. The color inverse transformer 13010 according to an embodiment performs inverse transform coding to inversely transform the color value (or texture) included in the decoded attribute. The color inverse transformer 13010 performs the same or similar operations and / or inverse transform coding as the operations and / or inverse transform coding of the color inverse transformer 11010. The renderer 13011 according to an embodiment can render point cloud data.

[0211] Fig.14 An architecture for streaming G-PCC based point cloud data according to an embodiment is shown.

[0212] Fig.14 The upper part shows Figures 1 to 13 The sending device described in Fig.12The process of processing and sending point cloud content (such as a sending device).

[0213] As reference Figures 1 to 13 As described above, the transmitting device can obtain the audio Ba (audio acquisition) of the point cloud content, encode the obtained audio (audio encoding), and output the audio bitstream Ea. In addition, the transmitting device can obtain the point cloud (or point cloud video) Bv (point acquisition) of the point cloud content, and perform point cloud encoding on the obtained point cloud to output the point cloud video bitstream Eb. The point cloud encoding of the transmitting device is similar to the reference Figures 1 to 13 The point cloud encoding described (e.g., Figure 4 The encoding of the point cloud encoder is the same or similar to that of the point cloud encoder, so its detailed description will be omitted.

[0214] The sending device may encapsulate the generated audio bitstream and video bitstream into files and / or fragments (file / fragment encapsulation). The encapsulated files and / or fragments Fs, File may include files in file formats such as ISOBMFF or DASH fragments. Point cloud-related metadata according to an embodiment may be included in the encapsulated file format and / or fragments. The metadata may be included in boxes at various levels on the ISOBMFF file format, or may be included in a separate track within the file. According to an embodiment, the sending device encapsulates the metadata into a separate file. The sending device according to an embodiment may transmit the encapsulated file format and / or fragments via a network. The encapsulation and transmission processing method of the sending device is the same as that of the reference Figures 1 to 13 The same as described (eg, transmitter 10003, Figure 2 transmission step 20002, etc.), so its detailed description will be omitted.

[0215] Fig.14 The lower part shows the reference Figures 1 to 13 The receiving device described (eg, receiving device 10004, Fig.13 The process of processing and outputting point cloud content (such as a receiving device).

[0216] According to an embodiment, the receiving device may include a device (e.g., a speaker, a headset, a display) configured to output final audio data and final video data and a point cloud player (point cloud player) configured to process point cloud content. The final data output device and the point cloud player may be configured as separate physical devices. The point cloud player according to an embodiment may perform geometry-based point cloud compression (G-PCC) coding, video-based point cloud compression (V-PCC) coding, and / or next generation coding.

[0217] The receiving device according to the embodiment can obtain the file and / or segment F', Fs' contained in the received data (for example, a broadcast signal, a signal sent via a network, etc.) and decapsulate it (file / segment decapsulation). Figures 1 to 13 Those described (e.g., receiver 10005, receiving unit 13000, receiving processing unit 13001, etc.) are the same, so their detailed description will be omitted.

[0218] The receiving device according to the embodiment obtains the audio bitstream E'a and the video bitstream E'v contained in the file and / or the segment. As shown in the figure, the receiving device outputs the decoded audio data B'a by performing audio decoding on the audio bitstream, and renders the decoded audio data (audio rendering) to output the final audio data A'a through a speaker or a headphone.

[0219] In addition, the receiving device performs point cloud decoding on the video bit stream E'v and outputs the decoded video data B'v. Figures 1 to 13 Described point cloud decoding (e.g., Fig.11 The decoding of the point cloud decoder is the same or similar to that of the point cloud decoder, so its detailed description will be omitted. The receiving device can render the decoded video data and output the final video data through a display.

[0220] The receiving device according to the embodiment may perform at least one of decapsulation, audio decoding, audio rendering, point cloud decoding, and point cloud video rendering based on the transmitted metadata. Figure 12 to Figure 13 Those described are the same, so their description will be omitted.

[0221] As indicated by the dashed lines shown in the figure, a receiving device (e.g., a point cloud player or a sensing / tracking unit in a point cloud player) according to an embodiment may generate feedback information (orientation, viewport). According to an embodiment, the feedback information may be used in the decapsulation process, point cloud decoding process, and / or rendering process of the receiving device, or may be transmitted to the sending device. Details and references to feedback information Figures 1 to 13 Those described are the same, so their description will be omitted.

[0222] Fig.15 An exemplary transmitting device according to an embodiment is shown.

[0223] Fig.15 The sending device is a device configured to send point cloud content and corresponds to the reference Figures 1 to 14 The sending device described (for example, Figure 1 The sending device 10000, Figure 4 Point cloud encoder, Fig.12 The sending device Fig.14Therefore, Fig.15 The sending device performs the reference Figures 1 to 14 The same or similar operation as described for the sending device.

[0224] The sending device according to the embodiment may perform one or more of point cloud acquisition, point cloud encoding, file / segment packaging and transmission.

[0225] Since the operations of acquiring and transmitting the point cloud shown in the figure are similar to those of reference Figures 1 to 14 The operations described are the same, so their detailed description will be omitted.

[0226] As referenced above Figures 1 to 14 As described, the sending device according to the embodiment can perform geometry encoding and attribute encoding. Geometry encoding can be called geometry compression, and attribute encoding can be called attribute compression. As described above, a point can have a geometry and one or more attributes. Therefore, the sending device performs attribute encoding on each attribute. The figure shows that the sending device performs one or more attribute compressions (attribute #1 compression, ..., attribute #N compression). In addition, the sending device according to the embodiment can perform auxiliary compression. Auxiliary compression is performed on metadata. Details of metadata and reference Figures 1 to 14 The transmitting device may also perform mesh data compression. The mesh data compression according to the embodiment may include reference to Figures 1 to 14 Triplet geometry encoding of descriptions.

[0227] According to an embodiment, a transmitting device may encapsulate a bit stream (e.g., a point cloud stream) output according to point cloud coding into a file and / or a fragment. According to an embodiment, a transmitting device may perform media track encapsulation for carrying data other than metadata (e.g., media data), and perform metadata track encapsulation for carrying metadata. According to an embodiment, metadata may be encapsulated into a media track.

[0228] As reference Figures 1 to 14 As described, the sending device may receive feedback information (orientation / viewport metadata) from the receiving device, and perform at least one of point cloud encoding, file / segment packaging, and transmission operations based on the received feedback information. Figures 1 to 14 Those described are the same, so their description will be omitted.

[0229] Fig.16 An exemplary receiving device according to an embodiment is shown.

[0230] Fig.16 The receiving device is a device for receiving point cloud content and corresponds to the reference Figures 1 to 14 Examples of receiving devices described (e.g., Figure 1The receiving device 10004, Fig.11 Point cloud decoder and Fig.13 The receiving device Fig.14 Therefore, Fig.16 The receiving device performs the reference Figures 1 to 14 The receiving device described above operates the same or similarly. Fig.16 The receiving device can receive Fig.15 The sending device sends a signal and executes Fig.15 The reverse process of the operation of the sending device.

[0231] The receiving device according to the embodiment may perform at least one of transmission, file / segment decapsulation, point cloud decoding, and point cloud rendering.

[0232] Since the point cloud reception and point cloud rendering operations shown in the figure are similar to those of reference Figures 1 to 14 Those described are the same, so their detailed description will be omitted.

[0233] As reference Figures 1 to 14 As described, according to an embodiment, a receiving device decapsulates files and / or segments obtained from a network or a storage device. According to an embodiment, the receiving device may perform media track decapsulation for carrying data other than metadata (e.g., media data), and perform metadata track decapsulation for carrying metadata. According to an embodiment, in the case where metadata is encapsulated in a media track, metadata track decapsulation is omitted.

[0234] As reference Figures 1 to 14 As described, the receiving device can perform geometry decoding and attribute decoding on the bit stream (e.g., point cloud stream) obtained by decapsulation. Geometry decoding can be called geometry decompression, and attribute decoding can be called attribute decompression. As described above, a point can have a geometry and one or more attributes, each of which is encoded by the sending device. Therefore, the receiving device performs attribute decoding on each attribute. The figure shows that the receiving device performs one or more attribute decompressions (attribute #1 decompression,..., attribute #N decompression). The receiving device according to the embodiment can also perform auxiliary decompression. Auxiliary decompression is performed on metadata. Details of metadata and reference Figures 1 to 14 The receiving device may also perform mesh data decompression. The mesh data decompression according to an embodiment may include reference to Figures 1 to 14 The receiving device according to the embodiment can render the point cloud data output according to the point cloud decoding.

[0235] As reference Figures 1 to 14As described, the receiving device may use a separate sensing / tracking element to obtain the orientation / viewport metadata and send feedback information including the same to the sending device (e.g., Fig.15 In addition, the receiving device may perform at least one of a receiving operation, file / segment decapsulation, and point cloud decoding based on the feedback information. Figures 1 to 14 Those described are the same, so their description will be omitted.

[0236] Fig.17 An exemplary structure operatively connectable to a method / apparatus for transmitting and receiving point cloud data according to an embodiment is shown.

[0237] Fig.17 The structure of 1700 represents a configuration in which at least one of the server 1760, the robot 1710, the self-driving vehicle 1720, the XR device 1730, the smart phone 1740, the home appliance 1750, and / or the HMD 1770 is connected to the cloud network 1700. The robot 1710, the self-driving vehicle 1720, the XR device 1730, the smart phone 1740, or the home appliance 1750 is referred to as a device. In addition, the XR device 1730 may correspond to a point cloud data (PCC) device according to an embodiment or may be operably connected to a PCC device.

[0238] The cloud network 1700 may represent a network that constitutes a part of a cloud computing infrastructure or exists in a cloud computing infrastructure. Here, the cloud network 1700 may be configured using a 3G network, a 4G or Long Term Evolution (LTE) network, or a 5G network.

[0239] The server 1760 may be connected to at least one of the robot 1710 , the self-driving vehicle 1720 , the XR device 1730 , the smart phone 1740 , the home appliance 1750 , and / or the HMD 1770 via the cloud network 1700 , and may assist in at least a portion of the processing of the connected devices 1710 to 1770 .

[0240] The HMD 1770 represents one of implementation types of an XR device and / or a PCC device according to an embodiment. According to an embodiment, the HMD type device includes a communication unit, a control unit, a memory, an I / O unit, a sensor unit, and a power supply unit.

[0241] Hereinafter, various embodiments of devices 1710 to 1750 to which the above-described technology is applied will be described. Fig.17 The illustrated devices 1710 to 1750 may be operably connected / coupled to the point cloud data transmitting / receiving device according to the above-described embodiments.

[0242] <PCC+XR>

[0243] The XR / PCC device 1730 can adopt PCC technology and / or XR (AR+VR) technology, and can be implemented as an HMD, a head-up display (HUD) installed in a vehicle, a television, a mobile phone, a smartphone, a computer, a wearable device, a household appliance, a digital signage, a vehicle, a stationary robot or a mobile robot.

[0244] The XR / PCC device 1730 can analyze 3D point cloud data or image data obtained through various sensors or from external devices and generate position data and attribute data about 3D points. Thus, the XR / PCC device 1730 can obtain information about the surrounding space or real objects, and render and output XR objects. For example, the XR / PCC device 1730 can match an XR object including auxiliary information about the identified object with the identified object and output the matched XR object.

[0245] <PCC+Autonomous Driving+XR>

[0246] The autonomous vehicle 1720 can be implemented as a mobile robot, a vehicle, an unmanned aerial vehicle, etc. by applying PCC technology and XR technology.

[0247] The autonomous vehicle 1720 applying XR / PCC technology can represent an autonomous vehicle provided with means for providing XR images, or an autonomous vehicle as a control / interaction target in XR images. Specifically, as a control / interaction target in XR images, the autonomous vehicle 1720 can be distinguished from the XR device 1730 and operably connected thereto.

[0248] The autonomous vehicle 1720 having means for providing XR / PCC images can obtain sensor information from sensors including cameras, and output the generated XR / PCC images based on the obtained sensor information. For example, the autonomous vehicle 1720 can have an HUD and output XR / PCC images thereto to provide passengers with XR / PCC objects corresponding to real objects or objects presented on the screen.

[0249] In this case, when the XR / PCC object is output to the HUD, at least a part of the XR / PCC object can be output to overlap with the real object pointed by the passenger's eyes. On the other hand, when the XR / PCC object is output on a display installed in the autonomous vehicle, at least a part of the XR / PCC object can be output to overlap with the object on the screen. For example, the autonomous vehicle 1220 can output XR / PCC objects corresponding to objects such as roads, other vehicles, traffic lights, traffic signs, two-wheelers, pedestrians and buildings.

[0250] Virtual reality (VR) technology, augmented reality (AR) technology, mixed reality (MR) technology, and / or point cloud compression (PCC) technology according to an embodiment are applicable to various devices.

[0251] In other words, VR technology is a display technology that only provides CG images of real-world objects, backgrounds, etc. On the other hand, AR technology refers to a technology that displays a virtually created CG image on an image of a real object. MR technology is similar to the above-mentioned AR technology in that the virtual objects to be displayed are mixed and combined with the real world. However, MR technology is different from AR technology in that AR technology clearly distinguishes between real objects and virtual objects created as CG images and uses virtual objects as supplementary objects to real objects, while MR technology treats virtual objects as objects with properties equivalent to real objects. More specifically, an example of the application of MR technology is a holographic service.

[0252] Recently, VR, AR, and MR technologies are often referred to as extended display (XR) technologies rather than being clearly distinguished from each other. Therefore, embodiments of the present disclosure are applicable to any of VR, AR, MR, and XR technologies. Encoding / decoding based on PCC, V-PCC, and G-PCC technologies are applicable to such technologies.

[0253] The PCC method / apparatus according to the embodiment may be applied to a vehicle providing a self-driving service.

[0254] Vehicles providing self-driving services are connected to the PCC device for wired / wireless communication.

[0255] When a point cloud data (PCC) transmitting / receiving device according to an embodiment is connected to a vehicle for wired / wireless communication, the device can receive / process content data related to AR / VR / PCC services (which can be provided together with self-driving services) and send it to the vehicle. In the case where the PCC transmitting / receiving device is installed on the vehicle, the PCC transmitting / receiving device can receive / process content data related to AR / VR / PCC services and provide it to the user based on a user input signal input through a user interface device. The vehicle or user interface device according to the embodiment can receive the user input signal. The user input signal according to the embodiment may include a signal indicating a self-driving service.

[0256] Fig.18 is a block diagram illustrating an exemplary point cloud encoder.

[0257] According to an embodiment, the point cloud encoder 1800 (eg, Figure 1 Point cloud video encoder 10002, Figure 4 Point cloud encoder, reference Fig.12 , Fig.14 and Fig.15The point cloud encoder described in Figures 1 to 17 The point cloud encoder 1800 according to the embodiment may include a space divider 1810, a geometry information encoder 1820, and an attribute information encoder 1830. Fig.18 Not shown, the point cloud encoder 1800 according to the embodiment may also include one or more components to perform reference Figures 1 to 17 Describes the encoding operation.

[0258] Point cloud compression (PCC) data (or PCC data or point cloud data) is input data of the point cloud encoder 1800 and may include geometry and / or attributes. Geometry according to an embodiment is information indicating the position of a point and may be represented as a parameter of a coordinate system such as a Cartesian coordinate system, a cylindrical coordinate system, or a spherical coordinate system. According to an embodiment, geometry may be referred to as geometric information and attributes may be referred to as attribute information.

[0259] The spatial divider 1810 according to the embodiment can generate the geometry and attributes of the point cloud data. The spatial divider 1810 according to the embodiment can divide the point cloud data into one or more 3D blocks in the 3D space to store the point information of the point cloud data. The block according to the embodiment can represent at least one of a tile group, a tile, a slice, a coding unit (CU), a prediction unit (PU) or a transform unit (TU). The spatial divider 1810 according to the embodiment can perform a segmentation operation based on at least one of an octree, a quadtree, a binary tree, a ternary tree or a kd tree. The block can contain one or more points. The block according to the embodiment can be a hexahedral block with a preset width, depth and height. The size of the block according to the embodiment is changeable and is not limited to the above example. The spatial divider 1810 according to the embodiment can generate geometric information about one or more points contained in the block.

[0260] The geometry information encoder (or geometry encoder) 1820 according to the embodiment may perform geometry encoding and generate a geometry bitstream and reconstructed geometry information. In the geometry encoding according to the embodiment, the reconstructed geometry information is input to the attribute information encoder (or attribute encoder) 1830. The geometry information encoder 1820 according to the embodiment may perform a reference Figure 4 At least one operation of the coordinate transformer 40000, the quantizer 40001, the octree analyzer 40002, the surface approximation analyzer 40003, the arithmetic encoder 40003 and the geometry reconstructor (reconstructed geometry) 40005 described above. In addition, the geometry information encoder 1820 according to the embodiment may perform the reference Fig.12At least one operation of the described data input unit 12000, quantization processor 12001, voxelization processor 12002, octree occupancy code generator 12003, surface model processor 12004, intra / inter coding processor 12005, arithmetic encoder 12006 and metadata processor 12007.

[0261] The attribute information encoder 1830 according to an embodiment may generate an attribute information bitstream (or attribute bitstream) based on the reconstructed geometry information and the attributes.

[0262] The point cloud encoder according to the embodiment may send a bitstream multiplexed with a geometry information bitstream and an attribute information bitstream or a geometry information bitstream and an attribute information bitstream. As described above, the bitstream may also include signaling information related to the geometry information and the attribute information and signaling information related to the coordinate transformation. In addition, the point cloud encoder according to the embodiment may encapsulate the bitstream and send it in the form of fragments and / or files.

[0263] Fig.19 is a block diagram illustrating an exemplary geometry information encoder.

[0264] According to an embodiment, the geometry information encoder 1900 (or geometry encoder) is Fig.18 An example of a geometric information encoder 1820, and geometric encoding can be performed. Since the geometric encoding according to the embodiment is different from the reference Figures 1 to 18 The geometric coding described in the embodiment is the same or similar, so the detailed description thereof will be omitted. As shown in the figure, the geometric information encoder 1900 may include a coordinate transformer 1910, a geometric information transform quantizer 1920, a geometric information predictor 1930, a residual geometric information quantizer 1940, a geometric information entropy encoder 1950, a residual geometric information inverse quantizer 1960, a filter 1970 and a memory 1980. Although Fig.19 Not shown in the figure, the geometric information encoder 1900 according to the embodiment may also include one or more components to perform reference Figures 1 to 18 The geometric encoding of the description.

[0265] The coordinate transformer 1910 according to an embodiment may transform the received geometric information into information in a coordinate system so as to represent the position of each point indicated by the input geometric information as a position in a 3D space. Figure 4 The coordinate converter 40000 described above may be the same or similar to the operation of the coordinate converter 40000. As described above, the coordinate system according to the embodiment may include (but is not limited to) a three-dimensional Cartesian coordinate system, a cylindrical coordinate system, and a spherical coordinate system. The coordinate converter 1910 according to the embodiment may convert the set coordinate system into another coordinate system.

[0266] The coordinate transformer 1910 according to an embodiment may perform coordinate transformation on a unit such as a sequence, a frame, a patch, a slice, or a block. According to an embodiment, whether to transform coordinates and information related to the coordinate system and / or transformation may be signaled based on each sequence, frame, patch, slice, or block. Therefore, the point cloud data receiving device according to an embodiment may obtain information related to the coordinate system and / or transformation based on the coordinate transformation state of the neighboring block, the size of the block, the number of points, the quantization value, the block segmentation depth, the position of the unit, the distance between the unit and the origin, etc.

[0267] The geometric information transform quantizer 1920 according to an embodiment may quantize the geometric information presented in the coordinate system and generate transformed quantized geometric information. The geometric information transform quantizer 1920 according to an embodiment may apply one or more transformations such as position transformation and / or rotation transformation to the point position indicated by the geometric information output from the coordinate transformer 1910, and perform quantization by dividing the transformed geometric information by a quantized value. The geometric information transform quantizer 1920 may perform the transformation with Figure 4 Quantizer 40001 and / or Fig.12 The quantization value according to the embodiment may vary based on the distance between the coding unit (e.g., patch, slice, etc.) and the origin of the coordinate system or the angle with the reference direction. According to the embodiment, the quantization value may be a preset value.

[0268] The geometry information predictor 1930 according to an embodiment may calculate a prediction value (or predicted geometry information) based on a quantization value of a neighbor coding unit.

[0269] The residual geometric information quantizer 1940 may receive the transformed quantized geometric information and the residual geometric information obtained by subtracting the prediction value, and quantize the residual geometric information using the quantization value to generate quantized residual geometric information.

[0270] The geometry information entropy encoder 1950 may entropy encode the quantized residual geometry information. Entropy encoding according to an embodiment may include exponential Golomb, context adaptive variable length coding (CAVLC), and context adaptive binary arithmetic coding (CABAC).

[0271] The residual geometric information inverse quantizer 1960 may reconstruct the residual geometric information by scaling the quantized geometric information with the quantization value. The reconstructed residual geometric information and the predicted geometric information may be summed to generate the reconstructed geometric information.

[0272] The filter 1970 may filter the reconstructed geometric information. According to an embodiment, the filter 1970 may include a deblocking filter and an offset corrector. The filter 1970 according to an embodiment may perform additional filtering on a boundary between two different coding units with respect to geometric information obtained by coordinate-transforming the two coding units differently.

[0273] The memory 1980 may store the reconstructed geometric information (or restored geometric information). The stored geometric information may be provided to the geometric information predictor 1930. In addition, the reconstructed geometric information stored in the memory may be provided to the reference. Fig.18 Described attribute information encoder 1830.

[0274] Fig. 20 An exemplary attribute information encoder according to an embodiment is shown.

[0275] Fig. 20 The attribute information encoder 2000 shown can be used as a reference Fig.18 The example of the attribute information encoder 1830 described above performs attribute encoding. Figures 1 to 17 The attribute encoding described is the same or similar, so its detailed description will be omitted. As shown in the figure, the attribute information encoder 2000 according to the embodiment may include an attribute characteristic converter 2010, a geometric information mapper 2020, an attribute information converter 2030, an attribute information quantizer 2040 and an attribute information entropy encoder 2050.

[0276] The attribute characteristic converter 2010 according to an embodiment may receive attribute information and convert the characteristics (e.g., color, etc.) of the received attribute information. For example, when the attribute information includes color information, the attribute characteristic converter 2010 may convert the color space of the attribute information (e.g., from RGB to YCbCr). In addition, the attribute characteristic converter 2010 may selectively skip the conversion of the characteristics of the attribute information. The attribute characteristic converter 2010 may perform the same or similar operations as the attribute converter 40007 and / or the color conversion processor 12008.

[0277] The geometric information mapper 2020 according to the embodiment can generate reconstructed attribute information by mapping the attribute information output from the attribute characteristic converter 2010 and the received reconstructed geometric information. The geometric information mapper 2020 can generate attribute information by reconstructing the attribute information about one or more points based on the reconstructed geometric information. As described above, the geometric information about one or more points included in one voxel can be reconstructed based on the median of the voxel. Since the attribute information depends on the geometric information, the geometric information mapper 2020 reconstructs the attribute information based on the reconstructed geometric information. The geometric information mapper 2020 can perform the same or similar operations as those of the attribute transformation processor 12009.

[0278] The attribute information transformer 2030 according to an embodiment may receive and transform the reconstructed attribute information. The attribute information transformer 2030 according to an embodiment may predict the attribute information and transform the residual attribute information corresponding to the residual between the received reconstructed attribute information and the predicted attribute information using one or more transform types (e.g., DCT, DST, SADCT, RAHT).

[0279] The property information quantizer 2040 according to an embodiment may receive the transformed residual property information and generate transformed and quantized residual property information based on the quantization value.

[0280] The attribute information entropy encoder 2050 according to the embodiment may receive the transformed quantized residual attribute information, perform entropy coding on it, and output the attribute information bitstream. The entropy coding according to the embodiment may include (but is not limited to) one or more of exponential Golomb, CAVLC, and CABAC. The attribute information entropy encoder 2050 may perform the same or similar operation as the operation of the arithmetic encoder 12011.

[0281] Fig.21 An exemplary attribute information encoder according to an embodiment is shown.

[0282] Fig.21 The attribute information encoder 2100 shown corresponds to the reference Fig.18 The attribute information encoder 1830 and reference Fig. 20 An example of the attribute information encoder 2000 is described. The attribute information encoder 2100 according to the embodiment may include an attribute characteristic converter 2110, a geometry information mapper 2120, an attribute information predictor 2130, a residual attribute information converter 2140, a residual attribute information inverse converter 2145, a residual attribute information quantizer 2150, a residual attribute information inverse quantizer 2155, a filter 2160, a memory 2170, and an attribute information entropy encoder 2180. Fig.21 The attribute information encoder 2100 shown is Fig. 20 The difference of the attribute information encoder 2000 shown is that the encoder also includes a residual attribute information transformer 2140, a residual attribute information inverse transformer 2145, a residual attribute information quantizer 2150, a residual attribute information inverse quantizer 2155, a filter 2160 and a memory 2170.

[0283] The attribute property converter 2110 and the geometry information mapper 2120 according to the embodiment may perform the same Fig. 20 The operations of the attribute characteristic converter 2010 and the geometry information mapper 2120 described above are the same or similar operations. The attribute information predictor 2130 according to the embodiment may generate predicted attribute information. The residual attribute information converter 2140 may receive residual attribute information generated by obtaining a difference between the reconstructed attribute information output from the geometry information mapper 2120 and the predicted attribute information. The residual attribute information converter 2140 may transform the residual 3D block including the received residual attribute information using one or more transform types (e.g., DCT, DST, SADCT, RAHT, etc.).

[0284] The residual attribute information quantizer 2150 according to the embodiment may transform the inputted transformed residual attribute information based on the quantization value. The transformed residual attribute information may be input to the residual attribute information inverse quantizer 2155. The residual attribute information inverse quantizer 2155 according to the embodiment may transform the transformed quantized residual attribute information based on the quantization value and generate the transformed residual attribute information. The transformed residual attribute information generated by the residual attribute information inverse quantizer 2155 is input to the residual attribute inverse transformer 2145. The residual attribute inverse transformer 2145 according to the embodiment may inversely transform the residual 3D block including the transformed residual attribute information using one or more transform types (e.g., DCT, DST, SADCT, RAHT, etc.). According to the embodiment, the reconstructed attribute information may be generated by combining the inverse transformed residual attribute information and the predicted attribute information output from the attribute information predictor 2130. According to the embodiment, the reconstructed attribute information may be generated by combining the non-inverse transformed residual attribute information and the predicted attribute information. The reconstructed attribute information may be input to the filter 2160. The attribute information predictor 2130 , the residual attribute information transformer 2140 , and / or the residual attribute information quantizer 2150 according to an embodiment may perform operations identical to or similar to those of the prediction / lifting / RAHT transform processor 12010 .

[0285] The filter 2160 according to the embodiment may filter the reconstructed attribute information. The filter 2160 according to the embodiment may include a deblocking filter, an offset corrector, and an adaptive loop filter (ALF). The filter 2160 may perform the same Fig.19The operation of filter 1970 is the same or similar to the operation of filter 1970.

[0286] The memory 2170 according to an embodiment may store the reconstructed attribute information output from the filter 2160. The stored reconstructed attribute information may be provided as input data for the prediction operation of the attribute information predictor 2130. The attribute information predictor 2130 may generate predicted attribute information based on the reconstructed attribute information about the point. Although the memory 2170 is shown as one block in the figure, it may include one or more physical memories. The attribute information entropy encoder 2180 according to an embodiment may perform the same as the reference. Fig. 20 The operation of the attribute information entropy encoder 2050 described above is the same as or similar to the operation of the attribute information entropy encoder 2050.

[0287] Fig. 22 An exemplary attribute information predictor according to an embodiment is shown.

[0288] Fig. 22 The attribute information predictor 2200 shown corresponds to the reference Fig.21 An example of the attribute information predictor 2130 described. The attribute information predictor 2200 may perform operations that are the same as or similar to the operations of the attribute information predictor 2130. The attribute information predictor 2200 according to an embodiment may include an LOD configurator 2210 and a neighbor point set configurator 2220. The LOD configurator 2210 may perform operations that are the same as or similar to the operations of the LOD generator 40009. That is, as shown in the figure, the LOD configurator 2210 may receive attributes and reconstructed geometry, and configure one or more LODs based on the received attributes and reconstructed geometry. As described above with reference Figure 4 and Figure 8 As described, LOD can be generated by reorganizing points distributed in 3D space into a set of refinement levels. According to an embodiment, LOD may include one or more points distributed at regular intervals. As described above, LOD according to an embodiment indicates the level of detail of the point cloud content. Therefore, as the level indicated by the LOD (or LOD value) decreases, the details of the point cloud content may deteriorate. As the level indicated by the LOD increases, the details of the point cloud content are enhanced. That is, the LOD indicating a higher level may include points distributed at shorter intervals. According to an embodiment, a point cloud encoder (e.g., Figure 4 point cloud encoder) and point cloud decoder (e.g., Fig.11 The point cloud encoder and the point cloud decoder according to the embodiment can generate LOD to increase the attribute compression rate. Since points with similar attributes are likely to be in the neighborhood of the target point, the residual value between the predicted attribute obtained based on the neighboring points with similar attributes and the attribute of the target point is likely to be close to 0. Therefore, the point cloud encoder and the point cloud decoder according to the embodiment can generate LOD in order to select appropriate neighboring points that can be used for attribute prediction.

[0289] According to the implementation, the LOD configurator 2210 may use one or more methods to configure the LOD. As described above, the point cloud decoder (e.g., reference Figure 10 to Figure 11 The point cloud decoder described in the embodiment may also generate LOD. Therefore, information related to one or more LOD configuration methods (or LOD generation methods) according to the embodiment is included in the bitstream generated by geometry encoding and attribute encoding. The LOD output from the LOD configurator 2210 is sent to at least one of the prediction transformer / inverse transformer and the lifting transformer / inverse transformer.

[0290] The LOD configurator 2210 according to an embodiment may generate LOD based on one or more methods that may reduce the complexity of calculating the distance between points while maintaining a specific distance between points when generating the set LOD1.

[0291] The LOD configurator 2210 according to an embodiment may configure the LOD based on the Morton code of the point. As described above, the Morton code represents the coordinate values ​​(eg (x, y, z)) of the three-dimensional positions of all points as bit values ​​and is generated by mixing the bits.

[0292] According to the embodiment, the LOD configurator 2210 can generate a Morton code for each point based on the reconstructed geometry, and arrange each point in ascending order based on the Morton code. The order of the points arranged in ascending order of the Morton code can be referred to as the Morton order. The LOD configurator 2210 can configure the LOD by performing sampling on the points arranged in the Morton order. According to the embodiment, the LOD configurator can perform sampling in various ways. For example, the LOD configurator 2210 according to the embodiment can select points sequentially based on the sampling rate gap in the Morton order of the points included in each area corresponding to the node. That is, the LOD configurator can select the first arrangement point (the 0th point) according to the Morton order, and select the points separated from the first point according to the sampling rate and as many as the number of samples (for example, for the sampling rate 5, the 5th point from the first point). In addition, the LOD configurator according to the embodiment can select as many points as the number of samples based on the Morton code value of the center point of the node and the Morton code value of the neighboring point, and the Morton code value of these points is closest to the Morton code value of the center point.

[0293] As reference Fig. 9 As described above, the point cloud encoder (or attribute information predictor) according to the embodiment can perform prediction transformation coding to configure the predicted attribute (or predicted attribute value) of each point by generating a predictor of the point. That is, N predictors can be generated for N points.

[0294] When generating each LOD (or LOD set), the neighbor point set configurator 2220 according to an embodiment may search or retrieve one or more neighbor points of a point of the LOD1 set. The number of the one or more neighbor points may be X, where X is an integer greater than zero. The neighbor point according to an embodiment is a nearest neighbor (NN) point located closest to the point of the LOD1 set in 3D space and is included in the same LOD as the target LOD (e.g., LOD1) or in a LOD set at a lower level than the target LOD (e.g., LOD2). 1-1 ,LOD 1-2 ..., LOD0). The neighbor point set configurator 2220 according to an embodiment may register one or more searched neighbor points as a neighbor point set in the predictor.

[0295] The number of neighbor points according to an embodiment may be set as a maximum number of neighbor points according to a user input signal, or may be preset to a specific value according to a neighbor point search method.

[0296] According to the embodiment, the neighbor point set configurator 2220 can search for the neighbor point set in LOD0 and LOD1. Fig. 9 The neighboring points of point P3 of LOD1 are shown. Fig. 9 As shown, LOD0 includes P0, P5, P4 and P2. LOD2 includes the points of LOD0, the points of LOD1, P9, P8 and P7. When the number of neighbor points X is 3, the neighbor point set configurator 2220 Fig. 9 In the 3D space shown at the top, the three neighbor points closest to P3 are searched from the points belonging to LOD0 or LOD1. That is, the neighbor point set configurator 2220 can search P6 belonging to LOD1 (which is at the same LOD level) and P2 and P4 belonging to LOD0 (which are at a lower LOD level) as neighbor points of P3. In the 3D space, P7 is a point close to P3, but is not searched as a neighbor point because it is at a higher LOD level. The neighbor point set configurator 2220 can register the searched neighbor points P2, P4 and P6 as a neighbor point set in the predictor of P3. The method for generating a set of neighbor points according to an embodiment is not limited to this example. Information about the method for generating a set of neighbor points according to an embodiment (hereinafter referred to as neighbor point set generation information) is sent to a receiving device (for example, a bit stream including the above-mentioned encoded point cloud video data). Figure 1 The receiving device 10004, Fig.10 and Fig.11 point cloud decoder, etc.).

[0297] As described above, each point can have a predictor. A point cloud encoder according to an embodiment can encode the attribute value of the corresponding point by applying the predictor, and can generate a predicted attribute (or predicted attribute value). According to an embodiment, after LOD generation, a predictor is generated based on the searched neighbor points. The predictor is used to predict the attribute of the target point. Therefore, the predictor can generate a predicted attribute by applying weights to the attributes of the neighbor points.

[0298] For example, the predictor may calculate and register weights based on the distance value (e.g., 1 / 2 distance) between the target point (e.g., P3) and each neighbor point in the neighbor point set. As described above, since the neighbor point set of P3 includes P2, P4, and P6, the point cloud encoder (or predictor) according to the embodiment calculates the weights based on the distance value between P3 and each neighbor point. Therefore, the weight of each neighbor point is expressed as When a set of neighbor points for a predictor is configured, the point cloud encoder according to an embodiment may normalize the weights of the neighbor points using the sum of the weights of the neighbor points. For example, the sum of the weights of all neighbor points in the neighbor point set of node P3 is expressed as follows. By adding the sum of weights (total_weight) The normalized weight generated by dividing the weight of each neighbor point is expressed as

[0299] The point cloud encoder (or attribute information predictor) according to the embodiment may predict the attribute by the predictor. The predicted attribute (or predicted attribute information) according to the embodiment may be an average of values ​​obtained by multiplying the attributes of the registered neighbor points by the calculated weights, as shown in FIG. Fig. 9 The or may be a value obtained by multiplying the attribute of a specific point by a weight. According to an embodiment, after precalculating the compression result value, the point cloud encoder may selectively use the predicted attribute value to generate the minimum flow among the above attribute values. The method of predicting the attribute is not limited to the above example.

[0300] The point cloud encoder (e.g., coefficient quantizer 40011, etc.) according to the embodiment may encode the attribute value of the point, the residual of the predicted attribute value, and the information of the selected predicted attribute (or the information about the method of selecting the predicted attribute), and transmit it to the receiving device (e.g., Figure 1 The receiving device 10004 or Fig.10 and Fig.11Point cloud decoder of the embodiment). The receiving device according to the embodiment performs the same processing of LOD generation, neighbor point set generation, weighted normalization of neighbor points, and attribute prediction as performed by the transmitting device. The receiving device may predict the attribute using the same method as the method performed by the transmitting device based on the information about the selected predicted attribute. The receiving device may decode the received residual value and reconstruct the attribute value by adding the decoded residual value to the predicted attribute value.

[0301] Fig.23 An example of setting a neighbor point search range is shown.

[0302] Fig.23 is a neighbor set configurator (e.g. Fig.23 Example 2300 of setting a neighbor point search range by a neighbor point set configurator 2220). Fig.23 The arrows 2310 shown in indicate the Morton order according to an embodiment.

[0303] As described above, the point cloud encoder according to the embodiment can generate a Morton code for each point by representing the position value of the point (e.g., the coordinate value (x, y, z) in the coordinate system representing the three-dimensional space) as a bit value and mixing the bits. The points are arranged in ascending order of the Morton code. Therefore, the first point among the points arranged in the Morton order has the smallest Morton code. The neighbor point set configurator according to the embodiment can configure the sets LOD0 to LOD1. 1-1 Among the points of the set LOD1 and the points of the set LOD2, the points located before the point Px belonging to the set LOD1 according to the Morton order (i.e., the points having a Morton code equal to or less than Px) perform a neighbor point search operation to generate a neighbor point set for the point Px (2320). The neighbor point set configurator can set a neighbor point search range. The neighbor point set configurator can search for a point 2330 having a Morton code closest to the Morton code of the point Px among the points located before the point Px according to the Morton order. The neighbor point set configurator can search for a point whose Morton code is equal to or greater than the Morton code of the point Px or greater than the Morton code of the point Px among the points located after the point Px according to the Morton order. According to an embodiment, the detected point 2330 can be referred to as a center point. The method for searching the center point 2330 is not limited to this example. The neighbor point configurator according to the embodiment can shift the search range according to the method for searching the center point 2330 to increase the compression rate of the attribute encoding. Information about the method of searching for the center point 2330 according to the embodiment is included in the neighbor point set generation information and is transmitted to the receiving device (eg, Figure 1 The receiving device 10004 or Fig.10 and Fig.11When there is one LOD according to an embodiment, the neighbor point set configurator may determine a neighbor point search range based on the position of a target prediction point (eg, Px) in the Morton order.

[0304] The neighbor point set configurator may set a neighbor point search range 2335 based on the detected center point 2330. The neighbor point search range 2335 according to an embodiment may include one or more points located before and after the center point 2330 in the Morton order. Information about the neighbor point search range according to an embodiment may be included in the neighbor point set generation information and sent to the receiving device in the bitstream including the encoded point cloud video data. The neighbor point set configurator may compare the distance between the points included in the neighbor point search range 2335 determined around the center point 2330 and the point Px, and search for as many points as the configured maximum number of neighbor points (e.g., three points) closest to the point Px. When the number of LODs is 1, the neighbor point set configurator may search for neighbor points among the points located before the point Px in the Morton order. The neighbor point set configurator may compare the distance between the point Px and the points located before the point Px as many as the number of points in the neighbor point search range, and search for as many points closest to the point Px as the configured maximum number of neighbor points. The neighbor point set searching method according to the embodiment is not limited to this example.

[0305] A neighbor point search range according to the implementation can be set for each LOD. For example, the neighbor point search range of LOD1 can be set to 128, and the neighbor point search range of LOD 1-1 The neighbor point search range of may be set to a value equal to or different from 64. The neighbor point search range of each LOD according to an embodiment may be set by applying a rate of change based on the level represented by each LOD to a reference neighbor point search range. Information about the value of the neighbor point search range of each LOD, information about the rate of change for setting the neighbor point search range of each LOD, and information about a calculation formula for setting the neighbor point search range of each LOD based on the rate of change are included in the neighbor point set generation information according to an embodiment. The neighbor point set generation information is transmitted to a receiving device (e.g., a video stream including encoded point cloud video data) in a bitstream. Figure 1 The receiving device 10004 or Fig.10 and Fig.11 Point cloud decoder).

[0306] As the level represented by the LOD increases, the neighbor point search range of each LOD according to an embodiment may increase. A calculation formula for setting the neighbor point search range for each LOD according to an embodiment is given as follows.

[0307] NN-Search-Range=searchRange*((lodIndex+1.0) / levelOfDetailCount)

[0308] As the level represented by the LOD decreases, the neighbor point search range of each LOD according to an embodiment may decrease. A calculation formula for setting the neighbor point search range for each LOD according to an embodiment is given as follows.

[0309] NN-Search-Range=searchRange*((levelOfDetailCount-lodIndex) / levelOfDetailCount)

[0310] The parameters of the above calculation formula are described below.

[0311] NN-Search-Range indicates the neighbor point search range of the LOD to be calculated. lodIndex is the index (level) of the LOD, which is an integer. levelOfDetailCount represents the total number of LODs, and searchRange represents the reference neighbor point search range.

[0312] The neighbor point configurator can be found in the reference Figure 21 to Figure 23 The neighbor point search range described in the embodiment searches for a neighbor point close to the target prediction point (e.g., point Px) among the points in the neighbor point search range. The neighbor point configurator according to the embodiment may search the entire neighbor point search range, or may divide the neighbor point search range and search the divided parts. Information about the neighbor point search method used by the neighbor point configurator may be included in the neighbor point set generation information and transmitted to the receiving device (e.g., Figure 1 The receiving device 10004 or Fig.10 and Fig.11 Therefore, the receiving device can search for neighbor points based on the neighbor point set generation information.

[0313] Fig.24 An exemplary neighbor point search process according to an embodiment is shown.

[0314] Fig.24 shows the configuration of the neighbor point based on the Fig. 22 An example of a process 2400 of applying a neighbor point configurator 2220 of the LOD generation method to search for neighbor points within a sampling range generated by the LOD generation method. Fig.23 The neighbor point search method described is the same or similar. Fig.24Example 2400 shows points included in a first sampling range 2410, a second sampling range 2420, and a third sampling range 2430. The first sampling range 2410 may include points P0, P1, P2, and P3, the second sampling range 2420 may include points P4, P5, P6, and P7, and the third sampling range 2430 may include points P8, P9, and other points (not shown). When point P2 2415 is selected from the first sampling range 2410, the neighbor point configurator may register point P2 2415 as a neighbor point of points P0, P1, and P3 included in the first sampling range 2410. When point P5 2425 is selected from the second sampling range 2420, the neighbor point configurator may set point P5 2425 as a neighbor point of points P0, P1, and P3 included in the first sampling range 2410 and points P4, P6, and P7 included in the second sampling range 2420. Therefore, the neighbor point configurator according to the embodiment may search for and register the point P2 2415 selected from the first sampling range 2410, the point P5 2425 selected from the second sampling range 2420, and the point P8 2435 selected from the third sampling range 2430 as neighbor points of the point P4 included in the second sampling range 2420. The neighbor point search method according to the embodiment eliminates the need to calculate the distance between all other points and the point Px (e.g., point P4), thereby reducing the complexity of the neighbor point search.

[0315] Fig.25 An exemplary neighbor point search process according to an embodiment is shown.

[0316] Fig.25 shows the configuration of the neighbor point based on the Fig. 22 The neighbor point configurator 2220 of the embodiment is applied to the sampling range generated by the LOD to search for neighbor points. The neighbor point search method according to this embodiment is similar to the reference Fig.23 The neighbor point search method described is the same or similar. Fig.25 The upper part is the reference Fig.23 The self-registration example 2500 described herein, while Fig.25 The lower part is the reference Fig.23 Example 2540 of paired registration is described. Fig.25 Examples 2500 and 2540 show points included in a first sampling range 2510, a second sampling range 2520, and a third sampling range 2530. The first sampling range 2510 may include points P0, P1, P2, and P3, the second sampling range 2520 may include points P4, P5, P6, and P7, and the third sampling range 2530 may include points P8, P9, and other points (not shown).

[0317] The example 2500 of self-registration according to an embodiment shows a process of registering a point selected from each sampling range as a neighbor point of other points included in the same sampling range. When point P2 2515 is selected from the first sampling range 2510, the neighbor point configurator registers point P2 2515 as a neighbor point of points P0, P1, and P3 included in the first sampling range 2510. In addition, when point P5 2525 is selected from the second sampling range 2520, the neighbor point configurator registers point P5 2525 as a neighbor point of points P4, P6, and P7 included in the second sampling range 2520. The neighbor point configurator according to an embodiment can perform registration on the third sampling range 2530 in the same manner.

[0318] The example 2540 of pairwise registration according to an embodiment shows a process of searching for neighbor points of points included in different sampling ranges based on the distance between the points selected for the corresponding sampling ranges. The neighbor point configurator according to an embodiment calculates the distance between the point P2 2515 selected from the first sampling range 2510 and the point P5 2525 selected from the second sampling range 2520. When the calculated distance is less than a preset threshold, the neighbor point configurator may register the point P2 2515 as a neighbor point (2545) of the points P4, P6, and P7 included in the second sampling range 2520. In addition, the neighbor point configurator may register the point P5 2525 as a neighbor point (2545-1) of the points P0, P1, and P3 included in the first sampling range 2510. Information about the neighbor point search method such as a preset threshold is included in the neighbor point set generation information and is sent to a receiving device (e.g., a bit stream including the encoded point cloud video data). Figure 1 The receiving device 10004 or Fig.10 and Fig.11 Therefore, the receiving device can search for neighbor points based on the neighbor point set generation information.

[0319] Fig.26 An exemplary structural diagram of a point cloud compression (PCC) bitstream is shown.

[0320] As described above, the point cloud data transmitting device (for example, referring to Figure 1 , Fig.12 , Fig.14 and Fig.15 The point cloud data transmitting device described in the embodiment can transmit the encoded point cloud data in the form of a bit stream 2600. The bit stream 2600 according to this embodiment may include one or more sub-bit streams.

[0321] Point cloud data sending device (for example, reference Figure 1 , Fig.12 , Fig.14 and Fig.15The point cloud data sending device described herein can divide the image of the point cloud data into one or more packets, and send the packets through the network, taking into account the attenuation of the transmission channel. According to an embodiment, the bitstream 2600 may include one or more packets (e.g., network abstraction layer (NAL) units). Therefore, even when some packets are lost in a poor network environment, the point cloud data receiving device can use the remaining packets to reconstruct the image. The point cloud data can be divided into one or more slices or one or more tiles to be processed. The tiles and slices according to the embodiment are areas for performing point cloud compression compilation by segmenting pictures of point cloud data. The point cloud data sending device can provide high-quality point cloud content by processing the data corresponding to each segmented area of ​​the point cloud data according to the importance of each area. That is, the point cloud data sending device according to the embodiment can perform point cloud compression compilation with better compression efficiency and appropriate delay on the data corresponding to the area important to the user.

[0322] A patch according to an embodiment refers to a cube in a three-dimensional space (e.g., a bounding box) in which point cloud data is distributed. A slice according to an embodiment is a series of syntax elements representing some or all of the encoded point cloud data, and refers to a collection of points that can be independently encoded or decoded. A slice according to an embodiment may include data sent in a packet, and may include a geometry data unit and a number of attribute data units having a size equal to zero. A patch according to an embodiment may include one or more slices.

[0323] The bitstream 2600 according to an embodiment may include signaling information including a sequence parameter set (SPS) for sequence level signaling, a geometry parameter set (GPS) for signaling of geometry information coding, an attribute parameter set (APS) for signaling of attribute information coding, and a patch parameter set (TPS) for patch level signaling as well as one or more slices.

[0324] The SPS according to an embodiment is encoding information about the entire sequence including a profile and a level, and may include comprehensive information about the entire file, such as picture resolution and video format.

[0325] According to an embodiment, a slice (e.g., Fig.30 The slice 0) includes a slice header and slice data. The slice data may include a geometry bit stream (Geom0 0 ) and one or more attribute bitstreams (Attr0 0 ,Attr1 0). The geometry bitstream may include a header (e.g., a geometry slice header) and a payload (e.g., geometry slice data). The header of the geometry bitstream according to the embodiment may include identification information (geom_geom_parameter_set_id) of a parameter set included in GPS, a tile identifier (geom_tile id), a slice identifier (geom_slice_id), and information related to the data included in the payload. The attribute bitstream may include a header (e.g., an attribute slice header or an attribute tile header) and a payload (e.g., attribute slice data or attribute tile data).

[0326] As reference Figure 22 to Figure 25 As described, the point cloud encoder and point cloud decoder according to the embodiment generate a neighbor point set by searching for one or more neighbor points of a point after LOD generation for attribute prediction. Fig.30 The bitstream shown in may include reference Figures 18 to 29 The neighbor point set described generates information.

[0327] Fig. 27 An exemplary syntax of an APS according to an embodiment is shown.

[0328] The APS according to an embodiment may include neighbor point set generation information 2720 and 2730 . Fig. 27 Examples 2710 , 2720 of the syntax of the APS according to an embodiment are shown and may include the following information (or fields, parameters, etc.).

[0329] aps_attr_parameter_set_id specifies the identifier of the APS for reference by other syntax elements. aps_attr_parameter_set_id has a value in the range of 0 to 15. Fig. 27 As shown, since one or more attribute bitstreams are included in the bitstream, a field (eg, ash_attr_parameter_set_id) having the same value as aps_attr_parameter_set_id may be included in the header of each attribute bitstream.

[0330] The point cloud decoder according to an embodiment may acquire an APS corresponding to each attribute bitstream based on aps_attr_parameter_set_id and process the attribute bitstream.

[0331] aps_seq_parameter_set_id specifies the value of sps_seq_parameter_set_id of the active SPS. aps_seq_parameter_set_id has a value in the range of 0 to 15.

[0332] attr_coding_type indicates the attribute coding type for a given value of attr_coding_type. In a bitstream according to this embodiment, the value of attr_coding_type is equal to 0, 1, or 2. Other values ​​of attr_coding_type may be reserved for future use by ISO / IEC. Therefore, a decoder according to an embodiment may ignore the reserved values ​​of attr_coding_type described above. Among these values, 0 may indicate the prediction weight lifting transform compilation as the attribute coding type, 1 may indicate the RAHT transform compilation as the attribute coding type, and 2 may indicate the fixed weight lifting transform compilation as the attribute coding type.

[0333] When attr_coding_type is equal to 0 or 2, the following parameters are given.

[0334] num_pred_nearest_neighburs indicates the maximum number of nearest neighbor points used to predict the transformation. Figure 22 to Figure 25 As described above, the maximum number of neighbor points may be set and changed according to a user input signal.

[0335] Hereinafter, the neighbor point set generation information 2720 according to an embodiment is described.

[0336] neighbor_search_origin_selection_type indicates the center point selection method used to set the neighbor point range. Fig.23The method for selecting the center point described is the same, so it will not be described again in this article. When the value of neighbor_search_origin_selection_type is 1, neighbor_search_origin_selection_type indicates that the center point selection method is a method of selecting a point with a Morton code closest to the Morton code of the target prediction point. When the value of neighbor_search_origin_selection_type is 2, neighbor_search_origin_selection_type indicates that the center point selection method is a method of selecting a point whose Morton code is greater than or equal to the Morton code of the target prediction point. The value of neighbor_search_origin_selection_type is not limited to this example. neighbor_search_range indicates the neighbor point search range. neighbor_search_range can indicate the number of points or the number of points in the sampling unit that are located before or after the position of the selected center point in Morton order. Reference Figure 22 to Figure 26 The described neighbor point set configurator may search for points within a neighbor point search range and register neighbor points. According to an embodiment, a neighbor point search range (neighbor_search_range[idx] 2730) may be set for each LOD.

[0337] neighbor_search_range_type indicates the type of neighbor search range set for each LOD. Fig.23 As described above, the neighbor point search range according to this embodiment can be set for each LOD. For example, the same or different neighbor point search ranges can be set for each LOD, such as for LOD1, the neighbor point search range is set to 128, and for LOD 1-1The neighbor point search range is set to 64. In addition, the neighbor point search range for each LOD according to the embodiment can be set by applying the change rate based on the level represented by each LOD to the reference neighbor point search range. When the value of neighbor_search_range_type is 0, neighbor_search_range_type indicates that the neighbor point search range is the same for each LOD. When the value of neighbor_search_range_type is 1, neighbor_search_range_type indicates that the neighbor point search range increases with the increase of the LOD level. When the value of neighbor_search_range_type is 2, neighbor_search_range_type indicates that the neighbor point search range decreases with the increase of the LOD level. When the value of neighbor_search_range_type is 3, neighbor_search_range_type indicates that the neighbor point search range increases at a specific rate as the LOD level increases. When the value of neighbor_search_range_type is 3, the neighbor point set generation information 2720 may further include neighbor_search_range_rate_per_lod. neighbor_search_range_rate_per_lod indicates a specific rate value when the neighbor search range increases at a specific rate as the LOD level increases. The value of neighbor_search_range_type is not limited to this example.

[0338] neighbor_search_type indicates the type of neighbor search method. The neighbor search method according to this embodiment is the same as that of reference Figure 22 to Figure 25 The neighbor point search method described is the same. The type of the neighbor point search method according to the embodiment corresponds to any one of the five types. According to the value of neighbor_search_type, neighbor_search_type indicates the type of each neighbor point search method.

[0339] When the value of neighbor_search_type is 1, neighbor_search_type indicates an all-point search method. The all-point search method according to an embodiment is a method of calculating the distances between all points within a neighboring point search range and a target prediction point (eg, Px) and searching for a point closest to the target prediction point as a neighbor point.

[0340] When the value of neighbor_search_type is 2, neighbor_search_type indicates a method of searching for points selected from a sampling range as neighbor points. In the method of searching for points selected from a sampling range as neighbor points, for points included in the neighbor point search range, points selected from each sampling range are detected as neighbor points for a target prediction point (e.g., point Px). Figure 22 to Figure 24 Those details described are the same and therefore will not be described again in this article.

[0341] When the value of neighbor_search_type is 3, neighbor_search_type indicates a method of preliminarily searching for points in a bounding box of each area obtained by dividing the neighbor point search range. In the method of preliminarily searching for points in a bounding box of each area obtained by dividing the neighbor point search range, points located in the neighbor point search range are divided into one or more groups, a bounding box is obtained for each area of ​​the group, the distance between the target prediction point (e.g., point Px) and the center position value of the bounding box in each group is calculated, and neighbor points are detected among points having a calculated distance value less than a preset threshold within the bounding box. Specific details and reference Figure 22 to Figure 24 Those details described are the same and therefore will not be described again here.

[0342] When the value of neighbor_search_type is 4, neighbor_search_type indicates a method for performing a preliminary search based on the division of the neighbor point search range based on the octree and the center position value of each octree node. In the method for performing a preliminary search based on the division of the neighbor point search range based on the octree and the center position value of each octree node, the points within the neighbor point search range are divided into groups according to the depth of the octree occupancy code and the nodes belonging to the corresponding depth, the center position value of each group is obtained, the distance between the target prediction point (e.g., point Px) and the center position value of each group is calculated, and when the calculated distance value is less than a preset value (e.g., a specific threshold), the neighbor point is detected from the points belonging to the corresponding group. Specific details and reference Figure 22 to Figure 24 Those details described are the same and therefore will not be described again here.

[0343] When the value of neighbor_search_type is 5, neighbor_search_type indicates a preliminary search method based on calculation of distances between points selected from a sampling range. In the preliminary search method based on calculation of distances between points selected from a sampling range, the distances between points selected from a sampling range are calculated, and when the calculated distances are less than a preset limit, the selected points are registered as neighbor points of points in another sampling range. Specific details and reference Figure 22 to Figure 25 Those details described are the same, so they will not be described here. The value of neighbor_search_type is not limited to this example.

[0344] When neighbor_search_type indicates a method of preliminarily searching for points in a bounding box in each area obtained by dividing the neighbor point search range, the neighbor point set generation information 2720 may further include neighbor_search_group_num_points. neighbor_search_group_num_points indicates the number of points in each group obtained by grouping. When neighbor_search_type does not indicate an all point search method and a method of searching points selected from a sampling range as neighbor points, the neighbor point set generation information 2720 may further include neighbor_search_distance_threshold_auto_flag and neighbor_search_distance_threshold. As described above, the method of preliminarily searching for points in a bounding box in each area obtained by dividing the neighbor point search range (neighbor_search_type value is 3), the method of performing a preliminary search based on the division of the neighbor point search range based on the octree and the center position value of each octree node (neighbor_search_type value is 4), and the preliminary search method based on the calculation of the distance between points selected from the sampling range (neighbor_search_type value is 5) include a process of comparing the distance between each point and the target prediction point (e.g., point Px) with a threshold. neighbor_search_distance_threshold_auto_flag indicates whether the threshold can be adjusted according to the content. neighbor_search_distance_thresholds indicates the threshold.

[0345] max_num_direct_predictors indicates the number of predictors to be used for direct prediction. max_num_direct_predictors has a value in the range of 0 to num_pred_nearest_neighburs. The value of the variable MaxNumPredictors used in the decoding operation is as follows.

[0346] MaxNumPredictors=max_num_direct_predictors+1

[0347] lifting_search_range indicates the search range for lifting.

[0348] lifting_quant_step_size indicates the quantization step size of the first component of the attribute. The value of lifting_quant_step_size can be in the range of 1 to xx.

[0349] When the attribute is color, lifting_quant_step_size_chroma may indicate the quantization step size used for the chroma components of the attribute. The value of lifting_quant_step_size_chroma may be in the range of 1 to xx.

[0350] lod_binary_tree_enabled_flag indicates whether the binary tree should be used for LOD generation.

[0351] num_detail_levels_minus1 indicates the number of LODs used for attribute compilation. num_detail_levels_minus1 may have a value in the range of 0 to xx. The following "for" statement is information about each LOD.

[0352] sampling_distance_squared[idx] may indicate the square of the sampling distance for each LOD indicated by idx. According to an embodiment, idx has a value in the range of 0 to the number of LODs for attribute coding indicated by num_detail_levels_minus1. sampling_distance_squared has a value in the range of 0 to xx.

[0353] Neighbor_search_range[idx] 2730 indicates a neighbor point search range set for each LOD identified by idx. The neighbor point search range may indicate the number of points located before and after the position of the center point selected in the Morton order or the number of points in a sampling unit.

[0354] When attribute_coding_type is equal to 0, the relevant parameters are given as follows.

[0355] adaptive_prediction_threshold indicates the prediction threshold.

[0356] When attribute_coding_type is equal to 1, the relevant parameters are given as follows.

[0357] raht_depth indicates the number of LODs used for RAHT. The value of depthRAHT is in the range of 1 to xx (arbitrary value).

[0358] raht_quant_step_size indicates the quantization step size of the first component of the attribute. The value of rate_quant_step_size is in the range of 1 to xx (arbitrary value).

[0359] raht_quant_step_size_chroma may indicate the quantization step size of the chroma component when RAHT is applied.

[0360] aps_extension_present_flag is a flag whose value is 0 or 1.

[0361] aps_extension_present_flag equal to 1 indicates that the aps_extension_data syntax structure is present in the APS RBSP syntax structure. aps_extension_present_flag equal to 0 indicates that the syntax structure is not present. When the syntax structure is not present, the value of aps_extension_present_flag is inferred to be equal to 0.

[0362] aps_extension_data_flag may have any value. Depending on the implementation, the presence and value of this field may not affect the decoder performance.

[0363] The LOD configurator (e.g., LOD configurator 2210) according to the embodiment may apply different neighbor point set generation methods to the LOD generated for each tile or slice. As described above, since the point cloud data receiving device should also generate the LOD and the neighbor point set of the points belonging to the LOD, the bitstream according to the embodiment may further include neighbor point set generation information.

[0364] Fig.28 An exemplary syntax of a TPS according to an embodiment is shown.

[0365] When the neighbor point set configurator according to an embodiment applies different neighbor point set generation methods to each tile, the TPS according to an embodiment may further include neighbor point set generation information (e.g., reference Fig. 27 The neighbor point set described generates information 2720 and 2730). Fig.28 An example 2800 of a syntax for TPS according to an embodiment is shown, and the syntax may include the following information (or fields, parameters, etc.).

[0366] num_tiles indicates the number of tiles signaled for the bitstream. When there are no tiles signaled for the bitstream, the value of this information is inferred to be 0. The following are the signaling parameters for each tile.

[0367] tile_bounding_box_offset_x[i] indicates the x offset of the i-th tile in the Cartesian coordinate system. When this parameter is not present, the value of tile_bounding_box_offset_x[0] is inferred to be the value of sps_bounding_box_offset_x included in the SPS.

[0368] tile_bounding_box_offset_y[i] indicates the y offset of the i-th tile in the Cartesian coordinate system. When this parameter is not present, the value of tile_bounding_box_offset_y[0] is inferred to be the value of sps_bounding_box_offset_y included in the SPS.

[0369] tile_bounding_box_offset_z[i] indicates the z offset of the i-th tile in the Cartesian coordinate system. When this parameter is not present, the value of tile_bounding_box_offset_z[0] is inferred to be the value of sps_bounding_box_offset_z included in the SPS.

[0370] tile_bounding_box_scale_factor[i] indicates the scale factor associated with the i-th tile in the Cartesian coordinate system. When this parameter is not present, the value of tile_bounding_box_scale_factor[0] is inferred to be the value of sps_bounding_box_scale_factor.

[0371] tile_bounding_box_size_width[i] indicates the width of the i-th tile in Cartesian coordinates. When this parameter is not present, the value of tile_bounding_box_size_width[0] is inferred to be the value of sps_bounding_box_size_width.

[0372] tile_bounding_box_size_height[i] indicates the height of the i-th tile in Cartesian coordinates. When this parameter is not present, the value of tile_bounding_box_size_height[0] is inferred to be the value of sps_bounding_box_size_height.

[0373] tile_bounding_box_size_depth[i] indicates the depth of the i-th tile in Cartesian coordinates. When this parameter is not present, the value of tile_bounding_box_size_depth[0] is inferred to be the value of sps_bounding_box_size_depth.

[0374] As shown in the figure, the TPS according to the embodiment may include neighbor point set generation information 2810. The neighbor point set generation information 2810 according to the embodiment is applied to each tile. The neighbor point set generation information 2810 is compared with the reference Fig. 27 The described neighbor point set generation information 2720 and 2730 are the same, so a detailed description thereof will be avoided here.

[0375] Fig.29 An exemplary syntax of an attribute header according to an embodiment is shown.

[0376] Fig.29 The Attribute Header Syntax 2900 is included by reference Fig.26 An example of the syntax describing the information sent in the header of the attribute bitstream.

[0377] When the LOD configurator according to an embodiment applies a neighbor point set generation method differently for each slice, the attribute header according to an embodiment may further include LOD configuration information 2910 (for example, referring to Fig. 27 Neighbor point set generation information 2720, 2730 and reference Fig.28 The neighbor point set described generates information 2810). Fig.29 The exemplary syntax 2900 of the attribute header shown in may include the following information (or fields, parameters, etc.).

[0378] ash_attr_parameter_set_id has the same value as aps_attr_parameter_set_id of the active APS.

[0379] ash_attr_sps_attr_idx may specify the value of sps_seq_parameter_set_id of the active SPS. The value of ash_attr_sps_attr_idx is in the range of 0 to the value of sps_num_attribute_sets included in the active SPS.

[0380] ash_attr_geom_slice_id specifies the value of the geometry slice ID (eg, geom_slice_id).

[0381] As shown in the figure, the attribute header according to the embodiment may include neighbor point set generation information 2910. The neighbor point set generation information 2910 according to the embodiment is applied to each attribute bitstream (or attribute slice data) included in each slice. Fig. 27 Neighbor point set generation information 2720 and 2730 described and reference Fig.28 The neighbor point set generation information 2810 described is the same, so its detailed description will be avoided.

[0382] Fig.30 is a block diagram illustrating an exemplary point cloud decoder.

[0383] The point cloud decoder 3000 according to the embodiment performs the same Figures 1 to 17 The decoder described (eg, reference Figure 1 , Figure 10 to Figure 11 , Fig.13 , Fig.14 and Fig.16 In addition, the point cloud decoder 3000 may perform the same or similar decoding operations as those described in the reference Fig.18 The decoding operation corresponds to the reverse process of the encoding operation of the described point cloud encoder 1800. The point cloud decoder 3000 according to the embodiment may include a space divider 3010, a geometry information decoder (or geometry decoder) 3020, and an attribute information decoder (or attribute decoder) 3030. Fig.30 Not shown, the point cloud decoder 3000 according to the embodiment may further include one or more components to perform reference Figures 1 to 17 Describes the decoding operation.

[0384] The space divider 3010 according to the embodiment may be based on the point cloud data transmitting device according to the embodiment (for example, reference Figure 1 , Fig.12 , Fig.14 and Fig.15 The point cloud data transmitting device described in the present invention) receives the signaling information (for example, about the point cloud data transmitting device described in the present invention) received by the reference Fig.18 The space is segmented using information of the segmentation operation performed by the spatial segmentor 1810 described in the specification or the segmentation information derived (generated) by the point cloud decoder 3000. As described above, the segmentation operation of the spatial segmentor 1810 of the point cloud encoder 1800 can be based on at least one of an octree, a quadtree, a binary tree, a ternary tree, or a kd tree.

[0385] The geometry information decoder 3020 according to the embodiment may decode the input geometry bitstream and reconstruct the geometry information. The reconstructed geometry information may be input to the attribute information decoder. The geometry information decoder 3020 according to the embodiment may perform reference Fig.11 The operations of the arithmetic decoder (arithmetic decoding) 11000, the octree synthesizer (synthesized octree) 11001, the surface approximation synthesizer (synthesized surface approximation) 11002, the geometry reconstructor (reconstructed geometry) 11003, and the coordinate inverse transformer (inversely transformed coordinates) 11004 described above. In addition, the geometry information decoder 3020 according to the embodiment may perform the operation of referring to Fig.13 The operations of the arithmetic decoder 13002, the occupancy code-based octree reconstruction processor 13003, the surface model processor (triangle reconstruction, upsampling, voxelization) 13004, and the inverse quantization processor 13005 described above. Alternatively, the geometric information decoder 3020 according to the embodiment may perform the operation of the reference Fig.16 Describe the operation of the point cloud decoder.

[0386] The attribute information decoder 3030 according to the embodiment may decode the attribute information based on the attribute information bitstream and the reconstructed geometric information. The attribute information decoder 3030 according to the embodiment may perform the same operation as that included in the Fig.11 The attribute information decoder 3030 according to the embodiment may perform the same or similar operations as those included in the arithmetic decoder 11005, the inverse quantizer 11006, the RAHT transformer 11007, the LOD generator 11008, the inverse lifter 11009 and / or the color inverse transformer 11010 in the point cloud decoder. Fig.13 The operations of the arithmetic decoder 13007, the inverse quantization processor 13008, the prediction / lifting / RAHT inverse transform processor 13009 and the color inverse transform processor 13010 in the receiving device are the same or similar operations.

[0387] The point cloud decoder 3000 can output final PCC data based on the reconstructed geometric information and the reconstructed attribute information.

[0388] Fig.31 is a block diagram illustrating an example of a geometry information decoder.

[0389] The geometry information decoder 3100 according to the embodiment is Fig.30 The geometric information decoder 3100 according to the embodiment may perform the same or similar operations as those of the geometric information decoder 3020. Fig.19The decoding operation is the reverse of the encoding operation of the geometry information encoder 1900 described above. The geometry information decoder 3100 according to the embodiment may include a geometry information entropy decoder 3110, a geometry information inverse quantizer 3120, a geometry information predictor 3130, a filter 3140, a memory 3150, a geometry information inverse transform / inverse quantizer 3160, and a coordinate inverse transformer 3170. Fig.31 Not shown, the geometry information decoder 3100 according to the embodiment may further include one or more elements to perform reference Figures 1 to 31 Describes the geometry decoding operation.

[0390] According to an embodiment, the geometry information entropy decoder 3110 may generate quantized residual geometry information by entropy decoding the geometry information bitstream. The geometry information entropy decoder 3110 may be executed as a reference. Fig.19 The entropy decoding operation of the inverse process of the entropy encoding performed by the described geometry information entropy encoder 1905. As described above, the entropy encoding operation according to the embodiment may include the indexes Golomb, CAVLC and CABAC and the entropy decoding operation indexes Golomb, CAVLC and CABAC corresponding to the entropy encoding operation. The geometry information entropy decoder 3110 according to the embodiment can decode information related to geometry coding included in the geometry information bitstream (for example, information related to generation of predicted geometry information, information related to quantization (for example, quantization value, etc.), signaling information related to coordinate transformation, etc.).

[0391] The residual geometric information inverse quantizer 3120 according to an embodiment may generate residual geometric information or geometric information by performing an inverse quantization operation on the quantized residual geometric information based on quantization-related information.

[0392] The geometric information predictor 3130 according to an embodiment may generate predicted geometric information based on information related to generation of predicted geometric information output from the geometric entropy decoder 3110 and previously decoded geometric information stored in the memory 3150. The geometric information predictor 3130 according to an embodiment may include an inter-frame predictor and an intra-frame predictor. The inter-frame predictor according to an embodiment may perform inter-frame prediction on the current prediction unit (e.g., node, etc.) based on information required for inter-frame prediction of the current prediction unit (e.g., node, etc.) provided by the geometric information encoder (e.g., the geometric information encoder 1900), in the current space including the current prediction unit, based on information included in at least one of the spaces before or after the current space (e.g., frame, picture, etc.). The intra-frame predictor according to an embodiment may generate predicted geometric information based on geometric information about a point in the current space based on information related to intra-frame prediction in the prediction unit provided by the geometric information encoder 1900.

[0393] The filter 3140 according to an embodiment may filter the reconstructed geometric information generated by combining the predicted geometric information generated based on the filtering related information and the reconstructed residual geometric information. The filtering related information according to the embodiment may be signaled from the geometric information encoder 1900. Alternatively, the geometric information decoder 3100 according to the embodiment may derive and calculate the filtering related information during the decoding process.

[0394] The memory 3150 according to an embodiment may store the reconstructed geometric information. The inverse geometric transform quantizer 3160 according to an embodiment may inverse transform-quantize the reconstructed geometric information stored in the memory 3150 based on the quantization-related information.

[0395] The coordinate system inverse transformer 3170 according to an embodiment may inversely transform the coordinates of the inverse-transformed quantized geometric information based on the coordinate transformation related information provided by the geometric information entropy decoder 3110 and the reconstructed geometric information stored in the memory 3150, and output the geometric information.

[0396] Fig.32 is a block diagram illustrating an exemplary attribute information decoder.

[0397] The attribute information decoder 3200 according to the embodiment is Fig.30 The attribute information decoder 3200 according to the embodiment may perform the same or similar operation as that of the attribute information decoder 3030. Fig. 20 and Fig.21 The attribute information encoder 2000 and the attribute information encoder 2100 are described as decoding operations that are opposite to the encoding operations. The attribute information decoder 3200 according to the embodiment may include an attribute information entropy decoder 3210, a geometry information mapper 3220, a residual attribute information inverse quantizer 3230, a residual attribute information inverse transformer 3240, a memory 3250, an attribute information predictor 3260, and an attribute information inverse transformer 3270. Although Fig.32 , but the attribute information decoder 3200 according to an embodiment may further include one or more elements to perform reference Figures 1 to 31 Describes the attribute decoding operation.

[0398] The attribute information entropy decoder 3210 receives the attribute information bitstream and performs entropy decoding to generate transform-quantized attribute information.

[0399] The geometry information mapper 3220 generates residual attribute information by mapping the transformed quantized attribute information and the reconstructed geometry information.

[0400] The residual property information inverse quantizer 3230 may inverse quantize the residual property information based on the quantization value.

[0401] The residual property information inverse transformer 3240 may inversely transform the residual 3D block including the property information obtained through inverse quantization by performing transform coding such as DCT, DST, SADCT, or RAHT.

[0402] The memory 3250 may add the predicted attribute information output from the attribute information predictor 3260 and store the inverse-transformed attribute information. Alternatively, the memory 3250 may add and store the predicted attribute information and the attribute information without inverse-transforming the attribute information.

[0403] The attribute information predictor 3260 may generate predicted attribute information based on the attribute information stored in the memory 3250. The attribute information predictor 3260 may generate predicted attribute information by performing entropy decoding.

[0404] The attribute information inverse transformer 3270 may receive type and transformation information about attribute information from the attribute information entropy decoder 3110 to perform various types of color inverse transform coding.

[0405] Fig.33 is an exemplary flow chart of a point cloud data processing method according to an embodiment.

[0406] Fig.33 Flow chart 3300 shows a point cloud data processing device (eg, referring to Figure 1 , Fig.11 , Fig.14 , Fig.15 and Figures 18 to 22 The point cloud data processing device according to the embodiment may perform the same method as that of the point cloud data transmitting device or the point cloud data encoder described in the present invention. Figures 1 to 32 The encoding operations described are the same or similar operations.

[0407] The point cloud data processing apparatus according to the embodiment may encode point cloud data including geometric information and attribute information (3310). The geometric information according to the embodiment is information indicating the position of a point of the point cloud data. The attribute information according to the embodiment is information indicating the attribute of a point of the point cloud data.

[0408] The point cloud data processing device according to the embodiment can encode the geometric information and encode the attribute information. Figures 1 to 32 In addition, the point cloud data processing device performs the same or similar operations as those described in the geometric information encoding operation. Figures 1 to 32The point cloud data processing device according to the embodiment can generate at least one LOD by segmenting the points, and generate a neighbor point set belonging to each LOD point. Since the neighbor point set generation scheme or method according to the embodiment is the same as that of the reference Figures 22 to 25 The described neighbor point set generation schemes or methods are the same or similar, so their detailed description will be omitted.

[0409] The point cloud data processing apparatus according to the embodiment may transmit a bit stream (3320) including encoded point cloud data.

[0410] Since the structure of the bitstream according to the embodiment is similar to the reference Fig.26 The structure described is the same as that described above, so its detailed description will be omitted. The bitstream according to the embodiment may include neighbor point set generation information (for example, reference Figure 27 to Figure 29 The neighbor point set generation information according to the embodiment may be sent to the receiving device through APS, TPS, attribute header, etc., as shown in FIG. Figures 27 to 32 described.

[0411] According to an embodiment, the neighbor point set generation information may include type information (for example, reference Figures 27 to 29 The neighbor_search_origin_selection_type described above), search range information indicating a neighbor point search range for searching for neighboring points located around the center point (for example, referring to Figures 27 to 29 neighbor_search_range), search range type information indicating the type of neighbor point search range for each LOD (for example, reference Figures 27 to 29 The neighbor_search_range_type) and search type information indicating the neighbor point search method (for example, reference Figures 27 to 29 described neighbor_search_type).

[0412] The type information according to an embodiment may indicate at least one of a first type or a second type, the first type being used to select a point having a Morton code closest to a Morton code of a point to be subjected to attribute information encoding as a center point, and the second type being used to select a point having a Morton code equal to or greater than a Morton code of a point to be subjected to attribute information encoding as a center point. Figure 22 to Figure 29 The center point and neighbor point search range described for searching neighbor points are the same, so a detailed description thereof will be avoided here.

[0413] The search type information according to the embodiment may indicate at least one of an all-point search method, a method of searching points selected from a sampling range as neighbor points, a method of preliminarily searching for points in a bounding box in each area obtained by dividing a neighbor point search range, a method of performing a preliminary search based on the division of the neighbor point search range based on an octree and the center position value of each octree node, or a method of performing a preliminary search based on the calculation of the distance between points selected from the sampling range. Figure 27 to Figure 29 The neighbor_search_type described above is the same, so a detailed description thereof will be avoided here.

[0414] Fig.34 is an exemplary flow chart of a point cloud data processing method according to an embodiment.

[0415] Fig.34 Flowchart 3400 shows a method for processing point cloud data by a device (e.g., referring to Figure 1 , 13 , 14, 16, 27 and 28 described point cloud data receiving device or point cloud data decoder). The point cloud data processing device according to the embodiment can perform the same as the reference Figures 1 to 33 The decoding operations described are the same or similar operations.

[0416] The point cloud data processing apparatus according to the embodiment receives a bit stream including point cloud data (3410). The geometric information is information indicating the position of a point of the point cloud data. The attribute information according to the embodiment is information indicating the attribute of a point of the point cloud data. Fig.26 The structures described are the same, so their detailed description will be omitted.

[0417] The point cloud data processing apparatus according to the embodiment decodes the point cloud data ( 3420 ).

[0418] The point cloud data processing device according to the embodiment can decode the geometric information and decode the attribute information. Figures 1 to 32 The geometric information decoding operation described in the above description is the same or similar to the operation described in the above description. Figures 1 to 32 The point cloud data processing device according to the embodiment can generate at least one LOD by segmenting the points, and generate a neighbor point set belonging to each LOD point. Since the neighbor point set generation scheme or method according to the embodiment is the same as that of the reference Figures 22 to 25 The described neighbor point set generation schemes or methods are the same or similar, so their detailed description will be omitted.

[0419] Since the structure of the bitstream according to the embodiment is similar to the reference Fig.26 The structure described is the same as that described above, so its detailed description will be omitted. The bitstream according to the embodiment may include neighbor point set generation information (for example, reference Figures 27 to 29 The neighbor point set generation information according to the implementation scheme may be sent to the receiving device via APS, TPS, attribute header, etc., as shown in FIG. Figures 27 to 32 described.

[0420] According to an embodiment, the neighbor point set generation information may include type information (for example, reference Figures 27 to 29 The neighbor_search_origin_selection_type described above), search range information indicating a neighbor point search range for searching for neighboring points located around the center point (for example, referring to Figures 27 to 29 neighbor_search_range), search range type information indicating the type of neighbor point search range for each LOD (for example, reference Figures 27 to 29 neighbor_search_range_type) and search type information indicating the type of neighbor point search method (for example, reference Figures 27 to 29 neighbor_search_type described above).

[0421] The type information according to an embodiment may indicate at least one of a first type or a second type, the first type being used to select a point having a Morton code closest to a Morton code of a point to be subjected to attribute information encoding as a center point, and the second type being used to select a point having a Morton code equal to or greater than a Morton code of a point to be subjected to attribute information encoding as a center point. Figure 22 to Figure 29 The center point and neighbor point search range described for searching neighbor points are the same, so a detailed description thereof will be avoided here.

[0422] The search type information according to the embodiment may indicate at least one of an all-point search method, a method of searching points selected from a sampling range as neighbor points, a method of preliminarily searching for points in a bounding box in each area obtained by dividing a neighbor point search range, a method of performing a preliminary search based on the division of the neighbor point search range based on an octree and the center position value of each octree node, or a method of performing a preliminary search based on the calculation of the distance between points selected from the sampling range. Figure 27 to Figure 29 The neighbor_search_type described above is the same as that described above, so a detailed description thereof will be avoided in this article.

[0423] According to reference Figures 1 to 34 The components of the point cloud data processing device of the described embodiments may be implemented as hardware, software, firmware, or a combination thereof including one or more processors coupled to a memory. The components of the device according to the embodiments may be implemented as a single chip, such as a single hardware circuit. Alternatively, the components of the point cloud data processing device according to the embodiments may be implemented as separate chips. In addition, at least one component of the point cloud data processing device according to the embodiments may include one or more processors capable of executing one or more programs, wherein the one or more programs may include executing or being configured to execute reference Figures 1 to 34 Instructions describing one or more operations / methods of a point cloud data processing apparatus.

[0424] Although the drawings are described separately for simplicity, new embodiments can be designed by merging the embodiments shown in the various figures. Designing a recording medium that can be read by a computer and records a program for executing the above-mentioned embodiments according to the needs of those skilled in the art also falls within the scope of the attached claims and their equivalents. The apparatus and method according to the embodiment may not be limited to the configuration and method of the above-mentioned embodiment. Various modifications can be made to the embodiment by selectively combining all or some of the embodiments. Although preferred embodiments are described with reference to the drawings, it will be understood by those skilled in the art that various modifications and changes can be made to the embodiment without departing from the spirit or scope of the present disclosure described in the attached claims. These modifications should not be understood separately from the technical ideas or viewpoints of the embodiments.

[0425] The description of the method and the apparatus may be applied to complement each other. For example, the point cloud data transmission method according to the embodiment may be performed by the point cloud data sending apparatus according to the embodiment or a component included in the point cloud data sending apparatus. In addition, the point cloud data receiving method according to the embodiment may be performed by the point cloud data receiving apparatus according to the embodiment or a component included in the point cloud data receiving apparatus.

[0426] Various elements of the device of the embodiment can be implemented by hardware, software, firmware or a combination thereof. Various elements in the embodiment can be implemented by a single chip (e.g., a single hardware circuit). According to the embodiment, the components according to the embodiment can be implemented as separate chips respectively. According to the embodiment, at least one or more components of the device according to the embodiment may include one or more processors capable of executing one or more programs. One or more programs may execute any one or more operations / methods according to the embodiment or include instructions for executing them. The executable instructions for executing the method / operation of the device according to the embodiment may be stored in a non-transitory CRM or other computer program product configured to be executed by one or more processors, or may be stored in a temporary CRM or other computer program product configured to be executed by one or more processors. In addition, the memory according to the embodiment can be used as not only covering volatile memory (e.g., RAM), but also covering the concepts of non-volatile memory, flash memory and PROM. In addition, it can also be implemented in the form of a carrier (e.g., transmission via the Internet). In addition, the processor-readable recording medium can be distributed to a computer system connected via a network so that the processor-readable code can be stored and executed in a distributed manner.

[0427] In the present specification, the terms " / " and "," should be interpreted as indicating "and / or". For example, the expression "A / B" may mean "A and / or B". In addition, "A, B" may mean "A and / or B". In addition, "A / B / C" may mean "at least one of A, B and / or C". In addition, "A / B / C" may mean "at least one of A, B and / or C". In addition, in the present specification, the term "or" should be interpreted as indicating "and / or". For example, the expression "A or B" may mean 1) only A, 2) only B, or 3) both A and B. In other words, the term "or" used in this document should be interpreted as indicating "in addition or alternatively".

[0428] Terms such as first and second can be used to describe various elements of the embodiment. However, various components according to the embodiment should not be limited by the above terms. These terms are only used to distinguish one element from another element. For example, a first user input signal can be referred to as a second user input signal. Similarly, a second user input signal can be referred to as a first user input signal. The use of these terms should not be interpreted without departing from the scope of various embodiments. Both the first user input signal and the second user input signal are user input signals, but unless the context clearly stipulates otherwise, they do not mean the same user input signal.

[0429] The terms used to describe the embodiments are used to describe specific embodiments and are not intended to limit the embodiments. As used in the description of the embodiments and the claims, unless the context clearly stipulates otherwise, the singular form includes plural referents. The expression "and / or" is used to include all possible combinations of terms. Terms such as "including" or "having" are intended to indicate the presence of graphics, quantities, steps, elements and / or components, and should be understood as not excluding the possibility of additional presence of graphics, quantities, steps, elements and / or components. As used herein, conditional expressions such as "if" and "when" are not limited to optional situations, and are intended to be interpreted as performing related operations when specific conditions are met, or interpreting related definitions according to specific conditions.

[0430] Public Mode

[0431] As described above, the relevant contents are described in the best mode for carrying out the embodiment.

[0432] Industrial Applicability

[0433] It will be apparent to those skilled in the art that various changes or modifications may be made to the embodiments within the scope of the embodiments. Therefore, the embodiments are intended to cover modifications and variations of the present disclosure as long as they fall within the scope of the appended claims and their equivalents.

Claims

1. A method for processing point cloud data, the method comprising: Encoding point cloud data including geometric information and attribute information, wherein the geometric information represents positions of points of the point cloud data, and the attribute information represents attributes of the points of the point cloud data; as well as Send a bitstream containing the encoded point cloud data, Wherein, encoding the point cloud data includes encoding the geometric information and the attribute information. Wherein encoding the attribute information comprises generating at least one LOD (level of detail) based on a Morton order, wherein a point in the at least one LOD is selected based on the Morton order, The bitstream includes attribute parameter set information, the attribute parameter set information includes search range information, and the search range information indicates a neighbor point search range for searching neighbor points located around the center of the point. Therein, the geometric information is encoded based on a tree having levels of points comprising the geometric information.

2. The method according to claim 1, wherein: Encoding the attribute information includes: Generate a set of neighboring points belonging to each LOD point.

3. The method according to claim 2, wherein: The bit stream includes neighbor point set generation information, The neighbor point set generation information includes: Type information indicating the type of centroid point selection method used to select a centroid point to generate the neighbor point set, search range information indicating the neighbor point search range used to search for neighbor points located around the centroid point, search type information indicating the type of the neighbor point search range for each LOD, and search type information indicating the type of the neighbor point search method.

4. The method according to claim 3, wherein: The type information represents at least one of a first type and a second type, the first type is used to select a point having a Morton code closest to the Morton code of the point to be encoded with attribute information as the centroid point, and the second type is used to select a point having a Morton code greater than or equal to the Morton code of the point to be encoded with attribute information as the centroid point.

5. The method according to claim 3, wherein: The search type information includes at least one of an all-point search method, a method of searching points selected from a sampling range as neighbor points, a method of preliminarily searching for points in a bounding box in each area obtained by dividing a neighbor point search range, a method of performing a preliminary search based on an octree-based division of a neighbor point search range and a center position value of each octree node, or a method of performing a preliminary search based on calculation of distances between points selected from a sampling range.

6. A method for processing point cloud data, the method comprising: Receiving a bitstream including point cloud data, wherein the point cloud data includes geometric information and attribute information, wherein the geometric information represents positions of points of the point cloud data, and the attribute information represents attributes of the points of the point cloud data; as well as decoding the geometric information; as well as decoding the attribute information, wherein decoding the attribute information comprises generating at least one LOD (level of detail) based on a Morton order, wherein a point in the at least one LOD is selected based on the Morton order, The bitstream includes attribute parameter set information, the attribute parameter set information includes search range information, and the search range information indicates a neighbor point search range for searching neighbor points located around the center of the point. Therein, the geometric information is decoded based on a tree having a level of points including the geometric information.

7. The method according to claim 6, wherein: Decoding the attribute information includes: Generate a set of neighboring points belonging to each LOD point.

8. The method according to claim 7, wherein: The bit stream includes neighbor point set generation information, The neighbor point set generation information includes: Type information indicating the type of centroid point selection method used to select a centroid point to generate the neighbor point set, search range information indicating the neighbor point search range used to search for neighbor points located around the centroid point, search type information indicating the type of the neighbor point search range for each LOD, and search type information indicating the type of the neighbor point search method.

9. The method according to claim 8, wherein: The type information represents at least one of a first type and a second type, the first type is used to select a point having a Morton code closest to the Morton code of the point to be encoded with attribute information as the centroid point, and the second type is used to select a point having a Morton code greater than or equal to the Morton code of the point to be encoded with attribute information as the centroid point.

10. The method according to claim 8, wherein: The search type information includes at least one of an all-point search method, a method of searching points selected from a sampling range as neighbor points, a method of preliminarily searching for points in a bounding box in each area obtained by dividing a neighbor point search range, a method of performing a preliminary search based on an octree-based division of a neighbor point search range and a center position value of each octree node, or a method of performing a preliminary search based on calculation of distances between points selected from a sampling range.

11. A device for processing point cloud data, the device comprising: a receiver configured to receive a bit stream including point cloud data, wherein the point cloud data includes geometric information and attribute information, wherein the geometric information represents positions of points of the point cloud data, and the attribute information represents attributes of points of the point cloud data; as well as A decoder, the decoder being configured to: decoding the geometric information; as well as decoding the attribute information, wherein decoding the attribute information comprises generating at least one LOD (level of detail) based on a Morton order, wherein a point in the at least one LOD is selected based on the Morton order, The bitstream includes attribute parameter set information, the attribute parameter set information includes search range information, and the search range information indicates a neighbor point search range for searching neighbor points located around the center of the point. Therein, the geometric information is decoded based on a tree having a level of points including the geometric information.

12. The device according to claim 11, wherein The second decoder is configured to generate at least one LOD (Level of Detail) by segmenting the point and to generate a set of neighbor points belonging to each LOD.

13. The device according to claim 12, wherein: The bit stream includes neighbor point set generation information, The neighbor point set generation information includes: Type information indicating the type of centroid point selection method used to select a centroid point to generate the neighbor point set, search range information indicating the neighbor point search range used to search for neighbor points located around the centroid point, search type information indicating the type of the neighbor point search range for each LOD, and search type information indicating the type of the neighbor point search method.

14. A device for processing point cloud data, the device comprising: an encoder configured to encode the point cloud data including geometric information and attribute information, wherein the geometric information represents positions of points of the point cloud data, and the attribute information represents attributes of the points of the point cloud data; as well as a transmitter configured to transmit a bit stream comprising encoded point cloud data, wherein the encoder is configured to encode the geometric information and the attribute information, Wherein encoding the attribute information comprises generating at least one LOD (level of detail) based on a Morton order, wherein a point in the at least one LOD is selected based on the Morton order, The bitstream includes attribute parameter set information, the attribute parameter set information includes search range information, and the search range information indicates a neighbor point search range for searching neighbor points located around the center of the point. Therein, the geometric information is encoded based on a tree having levels of points comprising the geometric information.

15. The device according to claim 14, wherein: The second encoder is configured to generate at least one LOD (Level of Detail) by segmenting the points and to generate a set of neighbor points belonging to each LOD.

16. The apparatus according to claim 15, wherein the bit stream includes neighbor point set generation information. The neighbor point set generation information includes: Type information indicating the type of centroid point selection method used to select a centroid point to generate the neighbor point set, search range information indicating the neighbor point search range used to search for neighbor points located around the centroid point, search type information indicating the type of the neighbor point search range for each LOD, and search type information indicating the type of the neighbor point search method.

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