WO2022124648A1 - Dispositif d'émission de données de nuage de points, procédé d'émission de données de nuage de points, dispositif de réception de données de nuage de points et procédé de réception de données de nuage de points - Google Patents

Dispositif d'émission de données de nuage de points, procédé d'émission de données de nuage de points, dispositif de réception de données de nuage de points et procédé de réception de données de nuage de points Download PDF

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WO2022124648A1
WO2022124648A1 PCT/KR2021/017419 KR2021017419W WO2022124648A1 WO 2022124648 A1 WO2022124648 A1 WO 2022124648A1 KR 2021017419 W KR2021017419 W KR 2021017419W WO 2022124648 A1 WO2022124648 A1 WO 2022124648A1
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point cloud
cloud data
time index
index
information
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PCT/KR2021/017419
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English (en)
Korean (ko)
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이수연
심동규
최한솔
오세진
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엘지전자 주식회사
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Publication of WO2022124648A1 publication Critical patent/WO2022124648A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T9/00—Image coding
    • G06T9/40—Tree coding, e.g. quadtree, octree
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/102—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or selection affected or controlled by the adaptive coding
    • H04N19/119—Adaptive subdivision aspects, e.g. subdivision of a picture into rectangular or non-rectangular coding blocks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/134—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or criterion affecting or controlling the adaptive coding
    • H04N19/136—Incoming video signal characteristics or properties
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/169—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding
    • H04N19/184—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being bits, e.g. of the compressed video stream
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/50—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding
    • H04N19/597—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding specially adapted for multi-view video sequence encoding
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/90—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using coding techniques not provided for in groups H04N19/10-H04N19/85, e.g. fractals
    • H04N19/96—Tree coding, e.g. quad-tree coding

Definitions

  • Embodiments relate to a method and apparatus for processing point cloud content.
  • the point cloud content is content expressed as a point cloud, which is a set of points (points) belonging to a coordinate system representing a three-dimensional space.
  • Point cloud content can express three-dimensional media, and provides various services such as VR (Virtual Reality), AR (Augmented Reality), MR (Mixed Reality), and autonomous driving service. used to provide However, tens of thousands to hundreds of thousands of point data are needed to express point cloud content. Therefore, a method for efficiently processing a large amount of point data is required.
  • Embodiments provide an apparatus and method for efficiently processing point cloud data.
  • Embodiments provide a point cloud data processing method and apparatus for solving latency and encoding/decoding complexity.
  • a method for transmitting point cloud data includes encoding point cloud data; and transmitting a bitstream including the point cloud data; may include.
  • An apparatus for receiving point cloud data according to embodiments includes: a receiver configured to receive a bitstream including point cloud data; and a decoder for decoding the point cloud data; may include.
  • the apparatus and method according to the embodiments may process point cloud data with high efficiency.
  • the apparatus and method according to the embodiments may provide a high quality point cloud service.
  • the apparatus and method according to the embodiments may provide point cloud content for providing universal services such as a VR service and an autonomous driving service.
  • FIG. 1 shows an example of a point cloud content providing system according to embodiments.
  • FIG. 2 is a block diagram illustrating an operation of providing point cloud content according to embodiments.
  • FIG 3 shows an example of a point cloud video capture process according to embodiments.
  • FIG. 4 shows an example of a point cloud encoder according to embodiments.
  • FIG. 5 illustrates an example of a voxel according to embodiments.
  • FIG. 6 shows an example of an octree and an occupancy code according to embodiments.
  • FIG. 7 shows an example of a neighbor node pattern according to embodiments.
  • FIG. 10 shows an example of a point cloud decoder according to embodiments.
  • FIG. 11 shows an example of a point cloud decoder according to embodiments.
  • FIG. 13 is an example of a receiving apparatus according to embodiments.
  • FIG. 14 illustrates an example of a structure capable of interworking with a method/device for transmitting and receiving point cloud data according to embodiments.
  • 15 shows an apparatus for transmitting point cloud data according to embodiments.
  • 16 shows an apparatus for receiving point cloud data according to embodiments.
  • 17 shows a time information encoding unit according to embodiments.
  • FIG. 18 shows a configuration of time information of point cloud data according to embodiments.
  • FIG. 19 illustrates an operation of a time information-geometric information mapping unit according to embodiments.
  • 20 and 21 show a method of expressing reference time information higher order information according to embodiments.
  • FIG. 22 shows a coordinate system of a spherical coordinate system for point geometry information according to embodiments.
  • FIG. 23 shows an example of a sub-node and a time information sharing point group according to embodiments.
  • 25 shows a representative time index difference expression method according to embodiments.
  • 26 shows a time information decoding unit according to embodiments.
  • 29 shows decoding of representative time information according to embodiments.
  • 31 shows a bitstream including point cloud data according to embodiments.
  • 33 shows a set of time information parameters according to embodiments.
  • 35 shows a temporal data unit according to embodiments.
  • FIG 36 shows the structure of an apparatus for transmitting point cloud data according to embodiments.
  • FIG. 37 shows an apparatus for receiving point cloud data according to embodiments.
  • 38 shows a method for transmitting point cloud data according to embodiments.
  • 39 shows a method of receiving point cloud data according to embodiments.
  • FIG. 1 shows an example of a point cloud content providing system according to embodiments.
  • the point cloud content providing system shown in FIG. 1 may include a transmission device 10000 and a reception device 10004 .
  • the transmitting device 10000 and the receiving device 10004 are capable of wired/wireless communication in order to transmit/receive point cloud data.
  • the transmission device 10000 may secure, process, and transmit a point cloud video (or point cloud content).
  • the transmitting device 10000 may be a fixed station, a base transceiver system (BTS), a network, an Ariticial Intelligence (AI) device and/or system, a robot, an AR/VR/XR device and/or a server and the like.
  • BTS base transceiver system
  • AI Ariticial Intelligence
  • the transmission device 10000 uses a radio access technology (eg, 5G NR (New RAT), LTE (Long Term Evolution)) to communicate with a base station and/or other wireless devices; It may include robots, vehicles, AR/VR/XR devices, mobile devices, home appliances, Internet of Things (IoT) devices, AI devices/servers, and the like.
  • a radio access technology eg, 5G NR (New RAT), LTE (Long Term Evolution)
  • 5G NR New RAT
  • LTE Long Term Evolution
  • IoT Internet of Things
  • Transmission device 10000 is a point cloud video acquisition unit (Point Cloud Video Acquisition, 10001), a point cloud video encoder (Point Cloud Video Encoder, 10002) and / or a transmitter (Transmitter (or Communication module), 10003 ) contains
  • the point cloud video acquisition unit 10001 acquires the point cloud video through processing such as capturing, synthesizing, or generating.
  • the point cloud video is point cloud content expressed as a point cloud that is a set of points located in a three-dimensional space, and may be referred to as point cloud video data or the like.
  • a point cloud video according to embodiments may include one or more frames. One frame represents a still image/picture. Accordingly, the point cloud video may include a point cloud image/frame/picture, and may be referred to as any one of a point cloud image, a frame, and a picture.
  • the point cloud video encoder 10002 encodes the obtained point cloud video data.
  • the point cloud video encoder 10002 may encode point cloud video data based on point cloud compression coding.
  • Point cloud compression coding may include Geometry-based Point Cloud Compression (G-PCC) coding and/or Video based Point Cloud Compression (V-PCC) coding or next-generation coding.
  • G-PCC Geometry-based Point Cloud Compression
  • V-PCC Video based Point Cloud Compression
  • the point cloud video encoder 10002 may output a bitstream including encoded point cloud video data.
  • the bitstream may include not only the encoded point cloud video data, but also signaling information related to encoding of the point cloud video data.
  • the transmitter 10003 transmits a bitstream including encoded point cloud video data.
  • the bitstream according to the embodiments is encapsulated into a file or segment (eg, a streaming segment) and transmitted through various networks such as a broadcasting network and/or a broadband network.
  • the transmission device 10000 may include an encapsulation unit (or an encapsulation module) that performs an encapsulation operation.
  • the encapsulation unit may be included in the transmitter 10003 .
  • the file or segment may be transmitted to the receiving device 10004 through a network or stored in a digital storage medium (eg, USB, SD, CD, DVD, Blu-ray, HDD, SSD, etc.).
  • the transmitter 10003 may communicate with the receiving device 10004 (or a receiver 10005) through wired/wireless communication through networks such as 4G, 5G, and 6G. Also, the transmitter 10003 may perform a necessary data processing operation according to a network system (eg, a communication network system such as 4G, 5G, 6G, etc.). Also, the transmission device 10000 may transmit encapsulated data according to an on demand method.
  • a network system eg, a communication network system such as 4G, 5G, 6G, etc.
  • the transmission device 10000 may transmit encapsulated data according to an on demand method.
  • the receiving device 10004 includes a receiver (Receiver, 10005), a point cloud video decoder (Point Cloud Decoder, 10006), and/or a renderer (Renderer, 10007).
  • the receiving device 10004 uses a radio access technology (eg, 5G NR (New RAT), LTE (Long Term Evolution)) to communicate with a base station and/or other wireless devices, a device or a robot , vehicles, AR/VR/XR devices, portable devices, home appliances, Internet of Things (IoT) devices, AI devices/servers, and the like.
  • 5G NR New RAT
  • LTE Long Term Evolution
  • the receiver 10005 receives a bitstream including point cloud video data or a file/segment in which the bitstream is encapsulated from a network or a storage medium.
  • the receiver 10005 may perform a necessary data processing operation according to a network system (eg, a communication network system such as 4G, 5G, or 6G).
  • the receiver 10005 may output a bitstream by decapsulating the received file/segment.
  • the receiver 10005 may include a decapsulation unit (or a decapsulation module) for performing a decapsulation operation.
  • the decapsulation unit may be implemented as an element (or component) separate from the receiver 10005 .
  • the point cloud video decoder 10006 decodes a bitstream including point cloud video data.
  • the point cloud video decoder 10006 may decode the point cloud video data according to an encoded manner (eg, a reverse process of the operation of the point cloud video encoder 10002 ). Accordingly, the point cloud video decoder 10006 may decode the point cloud video data by performing point cloud decompression coding, which is a reverse process of the point cloud compression.
  • Point cloud decompression coding includes G-PCC coding.
  • the renderer 10007 renders the decoded point cloud video data.
  • the renderer 10007 may output point cloud content by rendering audio data as well as point cloud video data.
  • the renderer 10007 may include a display for displaying the point cloud content.
  • the display may not be included in the renderer 10007 and may be implemented as a separate device or component.
  • the feedback information is information for reflecting the interactivity with the user who consumes the point cloud content, and includes user information (eg, head orientation information, viewport information, etc.).
  • user information eg, head orientation information, viewport information, etc.
  • the feedback information is provided by the content transmitting side (eg, the transmission device 10000) and/or the service provider can be passed on to According to embodiments, the feedback information may be used by the receiving device 10004 as well as the transmitting device 10000 or may not be provided.
  • the head orientation information is information about the user's head position, direction, angle, movement, and the like.
  • the reception apparatus 10004 may calculate viewport information based on head orientation information.
  • the viewport information is information about the area of the point cloud video that the user is looking at.
  • a viewpoint is a point at which a user is watching a point cloud video, and may mean a central point of the viewport area. That is, the viewport is an area centered on a viewpoint, and the size and shape of the area may be determined by a Field Of View (FOV).
  • FOV Field Of View
  • the reception device 10004 may extract viewport information based on a vertical or horizontal FOV supported by the device in addition to the head orientation information.
  • the receiving device 10004 checks the user's point cloud consumption method, the point cloud video area the user gazes on, the gaze time, and the like by performing a gaze analysis or the like.
  • the receiving device 10004 may transmit feedback information including the result of the gaze analysis to the transmitting device 10000 .
  • Feedback information may be obtained during rendering and/or display.
  • Feedback information may be secured by one or more sensors included in the receiving device 10004 .
  • the feedback information may be secured by the renderer 10007 or a separate external element (or device, component, etc.).
  • a dotted line in FIG. 1 shows a process of transmitting the feedback information secured by the renderer 10007 .
  • the point cloud content providing system may process (encode/decode) the point cloud data based on the feedback information. Accordingly, the point cloud video data decoder 10006 may perform a decoding operation based on the feedback information. Also, the receiving device 10004 may transmit feedback information to the transmitting device 10000 . The transmission device 10000 (or the point cloud video data encoder 10002 ) may perform an encoding operation based on the feedback information. Therefore, the point cloud content providing system does not process (encode / decode) all point cloud data, but efficiently processes necessary data (for example, point cloud data corresponding to the user's head position) based on the feedback information, and the user can provide point cloud content to
  • the transmitting apparatus 10000 may be referred to as an encoder, a transmitting device, a transmitter, etc.
  • the receiving apparatus 10004 may be referred to as a decoder, a receiving device, a receiver, or the like.
  • Point cloud data (processed in a series of acquisition/encoding/transmission/decoding/rendering) processed in the point cloud content providing system of FIG. 1 according to embodiments may be referred to as point cloud content data or point cloud video data.
  • the point cloud content data may be used as a concept including metadata or signaling information related to the point cloud data.
  • the elements of the point cloud content providing system shown in FIG. 1 may be implemented by hardware, software, a processor and/or a combination thereof.
  • FIG. 2 is a block diagram illustrating an operation of providing point cloud content according to embodiments.
  • the block diagram of FIG. 2 shows the operation of the point cloud content providing system described in FIG. 1 .
  • the point cloud content providing system may process point cloud data based on point cloud compression coding (eg, G-PCC).
  • point cloud compression coding eg, G-PCC
  • the point cloud content providing system may acquire a point cloud video (20000).
  • a point cloud video is expressed as a point cloud belonging to a coordinate system representing a three-dimensional space.
  • the point cloud video according to embodiments may include a Ply (Polygon File format or the Stanford Triangle format) file.
  • the acquired point cloud video may include one or more Ply files.
  • the Ply file contains point cloud data such as the point's geometry and/or attributes. Geometry includes positions of points.
  • the position of each point may be expressed by parameters (eg, values of each of the X-axis, Y-axis, and Z-axis) representing a three-dimensional coordinate system (eg, a coordinate system including XYZ axes).
  • the attribute includes attributes of points (eg, texture information of each point, color (YCbCr or RGB), reflectance (r), transparency, etc.).
  • a point has one or more attributes (or properties).
  • one point may have one attribute of color, or two attributes of color and reflectance.
  • the geometry may be referred to as positions, geometry information, geometry data, and the like, and the attribute may be referred to as attributes, attribute information, attribute data, and the like.
  • the point cloud content providing system receives points from information (eg, depth information, color information, etc.) related to the point cloud video acquisition process. Cloud data can be obtained.
  • the point cloud content providing system may encode the point cloud data (20001).
  • the point cloud content providing system may encode point cloud data based on point cloud compression coding.
  • the point cloud data may include the geometry and attributes of the point.
  • the point cloud content providing system may output a geometry bitstream by performing geometry encoding for encoding the geometry.
  • the point cloud content providing system may output an attribute bitstream by performing attribute encoding for encoding an attribute.
  • the point cloud content providing system may perform attribute encoding based on geometry encoding.
  • the geometry bitstream and the attribute bitstream according to the embodiments may be multiplexed and output as one bitstream.
  • the bitstream according to embodiments may further include signaling information related to geometry encoding and attribute encoding.
  • the point cloud content providing system may transmit the encoded point cloud data (20002).
  • the encoded point cloud data may be expressed as a geometry bitstream and an attribute bitstream.
  • the encoded point cloud data may be transmitted in the form of a bitstream together with signaling information related to encoding of the point cloud data (eg, signaling information related to geometry encoding and attribute encoding).
  • the point cloud content providing system may encapsulate the bitstream for transmitting the encoded point cloud data and transmit it in the form of a file or segment.
  • the point cloud content providing system (eg, the receiving device 10004 or the receiver 10005) according to the embodiments may receive a bitstream including the encoded point cloud data. Also, the point cloud content providing system (eg, the receiving device 10004 or the receiver 10005) may demultiplex the bitstream.
  • the point cloud content providing system may decode the encoded point cloud data (for example, a geometry bitstream, an attribute bitstream) transmitted as a bitstream. have.
  • the point cloud content providing system (for example, the receiving device 10004 or the point cloud video decoder 10005) may decode the point cloud video data based on signaling information related to encoding of the point cloud video data included in the bitstream. have.
  • the point cloud content providing system (eg, the receiving device 10004 or the point cloud video decoder 10005) may decode the geometry bitstream to restore positions (geometry) of the points.
  • the point cloud content providing system may restore attributes of points by decoding an attribute bitstream based on the restored geometry.
  • the point cloud content providing system (eg, the receiving device 10004 or the point cloud video decoder 10005) may reconstruct the point cloud video based on positions and decoded attributes according to the reconstructed geometry.
  • the point cloud content providing system may render the decoded point cloud data (20004).
  • the point cloud content providing system may render the geometry and attributes decoded through the decoding process according to various rendering methods according to the rendering method.
  • the points of the point cloud content may be rendered as a vertex having a certain thickness, a cube having a specific minimum size centered at the vertex position, or a circle centered at the vertex position. All or part of the rendered point cloud content is provided to the user through a display (eg, VR/AR display, general display, etc.).
  • the point cloud content providing system (eg, the reception device 10004) according to the embodiments may secure the feedback information (20005).
  • the point cloud content providing system may encode and/or decode the point cloud data based on the feedback information. Since the operation of the feedback information and point cloud content providing system according to the embodiments is the same as the feedback information and operation described with reference to FIG. 1 , a detailed description thereof will be omitted.
  • FIG 3 shows an example of a point cloud video capture process according to embodiments.
  • FIG. 3 shows an example of a point cloud video capture process of the point cloud content providing system described with reference to FIGS. 1 and 2 .
  • the point cloud content is an object located in various three-dimensional spaces (eg, a three-dimensional space representing a real environment, a three-dimensional space representing a virtual environment, etc.) and/or a point cloud video representing the environment (images and/or videos) are included.
  • one or more cameras eg, an infrared camera capable of securing depth information, color information corresponding to depth information
  • the point cloud content providing system according to the embodiments may extract a shape of a geometry composed of points in a three-dimensional space from depth information, and extract an attribute of each point from color information to secure point cloud data.
  • An image and/or an image according to embodiments may be captured based on at least one of an inward-facing method and an outward-facing method.
  • the left side of FIG. 3 shows an inward-pacing scheme.
  • the inward-pacing method refers to a method in which one or more cameras (or camera sensors) located surrounding the central object capture the central object.
  • the inward-facing method provides a 360-degree image of a point cloud content that provides a user with a 360-degree image of a core object (for example, a 360-degree image of an object (e.g., a core object such as a character, player, object, actor, etc.) to the user. It can be used to create VR/AR content).
  • the right side of FIG. 3 shows an outward-pacing scheme.
  • the outward-pacing method refers to a method in which one or more cameras (or camera sensors) positioned surrounding the central object capture the environment of the central object rather than the central object.
  • the outward-pacing method may be used to generate point cloud content (eg, content representing an external environment that may be provided to a user of an autonomous vehicle) for providing a surrounding environment that appears from the user's point of view.
  • point cloud content eg, content representing an external environment that may be provided to a user of an autonomous vehicle
  • the point cloud content may be generated based on a capture operation of one or more cameras.
  • the point cloud content providing system may perform calibration of one or more cameras in order to set a global coordinate system before the capture operation.
  • the point cloud content providing system may generate the point cloud content by synthesizing the image and/or image captured by the above-described capture method and an arbitrary image and/or image.
  • the point cloud content providing system may not perform the capture operation described with reference to FIG. 3 when generating point cloud content representing a virtual space.
  • the point cloud content providing system according to the embodiments may perform post-processing on the captured image and/or the image. That is, the point cloud content providing system removes an unwanted area (eg, a background), recognizes a space where captured images and/or images are connected, and fills in a spatial hole if there is one. can
  • the point cloud content providing system may generate one point cloud content by performing coordinate system transformation on points of the point cloud video secured from each camera.
  • the point cloud content providing system may perform coordinate system transformation of points based on the position coordinates of each camera. Accordingly, the point cloud content providing system may generate content representing one wide range and may generate point cloud content having a high density of points.
  • FIG. 4 shows an example of a point cloud encoder according to embodiments.
  • the point cloud encoder controls point cloud data (eg, positions of points and/or attributes) and perform an encoding operation.
  • point cloud data e.g, positions of points and/or attributes
  • the point cloud content providing system may not be able to stream the corresponding content in real time. Accordingly, the point cloud content providing system may reconfigure the point cloud content based on a maximum target bitrate in order to provide it according to a network environment.
  • the point cloud encoder may perform geometry encoding and attribute encoding. Geometry encoding is performed before attribute encoding.
  • the point cloud encoder may include a coordinate system transformation unit (Transformation Coordinates, 40000), a quantization unit (Quantize and Remove Points (Voxelize), 40001), an octree analysis unit (Analyze Octree, 40002), and a surface appropriation analysis unit ( Analyze Surface Approximation (40003), Arithmetic Encode (40004), Reconstruct Geometry (40005), Color Transformer (Transform Colors, 40006), Attribute Transformer (Transfer Attributes, 40007), RAHT Transform It includes a unit 40008, an LOD generator (Generated LOD, 40009), a lifting transform unit (Lifting) 40010, a coefficient quantization unit (Quantize Coefficients, 40011) and/or an arithmetic encoder (Arithmetic Encode, 40012).
  • a coordinate system transformation unit Transformation Coordinates, 40000
  • a quantization unit Quantization and Remove Points (Voxelize)
  • the coordinate system transformation unit 40000, the quantization unit 40001, the octree analysis unit 40002, the surface approxy analysis unit 40003, the arithmetic encoder 40004, and the geometry reconstruction unit 40005 perform geometry encoding. can do.
  • Geometry encoding according to embodiments may include octree geometry coding, direct coding, trisoup geometry encoding, and entropy encoding. Direct coding and trisup geometry encoding are applied selectively or in combination. Also, geometry encoding is not limited to the above example.
  • the coordinate system conversion unit 40000 receives the positions and converts them into a coordinate system.
  • the positions may be converted into position information in a three-dimensional space (eg, a three-dimensional space expressed in an XYZ coordinate system, etc.).
  • Location information in 3D space may be referred to as geometry information.
  • the quantizer 40001 quantizes the geometry.
  • the quantization unit 40001 may quantize the points based on the minimum position values of all points (eg, the minimum values on each axis with respect to the X-axis, Y-axis, and Z-axis).
  • the quantization unit 40001 performs a quantization operation to find the nearest integer value by multiplying the difference between the minimum position value and the position value of each point by a preset quatization scale value, and then rounding down or rounding it up. Accordingly, one or more points may have the same quantized position (or position value).
  • the quantizer 40001 according to embodiments performs voxelization based on quantized positions to reconstruct quantized points.
  • a minimum unit including 2D image/video information is a pixel, and points of point cloud content (or 3D point cloud video) according to embodiments may be included in one or more voxels.
  • the quantizer 40001 may match groups of points in a 3D space to voxels.
  • one voxel may include only one point.
  • one voxel may include one or more points.
  • a position of a center point of the voxel may be set based on positions of one or more points included in one voxel.
  • attributes of all positions included in one voxel may be combined and assigned to a corresponding voxel.
  • the octree analyzer 40002 performs octree geometry coding (or octree coding) to represent voxels in an octree structure.
  • the octree structure represents points matched to voxels based on the octal tree structure.
  • the surface appropriation analyzer 40003 may analyze and approximate the octree.
  • Octree analysis and approximation is a process of analyzing to voxelize a region including a plurality of points in order to efficiently provide octree and voxelization.
  • the arithmetic encoder 40004 entropy encodes the octree and/or the approximated octree.
  • the encoding method includes an arithmetic encoding method.
  • the encoding results in a geometry bitstream.
  • Color transform unit 40006 performs attribute encoding.
  • one point may have one or more attributes. Attribute encoding according to embodiments is equally applied to attributes of one point. However, when one attribute (eg, color) includes one or more elements, independent attribute encoding is applied to each element.
  • Attribute encoding may include color transform coding, attribute transform coding, region adaptive hierarchical transform (RAHT) coding, interpolaration-based hierarchical nearest-neighbor prediction-Prediction Transform coding, and interpolation-based hierarchical nearest -neighbor prediction with an update/lifting step (Lifting Transform)) may include coding.
  • RAHT region adaptive hierarchical transform
  • coding interpolaration-based hierarchical nearest-neighbor prediction-Prediction Transform coding
  • Lifting Transform interpolation-based hierarchical nearest -neighbor prediction with an update/lifting step
  • attribute encoding is not limited to the above-described example.
  • the color conversion unit 40006 performs color conversion coding for converting color values (or textures) included in attributes.
  • the color converter 40006 may convert the format of color information (eg, convert RGB to YCbCr).
  • the operation of the color converter 40006 according to embodiments may be optionally applied according to color values included in the attributes.
  • the geometry reconstruction unit 40005 reconstructs (decompresses) an octree and/or an approximated octree.
  • the geometry reconstruction unit 40005 reconstructs an octree/voxel based on a result of analyzing the distribution of points.
  • the reconstructed octree/voxel may be referred to as a reconstructed geometry (or a reconstructed geometry).
  • the attribute transform unit 40007 performs an attribute transform that transforms attributes based on positions to which geometry encoding has not been performed and/or a reconstructed geometry. As described above, since the attributes are dependent on the geometry, the attribute transform unit 40007 may transform the attributes based on the reconstructed geometry information. For example, the attribute conversion unit 40007 may convert an attribute of a point at the position based on the position value of the point included in the voxel. As described above, when the position of the center point of a voxel is set based on the positions of one or more points included in one voxel, the attribute conversion unit 40007 converts attributes of the one or more points. When the tri-soup geometry encoding is performed, the attribute conversion unit 40007 may convert the attributes based on the tri-soup geometry encoding.
  • the attribute conversion unit 40007 is an average value of attributes or attribute values (eg, color of each point, reflectance, etc.) of neighboring points within a specific position/radius from the position (or position value) of the central point of each voxel. can be calculated to perform attribute transformation.
  • the attribute conversion unit 40007 may apply a weight according to the distance from the center point to each point when calculating the average value.
  • each voxel has a position and a computed attribute (or attribute value).
  • the attribute conversion unit 40007 may search for neighboring points existing within a specific position/radius from the position of the center point of each voxel based on the K-D tree or the Morton code.
  • the K-D tree is a binary search tree and supports a data structure that can manage points based on location so that Nearest Neighbor Search-NNS is possible quickly.
  • the Molton code represents a coordinate value (eg (x, y, z)) indicating a three-dimensional position of all points as a bit value, and is generated by mixing the bits. For example, if the coordinate value indicating the position of the point is (5, 9, 1), the bit value of the coordinate value is (0101, 1001, 0001).
  • the attribute transform unit 40007 may align the points based on the Molton code value and perform a shortest neighbor search (NNS) through a depth-first traversal process. After the attribute transformation operation, if the nearest neighbor search (NNS) is required in another transformation process for attribute coding, a K-D tree or a Molton code is used.
  • NSS shortest neighbor search
  • the converted attributes are input to the RAHT conversion unit 40008 and/or the LOD generation unit 40009.
  • the RAHT converter 40008 performs RAHT coding for predicting attribute information based on the reconstructed geometry information.
  • the RAHT transform unit 40008 may predict attribute information of a node at an upper level of the octree based on attribute information associated with a node at a lower level of the octree.
  • the LOD generator 40009 generates a level of detail (LOD) to perform predictive transform coding.
  • LOD level of detail
  • the LOD according to the embodiments indicates the detail of the point cloud content, and the smaller the LOD value, the lower the detail of the point cloud content, and the higher the LOD value, the higher the detail of the point cloud content. Points may be classified according to LOD.
  • the lifting transform unit 40010 performs lifting transform coding that transforms the attributes of the point cloud based on weights. As described above, lifting transform coding may be selectively applied.
  • the coefficient quantizer 40011 quantizes the attribute-coded attributes based on coefficients.
  • the arithmetic encoder 40012 encodes the quantized attributes based on arithmetic coding.
  • the elements of the point cloud encoder of FIG. 4 are hardware including one or more processors or integrated circuits configured to communicate with one or more memories included in the point cloud providing device. , software, firmware, or a combination thereof.
  • the one or more processors may perform at least any one or more of the operations and/or functions of the elements of the point cloud encoder of FIG. 4 described above.
  • the one or more processors may operate or execute a set of software programs and/or instructions for performing operations and/or functions of the elements of the point cloud encoder of FIG. 4 .
  • One or more memories in accordance with embodiments may include high speed random access memory, non-volatile memory (eg, one or more magnetic disk storage devices, flash memory devices, or other non-volatile solid state memory). memory devices (such as solid-state memory devices).
  • FIG. 5 illustrates an example of a voxel according to embodiments.
  • voxel 5 is an octree structure that recursively subdivides a bounding box defined by two poles (0,0,0) and (2 d , 2 d , 2 d ).
  • An example of a voxel generated through One voxel includes at least one or more points.
  • a voxel may estimate spatial coordinates from a positional relationship with a voxel group.
  • voxels have attributes (such as color or reflectance) like pixels of a 2D image/image.
  • a detailed description of the voxel is the same as that described with reference to FIG. 4 and thus will be omitted.
  • FIG. 6 shows an example of an octree and an occupancy code according to embodiments.
  • the point cloud content providing system (point cloud video encoder 10002) or point cloud encoder (eg, octree analysis unit 40002) efficiently manages the area and/or position of voxels
  • octree geometry coding (or octree coding) based on the octree structure is performed.
  • FIG. 6 shows the octree structure.
  • the three-dimensional space of the point cloud content according to the embodiments is expressed by axes (eg, X-axis, Y-axis, and Z-axis) of the coordinate system.
  • An octree structure is created by recursive subdividing a bounding box defined by two poles (0,0,0) and (2 d , 2 d , 2 d ). . 2d may be set to a value constituting the smallest bounding box surrounding all points of the point cloud content (or point cloud video).
  • d represents the depth of the octree.
  • the value of d is determined according to the following equation. In the following equation (x int n , y int n , z int n ) represents positions (or position values) of quantized points.
  • the entire 3D space may be divided into eight spaces according to the division.
  • Each divided space is expressed as a cube with six faces.
  • each of the eight spaces is again divided based on the axes of the coordinate system (eg, X-axis, Y-axis, and Z-axis). Therefore, each space is further divided into 8 small spaces.
  • the divided small space is also expressed as a cube with six faces. This division method is applied until a leaf node of the octree becomes a voxel.
  • the lower part of FIG. 6 shows the occupancy code of the octree.
  • the occupancy code of the octree is generated to indicate whether each of the eight divided spaces generated by dividing one space includes at least one point.
  • one occupanci code is expressed by eight child nodes.
  • Each child node represents an occupancies of the divided space, and each child node has a value of 1 bit. Therefore, the occupanci code is expressed as an 8-bit code. That is, if at least one point is included in the space corresponding to the child node, the corresponding node has a value of 1. If the space corresponding to the child node does not contain a point (empty), the node has a value of 0. Since the occupanci code shown in FIG.
  • a point cloud encoder (eg, arithmetic encoder 40004 ) according to embodiments may entropy encode the occupanci code. In addition, to increase the compression efficiency, the point cloud encoder can intra/inter-code the occupanci code.
  • the receiving apparatus (eg, the receiving apparatus 10004 or the point cloud video decoder 10006) according to embodiments reconstructs an octree based on the occupanci code.
  • the point cloud encoder (eg, the point cloud encoder of FIG. 4 , or the octree analyzer 40002) according to embodiments may perform voxelization and octree coding to store positions of points.
  • the points in the 3D space are not always evenly distributed, there may be a specific area where there are not many points. Therefore, it is inefficient to perform voxelization on the entire 3D space. For example, if there are few points in a specific area, there is no need to perform voxelization up to the corresponding area.
  • the point cloud encoder does not perform voxelization on the above-described specific region (or a node other than a leaf node of an octree), but directly codes positions of points included in the specific region. ) can be done. Coordinates of direct coding points according to embodiments are called direct coding mode (DCM).
  • DCM direct coding mode
  • the point cloud encoder according to embodiments may perform trisoup geometry encoding for reconstructing positions of points in a specific region (or node) based on a voxel based on a surface model.
  • Tri-Soop geometry encoding is a geometry encoding that expresses the representation of an object as a series of triangle meshes.
  • the point cloud decoder can generate a point cloud from the mesh surface.
  • Direct coding and trisup geometry encoding according to embodiments may be selectively performed.
  • direct coding and trisup geometry encoding according to embodiments may be performed in combination with octree geometry coding (or octree coding).
  • the option to use a direct mode for applying direct coding must be activated, and a node to which direct coding is to be applied is not a leaf node, but is less than a threshold within a specific node. points must exist. In addition, the number of whole points to be subjected to direct coding must not exceed a preset limit value. If the above condition is satisfied, the point cloud encoder (or the arithmetic encoder 40004 ) according to the embodiments may entropy-code positions (or position values) of points.
  • the point cloud encoder (for example, the surface appropriation analyzer 40003) according to the embodiments determines a specific level of the octree (when the level is smaller than the depth d of the octree), and from that level, a node using the surface model It is possible to perform tri-soup geometry encoding, which reconstructs the position of a point in a region based on voxels (tri-soup mode).
  • the point cloud encoder may designate a level to which tri-soup geometry encoding is to be applied. For example, if the specified level is equal to the depth of the octree, the point cloud encoder will not operate in tri-soup mode.
  • the point cloud encoder may operate in the tri-soup mode only when the specified level is smaller than the depth value of the octree.
  • a three-dimensional cube region of nodes of a specified level according to embodiments is called a block.
  • One block may include one or more voxels.
  • a block or voxel may correspond to a brick.
  • the geometry is represented as a surface.
  • a surface according to embodiments may intersect each edge of the block at most once.
  • a vertex existing along an edge is detected when there is at least one occupied voxel adjacent to the edge among all blocks sharing the edge.
  • An ocupided voxel means a voxel including a point. The position of the vertex detected along the edge is the average position along the edge of all voxels of all voxels adjacent to the edge among all blocks sharing the edge.
  • the point cloud encoder When a vertex is detected, the point cloud encoder according to the embodiments entropy-codes the starting point (x, y, z) of the edge, the direction vectors ( ⁇ x, ⁇ y, ⁇ z) of the edge, and the vertex position values (relative position values within the edge).
  • the point cloud encoder eg, the geometry reconstruction unit 40005
  • the point cloud encoder performs triangle reconstruction, up-sampling, and voxelization processes. to create a reconstructed geometry (reconstructed geometry).
  • Vertices located on the edge of a block determine the surface that passes through the block.
  • the surface according to embodiments is a non-planar polygon.
  • the triangle reconstruction process reconstructs the surface represented by a triangle based on the starting point of the edge, the direction vector of the edge, and the position value of the vertex.
  • the triangle reconstruction process is as follows. 1 Calculate the centroid of each vertex, 2 perform the square on the values obtained by subtracting the centroid from each vertex value, and obtain the sum of all the values.
  • the minimum value of the added values is obtained, and the projection process is performed along the axis with the minimum value. For example, if the x element is the minimum, each vertex is projected on the x-axis with respect to the center of the block and projected on the (y, z) plane. If the value that comes out when projecting on the (y, z) plane is (ai, bi), the ⁇ value is obtained through atan2(bi, ai), and the vertices are aligned based on the ⁇ value.
  • the table below shows combinations of vertices for generating a triangle according to the number of vertices. Vertices are sorted in order from 1 to n.
  • the table below shows that for four vertices, two triangles can be formed according to a combination of vertices.
  • the first triangle may be composed of 1st, 2nd, and 3rd vertices among the aligned vertices
  • the second triangle may be composed of 3rd, 4th, and 1st vertices among the aligned vertices. .
  • the upsampling process is performed to voxelize the triangle by adding points along the edge of the triangle. Create additional points based on the upsampling factor and the width of the block. The additional points are called refined vertices.
  • the point cloud encoder may voxel the refined vertices.
  • the point cloud encoder may perform attribute encoding based on the voxelized position (or position value).
  • FIG. 7 shows an example of a neighbor node pattern according to embodiments.
  • the point cloud encoder may perform entropy coding based on context adaptive arithmetic coding.
  • the point cloud content providing system or the point cloud encoder directly transmits the occupanci code.
  • Entropy coding is possible.
  • the point cloud content providing system or point cloud encoder performs entropy encoding (intra-encoding) based on the occupancies of the current node and the occupancies of neighboring nodes, or entropy encoding (inter-encoding) based on the occupancies of the previous frame. ) can be done.
  • a frame according to embodiments means a set of point cloud videos generated at the same time.
  • a point cloud encoder determines occupancy of neighboring nodes of each node of an octree and obtains a neighbor pattern value.
  • the neighbor node pattern is used to infer the occupancies pattern of the corresponding node.
  • the left side of FIG. 7 shows a cube corresponding to a node (a cube located in the center) and six cubes (neighboring nodes) that share at least one face with the cube.
  • the nodes shown in the figure are nodes of the same depth (depth).
  • the numbers shown in the figure represent the weights (1, 2, 4, 8, 16, 32, etc.) associated with each of the six nodes. Each weight is sequentially assigned according to the positions of neighboring nodes.
  • the right side of FIG. 7 shows the neighboring node pattern values.
  • the neighbor node pattern value is the sum of values multiplied by the weights of the ocupided neighbor nodes (neighbor nodes with points). Therefore, the neighbor node pattern values range from 0 to 63. When the neighbor node pattern value is 0, it indicates that there is no node (ocupid node) having a point among the neighboring nodes of the corresponding node. When the neighbor node pattern value is 63, it indicates that all of the neighboring nodes are ocupid nodes. As shown in the figure, since neighboring nodes to which weights 1, 2, 4, and 8 are assigned are ocupided nodes, the neighboring node pattern value is 15, which is the sum of 1, 2, 4, and 8.
  • the point cloud encoder may perform coding according to the neighboring node pattern value (eg, when the neighboring node pattern value is 63, 64 types of coding are performed). According to embodiments, the point cloud encoder may change the neighbor node pattern value (eg, based on a table that changes 64 to 10 or 6) to reduce coding complexity.
  • the encoded geometry is reconstructed (decompressed).
  • the geometry reconstruction operation may include changing the arrangement of the direct coded points (eg, placing the direct coded points in front of the point cloud data).
  • tri-soap geometry encoding is applied, the geometry reconstruction process is triangular reconstruction, upsampling, and voxelization. Since the attribute is dependent on the geometry, attribute encoding is performed based on the reconstructed geometry.
  • the point cloud encoder may reorganize the points by LOD.
  • the figure shows the point cloud content corresponding to the LOD.
  • the left side of the figure shows the original point cloud content.
  • the second figure from the left of the figure shows the distribution of the points of the lowest LOD, and the rightmost figure of the figure shows the distribution of the points of the highest LOD. That is, the points of the lowest LOD are sparsely distributed, and the points of the highest LOD are tightly distributed. That is, as the LOD increases according to the direction of the arrow indicated at the bottom of the drawing, the interval (or distance) between the points becomes shorter.
  • the point cloud content providing system or the point cloud encoder (for example, the point cloud video encoder 10002, the point cloud encoder of FIG. 4, or the LOD generator 40009) generates an LOD. can do.
  • the LOD is created by reorganizing the points into a set of refinement levels according to a set LOD distance value (or set of Euclidean Distance).
  • the LOD generation process is performed not only in the point cloud encoder but also in the point cloud decoder.
  • FIG. 9 shows examples (P0 to P9) of points of point cloud content distributed in a three-dimensional space.
  • the original order of FIG. 9 indicates the order of points P0 to P9 before LOD generation.
  • the LOD based order of FIG. 9 indicates the order of points according to the LOD generation. Points are rearranged by LOD. Also, the high LOD includes points belonging to the low LOD.
  • LOD0 includes P0, P5, P4 and P2.
  • LOD1 includes the points of LOD0 and P1, P6 and P3.
  • LOD2 includes points of LOD0, points of LOD1, and P9, P8 and P7.
  • the point cloud encoder may perform predictive transform coding, lifting transform coding, and RAHT transform coding selectively or in combination.
  • the point cloud encoder may generate predictors for points and perform predictive transform coding to set prediction attributes (or prediction attribute values) of each point. That is, N predictors may be generated for N points.
  • the prediction attribute (or attribute value) is a weight calculated based on the distance to each neighboring point in the attributes (or attribute values, for example, color, reflectance, etc.) of neighboring points set in the predictor of each point (or the weight value) is set as the average value of the multiplied value.
  • the point cloud encoder eg, the coefficient quantization unit 40011
  • the quantization process is shown in the following table.
  • the point cloud encoder (eg, the arithmetic encoder 40012) according to the embodiments may entropy-code the quantized and dequantized residual values as described above when there are points adjacent to the predictor of each point.
  • the point cloud encoder according to the examples (eg, the arithmetic encoder 40012) may entropy-code the attributes of each point without performing the above-described process if there are no neighboring points in the predictor of each point.
  • a point cloud encoder (eg, lifting transform unit 40010) generates a predictor of each point, sets an LOD calculated in the predictor, registers neighboring points, and weights according to distances to neighboring points
  • Lifting transform coding can be performed by setting .Lifting transform coding according to embodiments is similar to the aforementioned predictive transform coding, except that a weight is accumulated and applied to an attribute value. The process of cumulatively applying weights to values is as follows.
  • the weights calculated for all predictors are additionally multiplied by the weights stored in the QW corresponding to the predictor index, and the calculated weights are cumulatively added to the update weight array as the indices of neighboring nodes.
  • the value obtained by multiplying the calculated weight by the attribute value of the index of the neighbor node is accumulated and summed.
  • a predicted attribute value is calculated by additionally multiplying an attribute value updated through the lift update process by a weight updated through the lift prediction process (stored in QW).
  • a point cloud encoder eg, the coefficient quantization unit 40011
  • a point cloud encoder eg, arithmetic encoder 40012
  • entropy codes the quantized attribute values.
  • the point cloud encoder (for example, the RAHT transform unit 40008) according to the embodiments may perform RAHT transform coding for estimating the attributes of nodes of a higher level by using an attribute associated with a node at a lower level of the octree.
  • RAHT transform coding is an example of attribute intra coding with octree backward scan.
  • the point cloud encoder according to the embodiments scans the entire area from the voxel, and repeats the merging process up to the root node while merging the voxels into a larger block at each step.
  • the merging process according to the embodiments is performed only for the ocupid node. A merging process is not performed on an empty node, and a merging process is performed on a node immediately above the empty node.
  • g lx, y, and z represent the average attribute values of voxels in level l.
  • g lx, y, z can be calculated from g l+1 2x, y, z and g l+1 2x+1, y, z .
  • g l-1 x, y, z are low-pass values, which are used in the merging process at the next higher level.
  • h l-1 x, y, and z are high-pass coefficients, and the high-pass coefficients in each step are quantized and entropy-coded (eg, encoding of the arithmetic encoder 400012 ).
  • the root node is created as follows through the last g 1 0, 0, 0 and g 1 0, 0, 1 ,
  • FIG. 10 shows an example of a point cloud decoder according to embodiments.
  • the point cloud decoder shown in FIG. 10 is an example of the point cloud video decoder 10006 described in FIG. 1 , and may perform the same or similar operations to the operation of the point cloud video decoder 10006 described in FIG. 1 .
  • the point cloud decoder may receive a geometry bitstream and an attribute bitstream included in one or more bitstreams.
  • the point cloud decoder includes a geometry decoder and an attribute decoder.
  • the geometry decoder outputs decoded geometry by performing geometry decoding on the geometry bitstream.
  • the attribute decoder outputs decoded attributes by performing attribute decoding based on the decoded geometry and the attribute bitstream.
  • the decoded geometry and decoded attributes are used to reconstruct the point cloud content (decoded point cloud).
  • FIG. 11 shows an example of a point cloud decoder according to embodiments.
  • the point cloud decoder shown in FIG. 11 is an example of the point cloud decoder described with reference to FIG. 10 , and may perform a decoding operation that is a reverse process of the encoding operation of the point cloud encoder described with reference to FIGS. 1 to 9 .
  • the point cloud decoder may perform geometry decoding and attribute decoding. Geometry decoding is performed before attribute decoding.
  • a point cloud decoder may include an arithmetic decoder 11000, a synthesize octree 11001, a synthesize surface approximation 11002, and a reconstruct geometry , 11003), inverse transform coordinates (11004), arithmetic decoder (11005), inverse quantize (11006), RAHT transform unit (11007), LOD generator (generate LOD, 11008) ), inverse lifting unit (Inverse lifting, 11009), and / or color inverse transform unit (inverse transform colors, 11010).
  • the arithmetic decoder 11000 , the octree synthesizer 11001 , the surface opproximation synthesizer 11002 , the geometry reconstruction unit 11003 , and the coordinate system inverse transformation unit 11004 may perform geometry decoding.
  • Geometry decoding according to embodiments may include direct coding and trisoup geometry decoding. Direct coding and trisup geometry decoding are optionally applied. Also, the geometry decoding is not limited to the above example, and is performed as a reverse process of the geometry encoding described with reference to FIGS. 1 to 9 .
  • the arithmetic decoder 11000 decodes the received geometry bitstream based on arithmetic coding.
  • the operation of the arithmetic decoder 11000 corresponds to the reverse process of the arithmetic encoder 40004 .
  • the octree synthesizer 11001 may generate an octree by obtaining an occupanci code from a decoded geometry bitstream (or information about a geometry secured as a result of decoding).
  • a detailed description of the occupanci code is the same as described with reference to FIGS. 1 to 9 .
  • the surface op-proximation synthesizing unit 11002 may synthesize a surface based on a decoded geometry and/or a generated octree when trisupe geometry encoding is applied.
  • the geometry reconstruction unit 11003 may reconstruct a geometry based on the surface and/or the decoded geometry. As described with reference to FIGS. 1 to 9 , direct coding and tri-soup geometry encoding are selectively applied. Accordingly, the geometry reconstruction unit 11003 directly brings and adds position information of points to which direct coding is applied. In addition, when tri-soap geometry encoding is applied, the geometry reconstruction unit 11003 may perform a reconstruction operation of the geometry reconstruction unit 40005, for example, triangle reconstruction, up-sampling, and voxelization to restore the geometry. have. Specific details are the same as those described with reference to FIG. 6 and thus will be omitted.
  • the reconstructed geometry may include a point cloud picture or frame that does not include attributes.
  • the coordinate system inverse transform unit 11004 may obtain positions of points by transforming the coordinate system based on the restored geometry.
  • the arithmetic decoder 11005, the inverse quantization unit 11006, the RAHT transform unit 11007, the LOD generator 11008, the inverse lifting unit 11009, and/or the inverse color transform unit 11010 are the attributes described with reference to FIG. decoding can be performed.
  • Attribute decoding according to embodiments includes Region Adaptive Hierarchical Transform (RAHT) decoding, Interpolaration-based hierarchical nearest-neighbor prediction-Prediction Transform decoding, and interpolation-based hierarchical nearest-neighbor prediction with an update/lifting step (Lifting Transform)) decoding may be included.
  • RAHT Region Adaptive Hierarchical Transform
  • Interpolaration-based hierarchical nearest-neighbor prediction-Prediction Transform decoding Interpolaration-based hierarchical nearest-neighbor prediction-Prediction Transform decoding
  • interpolation-based hierarchical nearest-neighbor prediction with an update/lifting step (Lifting Transform)) decoding may be included.
  • the arithmetic decoder 11005 decodes an attribute bitstream by arithmetic coding.
  • the inverse quantization unit 11006 inverse quantizes the decoded attribute bitstream or information on the attribute secured as a result of decoding, and outputs inverse quantized attributes (or attribute values). Inverse quantization may be selectively applied based on attribute encoding of the point cloud encoder.
  • the RAHT transformation unit 11007, the LOD generation unit 11008, and/or the inverse lifting unit 11009 may process the reconstructed geometry and dequantized attributes. As described above, the RAHT converting unit 11007, the LOD generating unit 11008, and/or the inverse lifting unit 11009 may selectively perform a corresponding decoding operation according to the encoding of the point cloud encoder.
  • the color inverse transform unit 11010 performs inverse transform coding for inverse transforming color values (or textures) included in decoded attributes.
  • the operation of the color inverse transform unit 11010 may be selectively performed based on the operation of the color transform unit 40006 of the point cloud encoder.
  • the elements of the point cloud decoder of FIG. 11 are hardware including one or more processors or integrated circuits configured to communicate with one or more memories included in the point cloud providing device. , software, firmware, or a combination thereof.
  • the one or more processors may perform at least any one or more of the operations and/or functions of the elements of the point cloud decoder of FIG. 11 described above.
  • the one or more processors may operate or execute a set of software programs and/or instructions for performing operations and/or functions of the elements of the point cloud decoder of FIG. 11 .
  • the transmission device shown in FIG. 12 is an example of the transmission device 10000 of FIG. 1 (or the point cloud encoder of FIG. 4 ).
  • the transmitting apparatus shown in FIG. 12 may perform at least any one or more of the same or similar operations and methods to the operations and encoding methods of the point cloud encoder described with reference to FIGS. 1 to 9 .
  • the transmission apparatus includes a data input unit 12000 , a quantization processing unit 12001 , a voxelization processing unit 12002 , an occupancy code generation unit 12003 , a surface model processing unit 12004 , and an intra/ Inter-coding processing unit 12005, arithmetic coder 12006, metadata processing unit 12007, color conversion processing unit 12008, attribute conversion processing unit (or attribute conversion processing unit) 12009, prediction/lifting/RAHT conversion It may include a processing unit 12010 , an arithmetic coder 12011 , and/or a transmission processing unit 12012 .
  • the data input unit 12000 receives or acquires point cloud data.
  • the data input unit 12000 may perform the same or similar operation and/or acquisition method to the operation and/or acquisition method of the point cloud video acquisition unit 10001 (or the acquisition process 20000 described in FIG. 2 ).
  • the coder 12006 performs geometry encoding. Since the geometry encoding according to the embodiments is the same as or similar to the geometry encoding described with reference to FIGS. 1 to 9 , a detailed description thereof will be omitted.
  • the quantization processing unit 12001 quantizes a geometry (eg, a position value or a position value of points).
  • the operation and/or quantization of the quantization processing unit 12001 is the same as or similar to the operation and/or quantization of the quantization unit 40001 described with reference to FIG. 4 .
  • a detailed description is the same as that described with reference to FIGS. 1 to 9 .
  • the voxelization processing unit 12002 voxelizes position values of quantized points.
  • the voxelization processing unit 12002 may perform the same or similar operations and/or processes to those of the quantization unit 40001 described with reference to FIG. 4 and/or the voxelization process. A detailed description is the same as that described with reference to FIGS. 1 to 9 .
  • the octree occupancy code generator 12003 performs octree coding on the positions of voxelized points based on the octree structure.
  • the octree occupancy code generator 12003 may generate an occult code.
  • the octree occupancy code generator 12003 may perform the same or similar operations and/or methods to those of the point cloud encoder (or the octree analyzer 40002) described with reference to FIGS. 4 and 6 . A detailed description is the same as that described with reference to FIGS. 1 to 9 .
  • the surface model processing unit 12004 may perform tri-supply geometry encoding for reconstructing positions of points in a specific region (or node) based on voxels based on a surface model.
  • the fore surface model processing unit 12004 may perform the same or similar operations and/or methods to those of the point cloud encoder (eg, the surface appropriation analyzer 40003) described with reference to FIG. 4 .
  • a detailed description is the same as that described with reference to FIGS. 1 to 9 .
  • the intra/inter coding processing unit 12005 may perform intra/inter coding of point cloud data.
  • the intra/inter coding processing unit 12005 may perform the same or similar coding to the intra/inter coding described with reference to FIG. 7 . A detailed description is the same as that described with reference to FIG. 7 .
  • the intra/inter coding processing unit 12005 may be included in the arithmetic coder 12006 .
  • the arithmetic coder 12006 entropy encodes an octree and/or an approximated octree of point cloud data.
  • the encoding method includes an arithmetic encoding method.
  • the arithmetic coder 12006 performs the same or similar operations and/or methods as the operations and/or methods of the arithmetic encoder 40004 .
  • the metadata processing unit 12007 processes metadata related to point cloud data, for example, a setting value, and provides it to necessary processing such as geometry encoding and/or attribute encoding. Also, the metadata processing unit 12007 according to embodiments may generate and/or process signaling information related to geometry encoding and/or attribute encoding. Signaling information according to embodiments may be encoded separately from geometry encoding and/or attribute encoding. Also, signaling information according to embodiments may be interleaved.
  • the color conversion processing unit 12008, the attribute conversion processing unit 12009, the prediction/lifting/RAHT conversion processing unit 12010, and the arithmetic coder 12011 perform attribute encoding. Since the attribute encoding according to the embodiments is the same as or similar to the attribute encoding described with reference to FIGS. 1 to 9 , a detailed description thereof will be omitted.
  • the color conversion processing unit 12008 performs color conversion coding for converting color values included in the attributes.
  • the color conversion processing unit 12008 may perform color conversion coding based on the reconstructed geometry.
  • the description of the reconstructed geometry is the same as described with reference to FIGS. 1 to 9 .
  • the same or similar operation and/or method to the operation and/or method of the color conversion unit 40006 described with reference to FIG. 4 is performed. A detailed description will be omitted.
  • the attribute transformation processing unit 12009 performs an attribute transformation for transforming attributes based on positions where geometry encoding has not been performed and/or a reconstructed geometry.
  • the attribute transformation processing unit 12009 performs the same or similar operations and/or methods to those of the attribute transformation unit 40007 described in FIG. 4 . A detailed description will be omitted.
  • the prediction/lifting/RAHT transform processing unit 12010 may code the transformed attributes in any one or a combination of RAHT coding, predictive transform coding, and lifting transform coding.
  • the prediction/lifting/RAHT transformation processing unit 12010 performs at least one or more of the same or similar operations to the operations of the RAHT transformation unit 40008, the LOD generation unit 40009, and the lifting transformation unit 40010 described with reference to FIG. 4 . do.
  • the descriptions of predictive transform coding, lifting transform coding, and RAHT transform coding are the same as those described in FIGS. 1 to 9 , detailed descriptions thereof will be omitted.
  • the arithmetic coder 12011 may encode coded attributes based on arithmetic coding.
  • the arithmetic coder 12011 performs the same or similar operations and/or methods to the operations and/or methods of the arithmetic encoder 400012 .
  • the transmission processing unit 12012 transmits each bitstream including the encoded geometry and/or encoded attribute and metadata information, or converts the encoded geometry and/or the encoded attribute and metadata information into one It can be transmitted by composing it as a bitstream.
  • the bitstream may include one or more sub-bitstreams.
  • the bitstream according to the embodiments includes 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 tile Signaling information including a Tile Parameter Set (TPS) for level signaling and slice data may be included.
  • SPS sequence parameter set
  • GPS geometry parameter set
  • APS attribute parameter set
  • TPS Tile Parameter Set
  • Slice data may include information about one or more slices.
  • One slice according to embodiments may include one geometry bitstream (Geom00) and one or more attribute bitstreams (Attr00, Attr10).
  • a slice refers to a series of syntax elements representing all or a part of a coded point cloud frame.
  • the TPS may include information about each tile (eg, coordinate value information and height/size information of a bounding box, etc.) for one or more tiles.
  • a geometry bitstream may include a header and a payload.
  • the header of the geometry bitstream according to the embodiments may include identification information (geom_ parameter_set_id), a tile identifier (geom_tile_id), a slice identifier (geom_slice_id) of a parameter set included in GPS, and information on data included in a payload, etc.
  • the metadata processing unit 12007 may generate and/or process signaling information and transmit it to the transmission processing unit 12012 .
  • elements performing geometry encoding and elements performing attribute encoding may share data/information with each other as dotted line processing.
  • the transmission processing unit 12012 may perform the same or similar operation and/or transmission method to the operation and/or transmission method of the transmitter 10003 . Since the detailed description is the same as that described with reference to FIGS. 1 to 2 , a detailed description thereof will be omitted.
  • FIG. 13 is an example of a receiving apparatus according to embodiments.
  • the receiving device shown in FIG. 13 is an example of the receiving device 10004 of FIG. 1 (or the point cloud decoder of FIGS. 10 and 11 ).
  • the receiving apparatus shown in FIG. 13 may perform at least any one or more of the same or similar operations and methods to the operations and decoding methods of the point cloud decoder described with reference to FIGS. 1 to 11 .
  • the reception apparatus includes a reception unit 13000 , a reception processing unit 13001 , an arithmetic decoder 13002 , an Occupancy code-based octree reconstruction processing unit 13003 , and a surface model processing unit (triangle reconstruction). , up-sampling, voxelization) 13004, inverse quantization processing unit 13005, metadata parser 13006, arithmetic decoder 13007, inverse quantization processing unit 13008, prediction It may include a /lifting/RAHT inverse transformation processing unit 13009 , an inverse color transformation processing unit 13010 , and/or a renderer 13011 .
  • Each component of decoding according to embodiments may perform a reverse process of a component of encoding according to embodiments.
  • the receiver 13000 receives point cloud data.
  • the receiver 13000 may perform the same or similar operation and/or reception method to the operation and/or reception method of the receiver 10005 of FIG. 1 . A detailed description will be omitted.
  • the reception processing unit 13001 may acquire a geometry bitstream and/or an attribute bitstream from the received data.
  • the reception processing unit 13001 may be included in the reception unit 13000 .
  • the arithmetic decoder 13002 , the occupancy code-based octree reconstruction processing unit 13003 , the surface model processing unit 13004 , and the inverse quantization processing unit 13005 may perform geometry decoding. Since the geometry decoding according to the embodiments is the same as or similar to the geometry decoding described with reference to FIGS. 1 to 10 , a detailed description thereof will be omitted.
  • the arithmetic decoder 13002 may decode a geometry bitstream based on arithmetic coding.
  • the arithmetic decoder 13002 performs the same or similar operations and/or coding to the operations and/or coding of the arithmetic decoder 11000 .
  • the occupancy code-based octree reconstruction processing unit 13003 may reconstruct the octopus by obtaining an occupanci code from a decoded geometry bitstream (or information about a geometry secured as a result of decoding).
  • the occupancy code-based octree reconstruction processing unit 13003 performs the same or similar operations and/or methods to those of the octree synthesis unit 11001 and/or the octree generation method.
  • the surface model processing unit 13004 may decode a trichop geometry based on the surface model method and reconstruct a geometry related thereto (eg, triangle reconstruction, up-sampling, voxelization) based on the surface model method, when trisoop geometry encoding is applied. can be performed.
  • the surface model processing unit 13004 performs the same or similar operations to those of the surface op-proximation synthesis unit 11002 and/or the geometry reconstruction unit 11003 .
  • the inverse quantization processing unit 13005 may inverse quantize the decoded geometry.
  • the metadata parser 13006 may parse metadata included in the received point cloud data, for example, a setting value.
  • the metadata parser 13006 may pass the metadata to geometry decoding and/or attribute decoding. A detailed description of the metadata is the same as that described with reference to FIG. 12 , and thus will be omitted.
  • the arithmetic decoder 13007, the inverse quantization processing unit 13008, the prediction/lifting/RAHT inverse transformation processing unit 13009, and the inverse color transformation processing unit 13010 perform attribute decoding. Since the attribute decoding is the same as or similar to the attribute decoding described with reference to FIGS. 1 to 10 , a detailed description thereof will be omitted.
  • the arithmetic decoder 13007 may decode an attribute bitstream by arithmetic coding.
  • the arithmetic decoder 13007 may perform decoding of the attribute bitstream based on the reconstructed geometry.
  • the arithmetic decoder 13007 performs the same or similar operations and/or coding to the operations and/or coding of the arithmetic decoder 11005 .
  • the inverse quantization processing unit 13008 may inverse quantize the decoded attribute bitstream.
  • the inverse quantization processing unit 13008 performs the same or similar operations and/or methods to those of the inverse quantization unit 11006 and/or the inverse quantization method.
  • the prediction/lifting/RAHT inverse transform processing unit 13009 may process the reconstructed geometry and inverse quantized attributes.
  • the prediction/lifting/RAHT inverse transform processing unit 13009 performs the same or similar operations and/or decodings as the operations and/or decodings of the RAHT transform unit 11007, the LOD generation unit 11008 and/or the inverse lifting unit 11009 and/or At least any one or more of the decodings are performed.
  • the color inverse transform processing unit 13010 according to embodiments performs inverse transform coding for inverse transforming color values (or textures) included in decoded attributes.
  • the color inverse transform processing unit 13010 performs the same or similar operation and/or inverse transform coding to the operation and/or inverse transform coding of the color inverse transform unit 11010 .
  • the renderer 13011 may render point cloud data.
  • FIG. 14 illustrates an example of a structure capable of interworking with a method/device for transmitting and receiving point cloud data according to embodiments.
  • the structure of FIG. 14 includes at least one or more of a server 1460 , a robot 1410 , an autonomous vehicle 1420 , an XR device 1430 , a smartphone 1440 , a home appliance 1450 , and/or an HMD 1470 .
  • a configuration connected to the cloud network 1410 is shown.
  • the robot 1410 , the autonomous driving vehicle 1420 , the XR device 1430 , the smartphone 1440 , or the home appliance 1450 are referred to as devices.
  • the XR device 1430 may correspond to a point cloud data (PCC) device according to embodiments or may be linked with the PCC device.
  • PCC point cloud data
  • the cloud network 1400 may constitute a part of the cloud computing infrastructure or may refer to a network existing in the cloud computing infrastructure.
  • the cloud network 1400 may be configured using a 3G network, a 4G or Long Term Evolution (LTE) network, or a 5G network.
  • LTE Long Term Evolution
  • the server 1460 includes at least one of a robot 1410 , an autonomous vehicle 1420 , an XR device 1430 , a smartphone 1440 , a home appliance 1450 and/or an HMD 1470 , and a cloud network 1400 . It is connected through and may help at least a part of the processing of the connected devices 1410 to 1470 .
  • a Head-Mount Display (HMD) 1470 represents one of the types in which an XR device and/or a PCC device according to embodiments may be implemented.
  • the HMD type device according to the embodiments includes a communication unit, a control unit, a memory unit, an I/O unit, a sensor unit, a power supply unit, and the like.
  • the devices 1410 to 1450 shown in FIG. 14 may be linked/coupled with the point cloud data transmission/reception device according to the above-described embodiments.
  • XR / PCC device 1430 is PCC and / or XR (AR + VR) technology is applied, HMD (Head-Mount Display), HUD (Head-Up Display) provided in the vehicle, television, mobile phone, smart phone, It may be implemented as a computer, a wearable device, a home appliance, a digital signage, a vehicle, a stationary robot, or a mobile robot.
  • HMD Head-Mount Display
  • HUD Head-Up Display
  • the XR/PCC device 1430 analyzes three-dimensional point cloud data or image data acquired through various sensors or from an external device to generate position data and attribute data for three-dimensional points in the surrounding space or real objects. Information can be obtained and the XR object to be output can be rendered and output. For example, the XR/PCC apparatus 1430 may output an XR object including additional information on the recognized object to correspond to the recognized object.
  • the XR/PCC device 1430 may be implemented as a mobile phone 1440 or the like to which PCC technology is applied.
  • the mobile phone 1440 may decode and display the point cloud content based on the PCC technology.
  • the autonomous driving vehicle 1420 may be implemented as a mobile robot, a vehicle, an unmanned aerial vehicle, etc. by applying PCC technology and XR technology.
  • the autonomous driving vehicle 1420 to which the XR/PCC technology is applied may mean an autonomous driving vehicle equipped with a means for providing an XR image or an autonomous driving vehicle subject to control/interaction within the XR image.
  • the autonomous driving vehicle 1420 that is the target of control/interaction within the XR image may be distinguished from the XR device 1430 and may be interlocked with each other.
  • the autonomous vehicle 1420 having means for providing an XR/PCC image may obtain sensor information from sensors including a camera, and output an XR/PCC image generated based on the acquired sensor information.
  • the autonomous vehicle 1420 may provide an XR/PCC object corresponding to a real object or an object in the screen to the occupant by outputting an XR/PCC image with a HUD.
  • the XR/PCC object when the XR/PCC object is output to the HUD, at least a portion of the XR/PCC object may be output to overlap the real object to which the passenger's gaze is directed.
  • the XR/PCC object when the XR/PCC object is output to a display provided inside the autonomous vehicle, at least a portion of the XR/PCC object may be output to overlap the object in the screen.
  • the autonomous vehicle 1220 may output XR/PCC objects corresponding to objects such as a lane, other vehicles, traffic lights, traffic signs, two-wheeled vehicles, pedestrians, and buildings.
  • VR Virtual Reality
  • AR Augmented Reality
  • MR Magnetic Reality
  • PCC Point Cloud Compression
  • VR technology is a display technology that provides objects or backgrounds in the real world only as CG images.
  • AR technology refers to a technology that shows a virtual CG image on top of a real object image.
  • MR technology is similar to the aforementioned AR technology in that it shows virtual objects by mixing and combining them in the real world.
  • real objects and virtual objects made of CG images are clear, and virtual objects are used in a form that complements real objects, whereas in MR technology, virtual objects are regarded as having the same characteristics as real objects. distinct from technology. More specifically, for example, a hologram service to which the aforementioned MR technology is applied.
  • VR, AR, and MR technologies are sometimes called XR (extended reality) technologies rather than clearly distinguishing them. Accordingly, embodiments of the present invention are applicable to all of VR, AR, MR, and XR technologies.
  • encoding/decoding based on PCC, V-PCC, and G-PCC technology may be applied.
  • the PCC method/apparatus according to the embodiments may be applied to a vehicle providing an autonomous driving service.
  • a vehicle providing an autonomous driving service is connected to a PCC device to enable wired/wireless communication.
  • the point cloud data (PCC) transceiver receives/processes AR/VR/PCC service-related content data that can be provided together with the autonomous driving service when connected to a vehicle to enable wired/wireless communication, can be sent to
  • the point cloud transceiver may receive/process AR/VR/PCC service-related content data according to a user input signal input through the user interface device and provide it to the user.
  • a vehicle or a user interface device may receive a user input signal.
  • a user input signal according to embodiments may include a signal indicating an autonomous driving service.
  • a method/apparatus for transmitting point cloud data is a transmitting device 10000 of FIG. 1 , a point cloud video encoder 10002 , a transmitter 10003 , and an acquisition-encoding-transmitting (20000-20001-20002) of FIG. 2 .
  • the encoder of FIG. 4 the transmitter of FIG. 12
  • the device of FIG. 14 the encoder of FIGS. 15 , 17 , 36 , and the like.
  • a method/apparatus for receiving point cloud data is a receiving device 10004, a receiver 10005, a point cloud video decoder 10006 of FIG. 1, and a transmission-decoding-rendering (20002-20003-20004) of FIG. , the decoder of Figs. 10-11, the receiving apparatus of Fig. 13, the device of Fig. 14, the decoders of Figs. 16, 26, 37, and the like.
  • the method/device for transmitting and receiving point cloud data according to the embodiments may be abbreviated as a method/device according to the embodiments.
  • geometry data, geometry information, location information, and the like constituting point cloud data are interpreted to have the same meaning.
  • Attribute data, attribute information, and attribute information constituting the point cloud data are interpreted as the same meaning.
  • a method/device relates to a method and an apparatus for encoding and decoding time information about a point cloud (data) (method and device for Time encoding and decoding on Point Cloud).
  • point cloud data having one or more frames may further include time attribute information. That is, as a method for coding temporal attribute information, geometric information using temporal information can be reconstructed to be applied to an inter-frame prediction technique requiring high accuracy.
  • Embodiments address techniques for compressing data organized into point clouds. Specifically, it relates to a method of encoding/decoding by adding time information for accurate compression of a point cloud having one or more frames.
  • 15 shows an apparatus for transmitting point cloud data according to embodiments.
  • Fig. 15 is an encoder or encoder, the transmitter 10000, the point cloud video encoder 10002, the transmitter 10003 of Fig. 1, the acquire-encode-transmit (20000-20001-20002) of Fig. 2, and the encoder of Fig. 4 , the transmitter of FIG. 12 , the device of FIG. 14 , the encoder of FIGS. 17 and 36 , and the like.
  • Each component of FIG. 15 may correspond to hardware, software, a processor, and/or a combination thereof.
  • the transmitting apparatus may perform inter prediction of the point cloud.
  • the PCC encoder may include a geometry encoder and/or an attribute information encoder.
  • the PCC data may be included as geometric information and/or attribute information of the point.
  • the geometric information includes (x, y) in a two-dimensional Cartesian coordinate system or ( ⁇ , ⁇ ) in a cylindrical coordinate system, or (x, y, z) in a three-dimensional space, or ( ⁇ , ⁇ , z) in a cylindrical coordinate system, ( ⁇ , ⁇ , ) may be a coordinate vector. That is, geometric information (geometric data) may be expressed based on various coordinate systems.
  • Attribute information is obtained from one or more sensors, such as a vector (R,G,B) representing the color of a point or/and a brightness value or/and a reflection coefficient of a lidar or/and a temperature value obtained from a thermal imaging camera. It can be a single-value vector.
  • the space dividing unit may divide the input PCC data into at least one 3D block.
  • a block may mean a tile group, a tile, a slice, or a coding unit (CU), a prediction unit (PU), or a transformation unit (TU).
  • Partitioning according to embodiments may be performed based on at least one of an octree, a quadtree, a binary tree, a triple tree, and a k-d tree.
  • a point cloud may be represented and divided based on a tree having a hierarchical structure. Alternatively, it may be divided into blocks of predetermined horizontal and vertical heights. Alternatively, it can be divided by selectively determining various positions and sizes of blocks.
  • the corresponding information may be entropy-encoded and transmitted to a decoder.
  • the space divider may divide the space in which the point cloud data is distributed in detail based on a tree shape having a hierarchical structure. Point cloud data can be efficiently encoded based on the divided space.
  • the geometric information encoding unit encodes the received geometric information and generates a geometric information bitstream and restored geometric information. Encoding may be performed in units of whole point clouds or sub-point clouds or coding units, and inter prediction or intra prediction may be selected for each coding unit. Also, an inter prediction mode or an intra prediction mode may be selected for each prediction unit.
  • the bitstream generated through the geometric information encoding unit may be transmitted to the PCC decoder. In addition, the generated restored geometric information may be input to the attribute information encoder.
  • the attribute information encoder receives the received attribute information and generates an attribute information bitstream.
  • the generated attribute information bitstream may be transmitted to a PCC decoder.
  • 16 shows an apparatus for receiving point cloud data according to embodiments.
  • the receiving apparatus in Fig. 16 includes the receiving apparatus 10004, the receiver 10005, the point cloud video decoder 10006 in Fig. 1, the transmission-decoding-rendering (20002-20003-20004) in Fig. 2, and the decoder in Figs. , the receiver of Fig. 13, the device of Fig. 14, the decoders of Figs. 26 and 37, and the like.
  • Each component in FIG. 16 may correspond to hardware, software, a processor, and/or a combination thereof.
  • the PCC data may include point geometry and/or attribute information.
  • the PCC decoder may include a geometry decoder and/or an attribute information encoder.
  • the spatial division unit performs division by parsing the sub-point cloud determined from the encoder and/or division information such as encoding/decoding unit (CU), prediction unit (PU), or transformation unit (TU).
  • division information such as encoding/decoding unit (CU), prediction unit (PU), or transformation unit (TU).
  • the encoding/decoding unit, the prediction unit, and the transformation unit may have the same partition structure or different partition structures according to embodiments.
  • the geometric information decoding unit may perform decoding in the entire point cloud or sub-point cloud or in units of encoding/decoding, and may determine whether intra-prediction or inter-prediction flags are parsed for each encoding/decoding unit by parsing.
  • prediction may be performed by parsing mode information of inter prediction or intra prediction for each prediction unit.
  • the attribute information decoding unit may receive the restored geometric information as an input and perform decoding.
  • Decoding of attribute information may be performed in the entire point cloud or sub-point cloud or in units of encoding/decoding, and may be determined by parsing a flag whether it is intra prediction or inter prediction for each encoding/decoding unit.
  • prediction may be performed by parsing mode information of inter prediction or intra prediction for each prediction unit.
  • the attribute information decoding unit may be omitted.
  • Embodiments may perform inter prediction in place of time information as a frame index for point cloud data.
  • point cloud data obtained from an actual LiDAR sensor As the LiDAR sensor rotates, it obtains point information of the point cloud data or transmits point information to LiDAR sensors located at different locations. will be obtained Therefore, even if each point is refined and located in the same frame, a difference in captured time may exist.
  • Embodiments include a method of reconstructing point cloud contents by coding time information in the process of acquiring a point cloud and predicting geometric information using a specific capture time and a method of constructing geometric information.
  • time information can be processed as independent encoding/decoding information.
  • Time information can be composed of time index and time unit.
  • the time index may be mapped with geometric information.
  • the time index may be classified into point/point group/point cloud unit and transmitted by being included in the bitstream as higher-level information.
  • Time information may be transmitted as an index by dividing it into one or more upper/lower ranges based on a specific criterion. When it is separated into upper/lower ranges and transmitted, index information can be written if it is to be configured for each sequence.
  • time information When time information is divided into point groups, time information can be shared. In the case of a point group sharing time information, a representative time index can be selected. Point groups can be divided in the horizontal/vertical direction. There may be more than one representative time index included in the same point group. Residual information may be coded for the remaining points included in the same point group using the representative time index.
  • 17 shows a time information encoding unit according to embodiments.
  • Fig. 17 is an encoder or encoder; It is a time information encoder (encoder) included or corresponding to the encoder of FIG. 4, the transmitter of FIG. 12, the device of FIG. 14, and the encoder of FIGS. 15 and 36.
  • a method/apparatus for transmitting point cloud data may encode geometry data, encode attribute data, and encode time information and transmit.
  • Attribute data and/or time information may be encoded dependently on geometric data (position of a point).
  • Each component of FIG. 17 may correspond to hardware, software, a processor, and/or a combination thereof.
  • the time information encoding unit receives the time information and outputs a time information bitstream based on the time information-geometric information mapping unit, the time information range separation unit, the time information quantization unit, the representative time information difference derivation unit, and the time information entropy encoding unit.
  • Each step may be selectively performed and the order of execution may be changed.
  • the time information may be encoded using an attribute information encoding unit.
  • Time information-geometric information mapping unit
  • the time information t may be the time at which the point was acquired, and may be composed of a time index and a time unit.
  • the first point may have first time information.
  • the first time information may be expressed in terms of a first time index and/or a first time unit.
  • the time index t_idx may be an integer.
  • the time unit may be m/n seconds (s), and n and m may be integers.
  • a method for transmitting point cloud data may include encoding point cloud data; and transmitting a bitstream including the point cloud data; may include
  • the encoding of the point cloud data includes: encoding geometry data of the point cloud data; encoding attribute data of the point cloud data; and encoding time information of the point cloud data. , may include
  • FIG. 18 shows a configuration of time information of point cloud data according to embodiments.
  • the time index processed in FIG. 17 and the like may exist in a point unit, a point group unit, or a point cloud unit as shown in FIG. 18 .
  • a process of mapping with the restored geometric information may be performed.
  • the time index may be transmitted through subsequent processes.
  • the time unit may be transmitted in higher level information in unit of point cloud.
  • each point in space may have a time index.
  • each group corresponding to one or more points may have a time index.
  • the point cloud unit the entire acquired point cloud (points) may have a time index.
  • FIG. 19 illustrates an operation of a time information-geometric information mapping unit according to embodiments.
  • FIG. 19 shows the operation of the time information-geometric information mapping unit shown in FIG.
  • Time information-geometric information mapping unit
  • the coordinates of the current point (_(+1)) can be transformed and encoded based on a vector.
  • the m points according to embodiments may exist on the same plane.
  • the three-dimensional coordinates of the X-axis may be transmitted to the start point designated for each specific interval within one frame.
  • the points may be encoded in consideration of time information.
  • Embodiments may encode and decode points in the time order in which they are captured. For example, if there is a time when 'some' input value came in, it can be coded by sorting the point position (i.e., geometry information) from the one with the earliest capture time order, since the position information of the point can be substituted by coding the corresponding time. .
  • point position i.e., geometry information
  • a process (grouping) for bundling the points Pn to Pn+1 immediately before the time information tn to tn+1 with the time information tn may be performed.
  • a vector value can be calculated and transmitted.
  • a and b may be the vector difference between the coordinate values found by projecting the point immediately before Pn and Pn+1 onto the X-Y plane.
  • vector information may not be decoded. That is, since time information and point information are mapped to group points using time information, it may be information that is no longer needed after grouping. Instead of vector information, the last index of the point cloud to be included in single high-level information can be delivered as signaling information.
  • 20 and 21 show a method of expressing reference time information higher order information according to embodiments.
  • the time information range separating unit may divide the initial time index derived from the time information sampling unit into a plurality of ranges as shown in FIG. 20 and transmit it in a separate method.
  • the time information sampling unit may refer to a processing unit that processes the operation of the time information-geometric information mapping unit of FIG. 17 to generate time information.
  • the initial time index is a time index that constitutes the time information generated in FIGS. 17 to 18 , and may be an initial value of the time index.
  • the initial value may mean the value of the time index by bits located at the front.
  • the subrange of the initial time index 1 to n (bit) can transmit the value and up to n+1 to m (bit).
  • the upper range can be transmitted in the form of an index or table corresponding to the value.
  • the index can be transmitted from the frame or slice unit parameter set by selecting the upper range from the upper range list.
  • the upper range list may consist of one or multiple upper ranges and may be transmitted in units of point cloud sequences.
  • the upper range table When the upper range of the time information index is transmitted to the upper range table in sequence units, the upper range table may be transmitted in sequence units. A plurality of point clouds with continuous time may have the same upper range.
  • the upper range table can be composed of an index with the upper range and the last point cloud having the corresponding upper range.
  • the time information quantization unit of FIG. 17 may divide the point cloud (point cloud data) into time information sharing point groups.
  • a group of points having time information may be created as shown in FIG. 18 .
  • the time information of the point cloud as shown in FIG. 18 may be divided, and the group of divided points may share the time information.
  • a point group sharing time information can be divided, and a representative time index of the time information sharing point group can be selected.
  • a representative time index representing a group of points sharing time information may be selected from among time indices of time information in the group of points.
  • the time information sharing point group may be a group of one or a plurality of points sharing time information in the point cloud.
  • the time information quantization unit may divide a node including the point cloud into sub-nodes.
  • a point group included in the area of the same sub-node can be set as a time information sharing point group.
  • a point in a subnode may not exist, or more than one point may exist.
  • the lower range is location information of points belonging to the upper range. It is used to signal a common time index among points, and to bundle position information occurring in the same time zone into one.
  • point cloud unit If it is done in point cloud unit, it has the same time index in slice unit, and multiple slices can exist in the same frame. It must be possible to group by time unit.
  • Higher range information may be set according to higher_range_timeinfo[i] information according to embodiments.
  • the number of time information belonging to the upper range or the number of groups according to the time information may be changed, and the number of bits of the upper range of the time index may also be changed accordingly.
  • this is to define an upper range in units of frames or slices, and a table can be managed as common information in a sequence, that is, all contents.
  • the reason for defining transmission as a table of sequence units is that the use case in which points are grouped by time index (without frame boundary) within the frame range is considered.
  • the upper range is time information, and the point cloud index may be point information or index of the last position among points belonging to the upper range.
  • the encoding of time information includes mapping time information and geometry data, separating ranges for time information, quantizing time information, to time information. It may include a step of deriving a difference for the representative time index based on the representative time index related to the included time index.
  • an upper range and a lower range for the time index included in the time information are created, and a list or table including the upper range for the time index can create
  • FIG. 22 shows a coordinate system of a spherical coordinate system for point geometry information according to embodiments.
  • the sub-nodes described in FIGS. 20 and 21 are vertical angles based on the spherical coordinate system of FIG. 22 .
  • specific range and horizontal angle of It may be a non-overlapping area including a specific range of .
  • Each subnode can have a range of the same or different size. By dividing a node into a vertical angular axis and a horizontal angular axis, subnodes that do not overlap each other can be created, and the number of divisions and the length of divisions can be transmitted along each axis.
  • FIG. 23 shows an example of a sub-node and a time information sharing point group according to embodiments.
  • 23(a) is an example in which the horizontal angular axis is divided into n sub-nodes.
  • the time information quantization unit may assign an index to the subnode according to a specific scan order.
  • the scan order may be derived according to the same rules as the encoder in the decoder with or without transmission.
  • the sub-node index assigned to the sub-node may be derived based on the horizontal or vertical angular range of the sub-node. For example, the smaller the minimum horizontal angle of the subnode, the smaller the subnode index, and when the horizontal angle range is the same, the larger the vertical angle range, the smaller the subnode index.
  • the representative time index of the time information sharing point group can be derived by performing the following process.
  • the time index of a specific point in the time information sharing point group or the average time index of a plurality of points is the representative time index. can be specified as
  • the temporal information quantization unit may divide a space in which point cloud data (points, point clouds) are distributed (nodes, sub-nodes), and may set a representative index corresponding to a space group. Accordingly, time information for points can be quantized.
  • Fig. 24 shows the operation of the representative time information difference inducing unit of Fig. 17;
  • the time information expression conversion unit may convert the representative time index of the sub-nodes in the node into the start representative time index and the representative time index interval.
  • the time constant expression conversion unit may correspond to the representative time information difference derivation unit.
  • the representative time index of the subnode with the lowest index within the node may be the smallest, and the smallest representative time index is the start representative time index.
  • the representative time index may increase equally or unevenly.
  • the representative time index interval is between subnodes with adjacent indexes (in Fig. 24). , ... ) can be the interval of the representative time index.
  • a plurality of intervals of equal and unequal representative time index intervals may exist at arbitrary positions. The interval may mean from a subnode to a subnode having a larger index.
  • An equal interval may transmit the last sub-node index of the interval and one representative time index interval. In the unequal section, the last sub-node index of the section and all or part of the representative time index interval included in the section can be transmitted.
  • FIG. 24 means that a difference value can be calculated by combining a horizontal index and a vertical index separately and signaling. 23, since it is calculated based on the index without separating the horizontal and vertical, equal sections (e.g., subnodes 0-1, 1-2, 3-4, 4-5, 6-7, 7-8, 8- 9) and unequal sections (2 ⁇ 3, 5 ⁇ 6) may exist.
  • equal sections e.g., subnodes 0-1, 1-2, 3-4, 4-5, 6-7, 7-8, 8- 9
  • unequal sections (2 ⁇ 3, 5 ⁇ 6)
  • the step of inducing a difference for the representative time index is to generate a start representative time index and a representative time index interval for the representative time index, and the representative time index interval is a representative time index between nodes including a group. may be the difference between
  • the step of inducing a difference for the representative time index is to generate a start representative time index and a representative time index interval for the representative time index
  • the representative time index interval is a representative time index between nodes including a group.
  • the representative time index interval may include a representative time index vertical interval between nodes vertically adjacent to a node and a representative time index horizontal interval between nodes horizontally adjacent to a node.
  • a node according to embodiments may be a term referring to a sub-node included in the node.
  • 25 shows a representative time index difference expression method according to embodiments.
  • FIG. 25 shows an operation of a time information expression conversion unit that may be included in the time information encoding unit of FIG. 17 .
  • the time information expression conversion unit is an embodiment related to the generation of the representative time index and the generation of the representative time index interval by the representative time information difference inducing unit.
  • the representative time index interval of FIG. 24 may be composed of a representative time index horizontal interval and a representative time index vertical interval.
  • Representative time index horizontal interval (Fig. 25) ) may be a time index interval between subnodes having adjacent horizontal angle ranges.
  • the horizontal angular range may be an interval between the representative time indices of sub-node 0 and sub-node 3 having the same vertical angular range.
  • Representative time index vertical interval (Fig. 25) ) is a representative time index between subnodes having the same horizontal angle range and adjacent vertical angle ranges (subnodes (0,1), (1,2), (3,4), (4,5) in Fig. 25). may be an interval of
  • the subsequent process may be performed separately from the representative time index horizontal interval and the representative time index vertical interval.
  • a plurality of sections with equal and unequal horizontal intervals of the representative time index may exist at an arbitrary location.
  • the interval may mean from a subnode to a subnode having a larger index.
  • An equal interval may transmit the last sub-node index of the interval and one representative time index interval.
  • the last sub-node index of the section and all or part of the representative time index interval included in the section can be transmitted.
  • the same process as the representative time index horizontal interval may be performed.
  • the temporal information entropy encoding unit may perform entropy encoding on information for transmitting the upper range of the reference time information or the lower range of the reference time information.
  • various encoding methods such as Exponential Golomb, Context-Adaptive Variable Length Coding (CAVLC), and Context-Adaptive Binary Arithmetic Coding (CABAC) may be used.
  • 26 shows a time information decoding unit according to embodiments.
  • Fig. 26 shows the receiving device 10004, the receiver 10005, the point cloud video decoder 10006 of Fig. 1, the transmission-decoding-rendering (20002-20003-20004) of Fig. 2, the decoder of Figs. 10-11, Fig. 13 may be included in or correspond to the receiving apparatus of , the device of FIG. 14, the decoder of FIGS. 16 and 37, and the like.
  • Each component in FIG. 26 may correspond to hardware, software, a processor, and/or a combination thereof.
  • the time information decoding unit receives the time information bitstream from the point cloud data transmission device as shown in FIG. 26, and time information based on the time information entropy decoding unit, the representative time information restoration unit, the time information inverse quantization unit, and the time information range summing unit can be printed out. Each step may be omitted or the order may be changed.
  • the operation of the temporal information decoder may correspond to the operation of the temporal information encoder of FIG. 17 or may follow a reverse process of the operation of the temporal information encoder.
  • the time information entropy decoding unit may receive the time information bitstream and perform entropy decoding. For example, for entropy decoding, various methods such as Exponential Golomb, Context-Adaptive Variable Length Coding (CAVLC), and Context-Adaptive Binary Arithmetic Coding (CABAC) may be applied.
  • various methods such as Exponential Golomb, Context-Adaptive Variable Length Coding (CAVLC), and Context-Adaptive Binary Arithmetic Coding (CABAC) may be applied.
  • the representative time information restoration unit may restore the representative time index of each sub-node.
  • a method for receiving point cloud data includes receiving a bitstream including point cloud data; and decoding the point cloud data; may include
  • the decoding of the point cloud data may include decoding geometry data of the point cloud data, decoding attribute data of the point cloud data, and decoding time information of the point cloud data.
  • Decoding the time information according to the embodiments includes restoring representative time information for a group including one or more point cloud data, and converting the representative time index into a time index for the point cloud data included in the group. It may include the step of designating, adding the time index corresponding to the upper range to the time index for the point cloud data.
  • the time information interval between nodes can be restored in the order of the indexes of the nodes.
  • the representative time information differential mode may refer to methods for expressing the representative time index difference as shown in FIGS. 24 and 25 .
  • the representative time information differential mode is 0, as shown in Fig. 24, the difference between the representative time indexes (the representative time index interval) is sorted from the smallest to the largest of the representative time indexes, and then the difference between the representative time indices is expressed. Examples can be shown.
  • the representative time information difference mode is 1, as shown in FIG. 25, an embodiment in which the difference between the representative time indices is expressed in each of the horizontal and vertical directions in space can be shown.
  • the representative time information restoration unit may parse the number of sub-node sections and the start or end node index of each node. It is possible to parse whether the time index interval within each sub-node section is equal. In case of equality, one time index interval value can be parsed. If it is not uniform, the number of time index intervals in the sub-node interval may be parsed, and the time index interval may be parsed as much as the number of time index intervals.
  • the representative time information of each subnode can be restored by parsing the start representative time index, which is the representative time index of the node with the smallest index, and adding each time index interval sequentially starting with the start representative time index.
  • the step of restoring representative time information includes parsing the start representative time index based on the index of the node corresponding to the group, and summing the time index intervals for the nodes adjacent to the start representative time index to each node. It is possible to restore representative time information for
  • 29 shows decoding of representative time information according to embodiments.
  • the representative time index vertical interval and the representative time index horizontal interval can be parsed or derived in the same manner as in mode 0, respectively.
  • Arbitrary representative time index can be derived in the same way as in FIG. 12 .
  • the representative time index of the node with the smallest index among the nodes with the same vertical angle range as the index n node having as the representative time index is can be is the starting representative time index It can be derived by summing multiple representative time index horizontal intervals. class One or more representative time index vertical intervals between by adding to can induce
  • the representative time index vertical interval and the representative time index horizontal interval are parsed, the start representative time index is parsed based on the index of the node corresponding to the group, and the start representative
  • the representative time index can be derived by adding the horizontal interval of the representative time index to the time index, and the vertical interval of the representative time index can be added to the representative time index.
  • the time information dequantization unit may designate the representative time index of each sub-node restored by the representative time information restoration unit as the time index of the point in each sub-node.
  • an index is assigned to the sub-node in a specific order, and a representative time index matching the index can be assigned to the sub-node.
  • a method of dividing a node into sub-nodes may be as follows. As shown in FIG. 29 , the horizontal angular axis and the vertical angular axis may be divided, respectively.
  • the horizontal angular axis may be divided into n pieces, and the divided horizontal angular axes may be uniform in a specific section and non-uniform in a specific section. n can be parsed. For each section, the largest horizontal angle within each section and whether the section is equally divided may be parsed. When the section is equally divided, the section can be divided into the same length by parsing one split length. When the section is a non-uniform division, the section can be divided by parsing the number of sections to be divided and the division length corresponding to the number of sections.
  • the vertical angle axis can also be divided in the same way as the horizontal angle axis.
  • An index can be assigned to a subnode according to the parsed or derived subnode scan order in the same way as the encoder.
  • the representative time index of the same order as the index of the sub-node can be designated as the representative time index of the sub-node.
  • the representative time index can be designated as the time index of all points in the subnode.
  • the time information range summing unit of FIG. 26 is the time information range summing unit of FIG. 26:
  • the time information range summing unit may add the upper range of the time index to the time index given to the point by the time information inverse quantization unit.
  • the time index given to the point by the time information dequantization unit may be a subrange of the time index. It is possible to share the same upper range of the time index in units of point cloud groups.
  • the upper range temporal index of the current frame can be derived by referring to the temporal index upper range list or table.
  • the time index upper range table can be referred to in sequence units.
  • the temporal index high-range table can be parsed in sequence units.
  • the superrange table may contain N superranges and the last point cloud index that shares each superrange.
  • the upper range mapped to the current point cloud index can be derived by referring to the table.
  • the final time index can be derived by summing the derived upper range of the time index with the lower range of the time index.
  • the upper range time index may be shifted by n bits and then summed with the lower range time index.
  • the time index upper range list may be referred to in order to derive the time index upper range.
  • the temporal index upper range list can be parsed in sequence units.
  • the temporal index upper range list can be composed of M time index upper ranges, and the index of the temporal index upper range to be referenced in the list can be parsed in units of point clouds.
  • the final time index can be derived by summing the derived upper range of the time index with the lower range of the time index.
  • the upper range time index may be shifted by n bits and then summed with the lower range time index.
  • 31 shows a bitstream including point cloud data according to embodiments.
  • Fig. 31 shows the transmitting device 10000 of Fig. 1, the point cloud video encoder 10002, the transmitter 10003, the acquiring-encoding-transmitting (20000-20001-20002) of Fig. 2, the encoder of Fig. 4, and the transmitting of Fig. 12 It represents a bitstream including encoded point cloud data and related parameter information generated by the apparatus, the device of FIG. 14, the encoders of FIGS. 15, 17, 36, and the like.
  • the bitstream of Fig. 31 is the receiving device 10004, the receiver 10005, the point cloud video decoder 10006 of Fig. 1, the transmission-decoding-rendering (20002-20003-20004) of Fig. 2, and the decoder of Figs. , is decoded by the receiver of Fig. 13, the device of Fig. 14, the decoders of Figs. 16, 26, 37, or the like.
  • the parameter according to the embodiments may include information for buffer management of the restored geometry/attribute information.
  • a sequence parameter set may include information indicating that inter prediction (inter-frame prediction coding) has been performed.
  • a sequence parameter set may include all or part of related information.
  • a tile parameter set or the like may include each piece of information.
  • Each slice may include a geometry, an attribute, and a time slice header TSH, and the TSH may include a time data unit.
  • the syntax element defined as follows can be applied not only to the current point cloud data stream but also to a plurality of point cloud data streams, a parameter set of a higher concept, etc. can be transmitted through
  • SPS Sequence Parameter Set
  • GPS Geometry Parameter Set
  • APS Attribute Parameter Set
  • TIP Time Information Parameter Set (Time) Info Parameter Set
  • TPS Tile Parameter Set
  • attributes (Attr): Attribute bitstream attribute blick header + attribute brick data
  • TSH Time Slice Header
  • Time time data unit ( time data unit).
  • parameters may be generated in a process of a transmitter according to embodiments to be described later, and transmitted to a receiver according to embodiments to be used in a reconfiguration process can be
  • the parameters according to the embodiments may be generated by the metadata processing unit (or metadata generator) of the transmitting apparatus according to the embodiments to be described later, and may be obtained from the metadata parser of the receiving apparatus according to the embodiments. .
  • FIG. 32 shows some syntax of a sequence parameter set included in the bitstream of FIG.
  • Embodiments may add time attribute information to the SPS.
  • a flag for signaling the presence or absence of time information coding may be added to the SPS.
  • Time coding flag (Time_coding_flag): A flag indicating that time attribute information is included in the contents and can be decoded (Time_coding_flag is 1, time attribute information coding, 0 means not including time attribute information) have)
  • the sequence parameter set may further include the following elements:
  • simple_profile_compatibility_flag 1 specifies that the bitstream conforms to the simple profile.
  • simple_profile_compatibility_flag 0 specifies that the bitstream conforms to a profile other than the simple profile.
  • dense_profile_compatibility_flag 1 specifies that the bitstream complies with the Dense profile.
  • density_profile_compatibility_flag 0 specifies that the bitstream conforms to a profile other than the Dense profile.
  • predictive_profile_compatibility_flag 1 specifies that the bitstream conforms to the predictive profile.
  • predictive_profile_compatibility_flag 0 specifies that the bitstream conforms to a profile different from the prediction profile.
  • main_profile_compatibility_flag equal to 1 specifies that the bitstream conforms to the default profile.
  • main_profile_compatibility_flag 0 specifies that the bitstream conforms to profiles other than the main profile.
  • reserved_profile_compatibility_18bits MUST be equal to 0 in bitstreams conforming to this version of this document. Another value for reserved_profile_compatibility_18bits is reserved for future use in ISO/IEC. The decoder ignores the value of reserved_profile_compatibility_18bits.
  • slice_reordering_constraint_flag equal to 1 indicates that the bitstream is sensitive to reordering and removal of data units.
  • slice_reordering_constraint_flag 0 indicates that the bitstream is not sensitive to reordering and removal of data units.
  • unique_point_positions_constraint_flag equal to 1 indicates that every output point has a unique position in each point cloud frame referencing the current SPS.
  • unique_point_positions_constraint_flag 0 indicates that two or more output points may have the same position in any point cloud frame referring to the current SPS.
  • level_idc indicates the level to which the bitstream conforms as specified in Annex A.
  • the bitstream shall not contain any level_idc values other than those specified in Annex A. Other values of level_idc are reserved for future use in ISO/IEC.
  • sps_seq_parameter_set_id Provides an identifier for the SPS that can be referenced by other syntax elements. sps_seq_parameter_set_id shall be zero in the bitstream conforming to this version of this document. Other values of sps_seq_parameter_set_id are reserved for future use in ISO/IEC.
  • frame_ctr_lsb_bits Specifies the length of the frame_ctr_lsb syntax element in bits.
  • slice_tag_bits Specifies the length of the slice_tag syntax element in bits.
  • seq_origin_bits Specifies the length in bits of the syntax element seq_origin_xyz[ k ].
  • seq_origin_xyz [ k ] and seq_origin_log2_scale Specifies the origin of the sequence local coordinate system. Index k is the kth X, Y or Z component of the origin coordinates. If not present, the seq_origin_xyz[ k ] and seq_origin_log2_scale values are inferred to be 0.
  • the array SeqOrigin is the origin of the sequence local coordinate system:
  • SeqOrigin[k] seq_origin_xyz[k] ⁇ seq_origin_log2_scale
  • seq_bounding_box_size_bits The bit length of the syntax element seq_bounding_box_size_minus1_xyz[ k ].
  • seq_bounding_box_size_xyz_minus1 [ k ]: plus 1 specifies, respectively, the kth component of the width, height, and depth of the coded volume dimension in the output coordinate system. If not present, the coded volume dimension is undefined.
  • seq_unit_numerator_minus1 Specifies the length of the output coordinate system X, Y, and Z unit vectors.
  • seq_global_scale_factor_log2 Specifies the fixed-point scale factor used to derive the output point position from the position in the sequence local coordinate system.
  • seq_global_scale_factor_log2 Used to derive a global scale factor to be applied to the location of the point cloud.
  • seq_global_scale_refinement_num_bits Bit length of the syntax element seq_global_scale_refinement_factor. If seq_global_scale_refinement_num_bits is equal to 0, no refinement is applied.
  • seq_global_scale_refinement_factor Specifies the refinement of the global scale value. If not present, seq_global_scale_refinement_factor is inferred to be equal to 0.
  • sps_num_attributes Specifies the number of attributes in the coded point cloud. It is a requirement of bitstream conformance that all slices have attribute data units corresponding to all attribute components listed in the SPS.
  • attribute_dimension_minus1 [ attrId ]: plus 1 specifies the number of components of the attrId-th attribute.
  • attribute_instance_id [ attrId ] Specifies the instance identifier for the attrId-th attribute.
  • attribute_bitdepth_minus1 [ attrId ] : plus 1 specifies the bit depth of each component of the attrId-th attribute signal(s).
  • known_attribute_label_flag [ attrId ] indicates whether the attribute is identified by the value of known_attibute_label[attrId] or by the object identifier attribute_label_oid[attrId].
  • the attribute type identified by known_attribute_label may be specified. If the value of known_attribute_label is not specified, it is reserved for future use by ISO/IEC.
  • attribute_label_oid is Recommendation ITU-T X.660
  • the attribute type may indicate Color, Reflectance, Opacity, Frame index, Frame number, Material identifier, Normal vector, and the like.
  • num_attribute_parameters Specifies the number of attribute parameter sets in the bitstream. Attribute parameters signaled from the sequence parameter set are applied to all data units in the coded point cloud sequence.
  • axis_coding_order Specifies the correspondence between the X, Y, and Z output axis labels and the three position components of all points in the reconstructed point cloud.
  • bypass_stream_enabled_flag 1 specifies that bypass coding mode can be used when reading the bitstream.
  • bypass_stream_enabled_flag 0 specifies that bypass coding mode is not used when reading the bitstream.
  • entropy_continuation_enabled_flag 1 indicates that the initial entropy context state of a slice may depend on the final entropy context state of a preceding slice.
  • entropy_continuation_enabled_flag 0 specifies that the initial entropy context state of each slice is independent.
  • slice_reordering_constaint_flag 0 is a requirement of bitstream conformance.
  • sps_extension_flag 0 specifies that the sps_extension_data_flag syntax element is not present in the SPS syntax structure.
  • sps_extension _flag shall be equal to 0 in bitstreams conforming to this version of this document.
  • a value of 1 of sps_extension _flag is reserved for future use in ISO/IEC. The decoder MUST ignore all sps_extension_data_flag syntax elements that follow the value 1 for sps_extension_flag in the SPS syntax structure.
  • sps_extension_data_flag can have any value. Its presence and value do not affect decoder compliance to the profile specified in Annex A. Decoders conforming to this version of this document MUST ignore all sps_extension_data_flag syntax elements.
  • 33 shows a set of time information parameters according to embodiments.
  • Time Info Parameter set (TIP):
  • the time information attribute is defined as a separate parameter set, TimeInfo_parameter_set (TIP), and can be parsed by adding time attribute information.
  • TIP TimeInfo_parameter_set
  • Slice n may include a geometry slice header, an attribute slice header, a time slice header, a geometry data unit, an attribute data unit, and a time data unit.
  • FIG. 34 shows a time slice header included in the bitstream of FIG.
  • Time attribute information to be connected to TIP which is an independent parameter set, may be included in the Time slice header.
  • Each higher_range_timeinfo_idx received from TIP can call time slice_header.
  • num_higher_range_timeinfo_in_list may mean the number of higher information included in the upper range time information list.
  • higher_range_timeinfo[i] may mean higher range information.
  • 35 shows a temporal data unit according to embodiments.
  • Fig. 35 shows a time data unit included in the bitstream of Fig. 31;
  • num_higher_range_timeinfo_in_table Indicates the number of higher information included in the upper range time information table.
  • num_frame[i] Indicates the number of consecutive frames to share higher range information from the current frame.
  • higher-order information may be signaled.
  • an upper range it is necessary to indicate the range of the point cloud included in the upper range, so information about the point coordinates or the number of all points is required.
  • the point cloud index of the corresponding index can be called. For example, it can be referred to as a context table. In such a case, separate low-level information may not be signaled. Since the point coordinate information is separately managed within the geometry data unit, it may be redundant information.
  • FIG 36 shows the structure of an apparatus for transmitting point cloud data according to embodiments.
  • the transmitting device (encoder) of FIG. 36 is the transmitting device 10000 of FIG. 1, the point cloud video encoder 10002, the transmitter 10003, the acquiring-encoding-transmitting (20000-20001-20002) of FIG. It can correspond to the encoder, the transmitter of FIG. 12, the device of FIG. 14, the encoders of FIGS. 15 and 17, and the like.
  • Each component in FIG. 36 may correspond to hardware, software, a processor, and/or a combination thereof.
  • the PCC encoder may be composed of a geometry encoder and/or an attribute information encoder.
  • PCC data may be composed of point geometry and/or attribute information.
  • Geometric information is (x, y) in a two-dimensional Cartesian coordinate system or ( ⁇ ) in a cylindrical coordinate system or (x, y, z) in a three-dimensional space or ( ⁇ z) in a cylindrical coordinate system, ( ⁇ ) coordinates in a spherical coordinate system It can be a vector.
  • Attribute information is obtained from one or more sensors, such as a vector (R,G,B) representing the color of a point or/and a brightness value or/and a reflection coefficient of a lidar or/and a temperature value obtained from a thermal imaging camera. It can be a single-value vector.
  • the space dividing unit may divide the input PCC data into at least one 3D block.
  • a block may mean a tile group, a tile, a slice, or a coding unit (CU), a prediction unit (PU), or a transformation unit (TU).
  • the division may be performed based on at least one of an octree, a quadtree, a binary tree, a triple tree, and a kd tree. Alternatively, it may be divided into blocks of predetermined horizontal and vertical heights. Alternatively, it can be divided by selectively determining various positions and sizes of blocks.
  • the corresponding information may be entropy-encoded and transmitted to a decoder.
  • the input PCC data may be divided into voxel groups such as slices, tiles, bricks, and subframes.
  • Cartesian (x, y, z) or cylindrical ( , , z) or a spherical coordinate system ( , , ) can be equally or unequally divided into one or more axes.
  • the geometric information encoding unit encodes the received geometric information and generates a geometric information bitstream and restored geometric information.
  • the generated bitstream may be transmitted to a PCC decoder.
  • the generated restored geometric information may be input to the attribute information encoder.
  • the attribute information encoder receives the received attribute information and generates an attribute information bitstream.
  • the generated attribute information bitstream may be transmitted to a PCC decoder.
  • the time information encoding unit generates a time information bitstream by encoding the received time information.
  • the generated time information bitstream may be transmitted to a PCC decoder.
  • FIG. 37 shows an apparatus for receiving point cloud data according to embodiments.
  • Fig. 37 shows the receiving device 10004, the receiver 10005, the point cloud video decoder 10006 of Fig. 1, the transmission-decoding-rendering (20002-20003-20004) of Fig. 2, the decoder of Figs. 10-11, Fig. 13 It can correspond to the receiving apparatus of , the device of FIG. 14, the decoder of FIGS. 16 and 26, and the like.
  • Each component in FIG. 37 may correspond to hardware, software, a processor, and/or a combination thereof.
  • the PCC decoder may include a geometric information decoder and an attribute information decoder.
  • the geometric information decoding unit decodes the received geometric information bitstream to restore the geometric information.
  • the restored geometric information or coding information of the geometric information may be input to the attribute information decoding unit.
  • the attribution information decoding unit restores the attribution information by receiving the received attribution information bitstream and the restored geometric information or the coding information of the geometric information received from the geometric information decoding unit.
  • the restored attribute information may be composed of restored PCC data together with the restored geometric information.
  • the time information decoding unit decodes the time information by using the received time information bitstream.
  • the restored time information may be composed of PCC data together with the restored attribute information and the restored geometric information.
  • 38 shows a method for transmitting point cloud data according to embodiments.
  • Transmitting device 10000 in Fig. 1 point cloud video encoder 10002, transmitter 10003, Acquisition-encoding-transmitting (20000-20001-20002) in Fig. 2, Encoder in Fig. 4, Transmitting device in Fig. 12, Fig.
  • the device of 14 and the encoders of FIGS. 15, 17, and 36 may encode point cloud data based on the method shown in FIG. 38, generate parameter information, and transmit the data in the form of a bitstream.
  • the method for transmitting point cloud data may include encoding the point cloud data.
  • the encoding operation according to the embodiments is performed by the transmitter 10000 of FIG. 1 , the point cloud video encoder 10002 , the encoding 20001 of FIG. 2 , the encoder of FIGS. 4 and 12 , the XR device 1430 of FIG. It may include operations such as the encoder, the time information encoder of FIG. 17, the bitstream generation of FIG. 31, the encoder of FIG. 36, and the like.
  • the method for transmitting point cloud data may further include transmitting a bitstream including the point cloud data.
  • the transmission operation according to the embodiments may include operations such as the transmitter 10003 of FIG. 1 , the transmission 20002 of FIG. 1 , the bitstream transmission according to the encoding of FIG. 15 , and the bitstream transmission of FIG. 31 .
  • a method for transmitting point cloud data according to embodiments may be performed by a point cloud data transmitting apparatus.
  • An apparatus for transmitting point cloud data according to embodiments includes an encoder for encoding point cloud data; and a transmitter for transmitting a bitstream including point cloud data; may include
  • the encoder for encoding point cloud data may include a geometry encoder for encoding geometry data of point cloud data, an attribute encoder for encoding attribute data of point cloud data, and a temporal information encoder for encoding time information of point cloud data. have.
  • a time information encoder includes a mapping unit for mapping time information and geometric data, a separation unit for separating ranges for time information, a quantization unit for quantizing time information, and a representative related to a time index included in time information It may include an induction unit for inducing a difference with respect to the representative time index based on the time index.
  • 39 shows a method of receiving point cloud data according to embodiments.
  • the receiving device 10004, the receiver 10005, the point cloud video decoder 10006 of FIG. 1, the transmit-decode-render (20002-20003-20004) of FIG. 2, the decoder of FIGS. 10-11, the receiving device of FIG. , the device of FIG. 14, and the decoder of FIGS. 16, 26, 39 may receive a bitstream, parse parameter information, and decode point cloud data based on the method as shown in FIG.
  • the method for receiving point cloud data may include receiving a bitstream including point cloud data.
  • the reception operation is the reception device 10004 of FIG. 1 , the receiver 10005 , and the transmission 20002 of FIG. 2 . It may include operations such as bitstream reception of FIG. 16 and bitstream reception of FIG. 31 .
  • the method for receiving point cloud data may further include decoding the point cloud data.
  • the decoding operation includes the point cloud video decoder 10006, the decoding 20003 of FIG. 2, the decoders of FIGS. 10-11 and 13, the XR device 1430 of FIG. 14, the decoder of FIG. 16, and the time of FIG. It may include operations such as information decoder, FIG. 31 bitstream decoding, and FIG. 37 decoder.
  • a method for receiving point cloud data may be performed by an apparatus for receiving point cloud data.
  • a point cloud data receiving apparatus includes: a receiving unit for receiving a bitstream including point cloud data; and a decoder for decoding the point cloud data; may include The decoder for decoding the point cloud data may include a geometry decoder for decoding the geometry data of the point cloud data, an attribute decoder for decoding the attribute data of the point cloud data, and a time information decoder for decoding the time information of the point cloud data. have.
  • the time information decoder includes a restoration unit for restoring representative time information for a group including one or more point cloud data, an inverse quantization unit for designating a representative time index as a time index for the point cloud data included in the group, point It may include a summing unit for adding the time index corresponding to the upper range to the time index for cloud data.
  • the restoration unit that restores the representative time information parses the start representative time index based on the index of the node corresponding to the group, and sums the time index intervals for the nodes adjacent to the start representative time index to obtain representative time information for each node.
  • the restoration unit for restoring the representative time information parses the representative time index vertical interval and the representative time index horizontal interval, parses the start representative time index based on the index of the node corresponding to the group, and parses the representative time index to the start representative time index.
  • the representative time index can be derived by summing the horizontal intervals, and the vertical interval of the representative time index can be added to the representative time index.
  • Various components of the apparatus of the embodiments may be implemented by hardware, software, firmware, or a combination thereof.
  • Various components of the embodiments may be implemented with one chip, for example, one hardware circuit.
  • the components according to the embodiments may be implemented with separate chips.
  • at least one or more of the components of the device according to the embodiments may be composed of one or more processors capable of executing one or more programs, and the one or more programs may be implemented Any one or more of the operations/methods according to the examples may be performed or may include instructions for performing the operations/methods.
  • Executable instructions for performing the method/acts of the apparatus according to the embodiments may be stored in non-transitory CRM or other computer program products configured for execution by one or more processors, or one or more may be stored in temporary CRM or other computer program products configured for execution by processors.
  • the memory according to the embodiments may be used as a concept including not only volatile memory (eg, RAM, etc.) but also non-volatile memory, flash memory, PROM, and the like. Also, it may be implemented in the form of a carrier wave, such as transmission through the Internet.
  • the processor-readable recording medium is distributed in a computer system connected through a network, so that the processor-readable code can be stored and executed in a distributed manner.
  • first, second, etc. may be used to describe various components of the embodiments. However, interpretation of various components according to the embodiments should not be limited by the above terms. These terms are only used to distinguish one component from another. it is only For example, the first user input signal may be referred to as a second user input signal. Similarly, the second user input signal may be referred to as a first user input signal. Use of these terms should be interpreted as not departing from the scope of the various embodiments. Although both the first user input signal and the second user input signal are user input signals, they do not mean the same user input signals unless the context clearly indicates otherwise.
  • the operations according to the embodiments described in this document may be performed by a transceiver including a memory and/or a processor according to the embodiments.
  • the memory may store programs for processing/controlling operations according to the embodiments, and the processor may control various operations described in this document.
  • the processor may be referred to as a controller or the like.
  • operations may be performed by firmware, software, and/or a combination thereof, and the firmware, software, and/or a combination thereof may be stored in a processor or stored in a memory.
  • the transceiver device may include a transceiver for transmitting and receiving media data, a memory for storing instructions (program code, algorithm, flowchart, and/or data) for a process according to embodiments, and a processor for controlling operations of the transmitting/receiving device.
  • a processor may be referred to as a controller or the like, and may correspond to, for example, hardware, software, and/or a combination thereof. Operations according to the above-described embodiments may be performed by a processor.
  • the processor may be implemented as an encoder/decoder or the like for the operation of the above-described embodiments.

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  • Engineering & Computer Science (AREA)
  • Multimedia (AREA)
  • Signal Processing (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Compression Or Coding Systems Of Tv Signals (AREA)

Abstract

Un procédé d'émission de données de nuage de points selon des modes de réalisation de l'invention peut comprendre les étapes consistant à : coder des données de nuage de points ; et émettre un flux binaire contenant ces données de nuage de points. Un dispositif de réception de données de nuage de points selon des modes de réalisation de l'invention peut comprendre : une unité de réception destinée à recevoir un flux binaire comprenant des données de nuage de points ; et un décodeur destiné à décoder les données de nuage de points.
PCT/KR2021/017419 2020-12-09 2021-11-24 Dispositif d'émission de données de nuage de points, procédé d'émission de données de nuage de points, dispositif de réception de données de nuage de points et procédé de réception de données de nuage de points WO2022124648A1 (fr)

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US20190318488A1 (en) * 2018-04-12 2019-10-17 Samsung Electronics Co., Ltd. 3d point cloud compression systems for delivery and access of a subset of a compressed 3d point cloud
WO2020059826A1 (fr) * 2018-09-21 2020-03-26 パナソニック インテレクチュアル プロパティ コーポレーション オブ アメリカ Procédé de codage de données tridimensionnelles, procédé de décodage de données tridimensionnelles, dispositif de codage de données tridimensionnelles et dispositif de décodage de données tridimensionnelles
WO2020190093A1 (fr) * 2019-03-20 2020-09-24 엘지전자 주식회사 Dispositif de transmission de données de nuage de points, procédé de transmission de données de nuage de points, dispositif de réception de données de nuage de points et procédé de réception de données de nuage de points
US20200304834A1 (en) * 2019-03-19 2020-09-24 Mediatek Singapore Pte. Ltd. Methods and apparatus for track derivation for immersive media data tracks

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US20190318488A1 (en) * 2018-04-12 2019-10-17 Samsung Electronics Co., Ltd. 3d point cloud compression systems for delivery and access of a subset of a compressed 3d point cloud
WO2020059826A1 (fr) * 2018-09-21 2020-03-26 パナソニック インテレクチュアル プロパティ コーポレーション オブ アメリカ Procédé de codage de données tridimensionnelles, procédé de décodage de données tridimensionnelles, dispositif de codage de données tridimensionnelles et dispositif de décodage de données tridimensionnelles
US20200304834A1 (en) * 2019-03-19 2020-09-24 Mediatek Singapore Pte. Ltd. Methods and apparatus for track derivation for immersive media data tracks
WO2020190093A1 (fr) * 2019-03-20 2020-09-24 엘지전자 주식회사 Dispositif de transmission de données de nuage de points, procédé de transmission de données de nuage de points, dispositif de réception de données de nuage de points et procédé de réception de données de nuage de points

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