WO2024043406A1 - Système et procédé de compression de nuage de points pour diffusion en continu - Google Patents

Système et procédé de compression de nuage de points pour diffusion en continu Download PDF

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Publication number
WO2024043406A1
WO2024043406A1 PCT/KR2022/019640 KR2022019640W WO2024043406A1 WO 2024043406 A1 WO2024043406 A1 WO 2024043406A1 KR 2022019640 W KR2022019640 W KR 2022019640W WO 2024043406 A1 WO2024043406 A1 WO 2024043406A1
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WIPO (PCT)
Prior art keywords
point
points
normal vector
reference point
distance
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PCT/KR2022/019640
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English (en)
Korean (ko)
Inventor
박우출
장준환
양진욱
최민수
이준석
구본재
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한국전자기술연구원
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    • 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
    • H04N19/42—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals characterised by implementation details or hardware specially adapted for video compression or decompression, e.g. dedicated software implementation
    • H04N19/423—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals characterised by implementation details or hardware specially adapted for video compression or decompression, e.g. dedicated software implementation characterised by memory arrangements
    • 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
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
    • H04N21/23—Processing of content or additional data; Elementary server operations; Server middleware
    • H04N21/234—Processing of video elementary streams, e.g. splicing of video streams or manipulating encoded video stream scene graphs
    • H04N21/2343—Processing of video elementary streams, e.g. splicing of video streams or manipulating encoded video stream scene graphs involving reformatting operations of video signals for distribution or compliance with end-user requests or end-user device requirements

Definitions

  • the present invention relates to a point cloud compression system and method for streaming, and more specifically, to encoding points where the dot product of a normal vector generated from a spherical image of produced content and a direction vector of the user's field of view obtained by a camera is a positive number. It is about a technology that allows this to be done.
  • VR content must be rendered in a spherical shape, but conversion and transmission of spherical images have the problem of not conforming to existing video conversion formats or broadcast transmission formats.
  • the present applicant converted the spherical point cloud of the produced content into a hexahedron and then converted it into points in a tree structure, and in the converted tree structure, the reference point of the first node and the second node and lower child nodes that are child nodes of the first node.
  • the data is encoded by deriving the normal vectors of the points where the calculated distance between the points is less than the standard distance, and encoding the points where the dot product of the normal vector of the derived point and the user's viewing direction vector obtained through the camera is a positive number.
  • the present invention can reduce the number of data to be encoded, reduce the computational complexity and speed of normal vector calculation, and fundamentally secure communication resources accordingly.
  • a point cloud compression system for streaming according to an embodiment of the present invention to achieve the above-described object,
  • a point structuring device that converts the spherical point cloud of the produced content into hexahedral points and then converts them into points in a tree structure
  • a point normal vector estimation device for deriving a normal vector for each point based on a calculation distance and a predetermined reference distance between each point of the tree structure and the points of the child nodes of each point and storing the derived normal vector of the point in a temporary memory;
  • One feature includes an encoding device that extracts points to be encoded using an inner product of the normal vector of each point and the direction vector of the user's field of view obtained by a camera, encodes the extracted points, and transmits them.
  • the point normal vector estimation device is:
  • Each point in the tree structure is set as a reference point, the calculation distance between the point of the child node of each set reference point and the set reference point is output, and the reference points whose output calculation distance is less than the predetermined reference distance are stored in temporary memory.
  • a point sorting unit that sorts the reference points stored in the temporary memory in ascending order with respect to the calculation distance;
  • Extract two adjacent points in the order of the calculated distance from the reference point derive the normal vector of the plane formed by the two extracted adjacent points and the reference point, and set the derived normal vector to the location of the temporary memory where the reference points are arranged. It may include a normal vector derivation unit stored in .
  • the point normal vector estimation device is
  • a normal vector optimization unit that extracts at least three points that have a short calculation distance from the reference point stored in the temporary memory, averages the normal vector of each extracted point, and then sets the average normal vector as the normal vector of the reference point. can do.
  • the encoding device Preferably, the encoding device:
  • One feature includes an encoding step of extracting points to be encoded using an inner product of the normal vector of each point and the direction vector of the user's field of view obtained by a camera, encoding the extracted points, and transmitting them.
  • the point normal vector estimation step is,
  • Extract two adjacent points in the order of the calculated distance from the reference point derive the normal vector of the plane formed by the two extracted adjacent points and the reference point, and set the derived normal vector to the location of the temporary memory where the reference points are arranged. It may include the step of saving.
  • the point normal vector estimation step is,
  • It may include extracting at least three points that have a short calculation distance from the reference point stored in temporary memory, averaging the normal vectors of each extracted point, and then setting the average normal vector as the normal vector of the reference point.
  • the encoding step is,
  • a spherical point cloud is converted to a hexahedral point and then converted to a tree structure point, each point of the converted tree structure is set as a reference point, each point of the child node of the set reference point and the above
  • the present invention by performing encoding on points where the inner product of the normal vector of the derived point and the user's viewing direction vector obtained through the camera is a positive number, the number of data to be encoded can be reduced, and the communication resources accordingly and memory resources can be secured.
  • FIG. 1 is a configuration diagram of a point cloud compression system for streaming according to an embodiment.
  • Figure 2 is an exemplary diagram showing the process of the point structuring device of Figure 1.
  • Figure 3 is a detailed configuration diagram of the point normal vector estimation device of Figure 2.
  • Figure 4 is an example diagram showing the point arrangement process of the temporary memory of Figure 3.
  • Figure 5 is a diagram showing the operation process of the point array unit of Figure 3.
  • Figure 6 is an example diagram showing the point arrangement of the temporary memory based on Figure 5.
  • Figure 7 is a conceptual diagram of the normal vector of the normal vector derivation part of Figure 3.
  • Figure 8 is a diagram showing the operation process of the normal vector derivation unit of Figure 3.
  • Figure 9 is a diagram showing the operation process of the normal vector optimization unit of Figure 4.
  • Figure 10 is a diagram showing the normal vector for each point optimized based on Figure 9.
  • Figure 11 is a diagram explaining the concept of extracting points for encoding according to an embodiment.
  • An embodiment may include any number of each component to which it applies, in any suitable configuration.
  • computer and communication systems come in a wide range of configurations, and the drawings do not limit the scope of the disclosure to any particular configuration.
  • drawings illustrate one operating environment in which the various features disclosed in this patent document may be used, such features may be used in any other suitable system.
  • the subject performing the operation converts each point of the spherical point cloud into a hexahedral point, structures it in the form of a tree, sets each point of the tree structure as a reference point, and then sets the corresponding reference point and It may be a processor that derives the normal vector of each point through a neighboring point search and normal vector estimation algorithm based on the calculation distance between each point of the child node, which is a child node of the node of the corresponding reference point, and a predetermined reference distance. As another example, As such, it may be a recording medium on which a program that performs estimation and processing is recorded or a device containing the same.
  • the adjacent point search and normal vector estimation algorithm is sequentially performed for all points in the tree structure based on the tree structure, where the reference point refers to a point set as a reference point and a point set as a reference point among the points in the tree structure. .
  • FIG. 1 is a diagram showing the configuration of a point cloud compression system for streaming according to an embodiment
  • FIG. 2 is an example diagram explaining the point structuring process of FIG. 1
  • FIG. 3 is a conceptual diagram of a normal vector of a point
  • FIG. 4 is a detailed configuration diagram of the point normal vector estimation device of FIG. 1
  • FIG. 5 is an example diagram showing the point arrangement of the temporary memory of FIG. 4.
  • the point cloud compression system for streaming in one embodiment converts each point of a spherical point cloud into a hexahedron and then structures it into a tree form, and combines a reference point of a set tree structure and sub-points of the reference point.
  • Normal vectors are derived for points where the calculation distance between each point of the child node is less than the standard distance, and encoding is performed on the extracted points as the inner product of the derived normal vector and the direction vector of the user's field of view obtained through the camera.
  • the system may include a point structuring device (1), a point normal vector estimation device (2), and an encoding device (3).
  • the point structuring device 1 maps each point of the point cloud to a cube-shaped hexahedron and then performs the function of structuring each point of the mapped hexahedron into a tree shape.
  • the series of processes for mapping each point of a spherical point cloud to each face of a cube-shaped hexahedron is not specifically specified, but this can be understood by those skilled in the art.
  • the point structuring device 1 structures each point of the hexahedron in a tree form. For example, referring to FIG. 2, the point structuring device 1 configures the first node with points 60, 70, and 60 located at the center of the X-axis of the cube as shown in (a), and then constructs the first node in (b) As shown, the point ((25, 50, 30), (50, 45, 50)) located at the center of the Y axis of each of the two areas divided by the X axis of the hexahedron is the second node as a child of the first node.
  • a node It constitutes a node (hereinafter referred to as a child node), and as shown in (c), a point ((10, 70, 50) located at the center of the Z axis of each of the four areas divided by the X and Y axes of the hexahedron
  • a child node is formed as a child node of the second node with (40, 25, 55), (65, 70, 60), (80, 30, 60)).
  • points (25, 50, 30) represent the point location of the point's X, Y, and Z axis coordinate system.
  • Each point in the tree structure is transmitted to the point normal vector estimation device (2).
  • the point normal vector estimation device (2) sequentially sets a reference point at each point of the tree structure, searches for adjacent points based on the calculated distance d, which is the distance difference from the set corresponding reference point, and uses the two searched adjacent points and the reference point. It is equipped with a configuration for deriving the normal vector of a reference point perpendicular to the formed plane.
  • the point normal vector estimation device 2 may include a point alignment unit 21, a normal vector derivation unit 22, a temporary memory 23, and a normal vector optimization unit 24. .
  • the point sorting unit 21 searches for points where the calculation distance between the reference point sequentially set as the point of each node of the point structuring device 1 and the point of the child node of the reference point is less than or equal to the set reference distance, and stores it in the temporary memory 23. Then, the points stored in the temporary memory 23 are sorted in ascending order with respect to the calculation distance.
  • the point alignment unit 21 calculates the distance between the point of the child node, which is a child node of the first node, and the reference point. , a point array is created with points whose calculation distance d is less than or equal to a predetermined standard distance.
  • the point of point index 32 is set as the corresponding reference point, as shown in (b), the point indices 2 and 83 of the child nodes that are lower nodes of point 32
  • the calculated distance d is derived by calculating the distance difference between and the set corresponding reference point 32.
  • the point alignment unit 21 calculates the distance between the corresponding reference point of point index 2 and each point of point indexes 93 and 7 of the child nodes of the reference point, and at this time, calculate The distance d is 3.1 and 2.4, respectively.
  • FIG. 5 is a diagram for explaining the operation of the point arrangement unit 21 of FIG. 3, and FIG. 6 is an exemplary diagram showing the point arrangement of the temporary memory 23 of FIG. 3.
  • the point arrangement unit 21 performs point arrangement in the temporary memory based on the distance between the reference node sequentially set as each point of the tree structure and each point of the child node, which is a lower node of the node to which the reference node belongs.
  • the point array unit 21 sets the point array and reference distance r of the temporary memory formed by the number of points of the predetermined point cloud in steps 110 and 120, and sets the point array of the set temporary memory. Insert the point of each node.
  • the reference distance r is a result obtained through multiple experiments and can be set to reflect the resolution of the playback content.
  • the point array unit 21 calculates the left point A L and the set reference distance between the set reference point of the tree structure and the left point A L among the child nodes that are lower nodes of the node to which the reference point belongs. Insert point A L into the point array in temporary memory.
  • the point array unit 21 inserts the left point into the left position of the temporary memory if the calculation distance is less than or equal to the reference distance (progressing in the “Yes” direction of the arrow).
  • step 132 determines the right side of the child node of the first node. Based on the calculated distance between points A and R and the set standard distance, the right point is inserted into the point array of temporary memory.
  • the point array unit 21 inserts the right point A R into the right position of the temporary memory when the calculation distance is less than or equal to the reference distance (proceeding in the “Yes” direction of the arrow).
  • the point array unit 21 determines whether the last adjacent point and the last node of the point tree structure have been reached, and if the last node has been reached (proceeding in the direction of the arrow “Yes”), it is stored in temporary memory. Sort in ascending order of the calculated distance between the saved point and the reference point.
  • the point array unit 21 proceeds in the direction of the arrow “No” in step 137, it repeatedly proceeds to step S31. Accordingly, the point array of the temporary memory is stored in the order of points A L , BL L , C L , D L , DR , C R , B R , and A R , as shown in FIG. 6 .
  • the point sorting unit 21 sorts the point array of the temporary memory 23 in order of calculation distance in step 138, and the point array of the temporary memory 23 is sorted in ascending order of calculation distance, Therefore, the temporary memory 23 stores points in order of the shortest calculation distance from the reference point.
  • the number of points stored in the temporary memory 23 may be reduced compared to the number of input points to points whose calculation distance is less than the reference distance. Accordingly, the computational complexity of the normal vector for each point, which will be described later, is reduced.
  • the normal vector derivation unit 22 of FIG. 3 derives a normal vector as a perpendicular vector to a plane consisting of two adjacent points and one reference point retrieved as the first and second points of the temporary memory 23.
  • FIG. 7 is a conceptual diagram of the normal vector of the normal vector derivation unit of FIG. 3.
  • the normal vector is a reference point for a plane formed by an arbitrary reference point A and adjacent points A' and A'' among points in the temporary memory. It is a vertical vector.
  • the normal vector can be derived using a CUDA (Compute Unified Device Architecture) parallel processing algorithm, and the normal vector derivation unit 22 sets the CUDA threshold of the CUDA kernel function for each point and calculates the set CUDA threshold and two adjacent points and A normal vector is derived from one reference point.
  • CUDA Computer Unified Device Architecture
  • the process of deriving a normal vector through a CUDA (Compute Unified Device Architecture) parallel processing algorithm with a set CUDA threshold, two adjacent points, and one reference point as input is not specifically specified in the specification, but this is It can be understood at the level of those skilled in the art.
  • CUDA Computer Unified Device Architecture
  • each point below the standard distance stored in the temporary memory 23 includes information such as point index, point position coordinates of the hexahedron, and derived normal vector. may be included.
  • FIG. 8 is a diagram showing the operation process of the normal vector derivation unit of FIG. 4.
  • the normal vector derivation unit 22 converts the first point and the second point of the temporary memory into adjacent points in steps 141 to 144. After extraction, a plane is created based on the two extracted adjacent points and the reference point, a normal vector perpendicular to the created plane is derived, and the derived normal vector is inserted into the corresponding reference point in the temporary memory 23.
  • the first and second points of the temporary memory 23 are adjacent points having a short distance from the reference point.
  • the normal vector derivation unit 22 proceeds to step 138.
  • the point normal vector estimation device 2 derives the average value of the normal vectors of at least three points in the temporary memory and then sets the derived average normal vector as the normal vector of the corresponding reference point. Accordingly, all points stored in the temporary memory 23 are normalized.
  • the normal vector optimization unit 24 of FIG. 3 extracts at least 3 adjacent points to the corresponding point in the temporary memory 23, averages the normal vectors of the extracted adjacent points, and calculates the average normal vector as the normal of the reference point. Set as vector.
  • points whose calculation distance from the reference point is less than or equal to a predetermined reference distance are sorted and stored in ascending order of the calculation distance, making it easy to search for adjacent points and reducing the number of searches for adjacent points. You can.
  • FIG. 9 is a diagram showing the operation process of the normal vector optimization unit of FIG. 4, and FIG. 10 is a comparison diagram showing the optimized normal vector and the normal vector of FIG. 8.
  • the unit 24 sets a selection method for adjacent points stored in the temporary memory and extracts adjacent points in the temporary memory according to the set adjacent point selection method. For example, if the adjacent point selection method is a method of extracting the first N points, the first to Nth points are extracted as adjacent points. As another example, if the adjacent point selection method is a point extraction method within a predetermined calculation distance, points within the determined calculation distance for the point are extracted as adjacent points.
  • the normal vector optimization unit 24 calculates the average of the sail vector of each extracted point in steps 213 and 214, sets the calculated average normal vector as the normal vector of the corresponding reference point, and stores it in temporary memory. After executing step (214), proceed to step (138).
  • the normal vector optimization unit 24 of one embodiment is an optimized normal vector compared to the normal vector derived based on FIG. 8.
  • the encoding device 3 of FIG. 1 compresses and transmits the point of the object location that matches the user's field of view obtained through a camera (not shown) using the derived normal vector.
  • the encoding device 3 encodes the point if the inner product value derived by dot producting the direction vector of the camera and the normal vector of the point of the content object is a positive number, and if the inner product value is a negative number, it determines the point to be out of the user's field of view. .
  • Figure 11 is an example diagram showing the encoding point for an object of produced content. As shown in Figure 10, it can be seen that the corresponding point present in the user's field of view obtained by a camera (not shown) is encoded. You can.
  • the computation complexity of the normal vector can be reduced by generating a normal vector of a point whose computation distance from the reference point of the tree structure is less than or equal to the reference distance among the point cloud of the object of the produced content. Encoding speed can be improved and versatility can be improved because it can be applied to lightweight devices.

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Abstract

La présente technologie divulgue un système et un procédé de compression de nuage de points pour diffusion en continu. Selon un exemple spécifique de la présente technologie, un nuage de points sphériques est converti en points hexaédriques, puis converti en points de structure arborescente, chaque point converti de la structure arborescente est défini comme point de référence, et un vecteur normal d'un point, dans lequel une distance de fonctionnement entre chaque point d'un nœud enfant du point de référence correspondant défini et du point de référence correspondant est inférieure ou égale à la distance de référence, est dérivé, de telle sorte que la complexité de calcul et la vitesse de calcul selon le calcul de vecteur normal peuvent être réduites, et en tant que codage, est effectué pour un point dans lequel la valeur interne du vecteur normal dérivé du point et un vecteur de direction de visualisation d'utilisateur obtenu par l'intermédiaire d'une caméra est un nombre positif, la quantité de données à coder peut être réduite, et des ressources de communication et des ressources de mémoire peuvent être sécurisées en conséquence.
PCT/KR2022/019640 2022-08-22 2022-12-05 Système et procédé de compression de nuage de points pour diffusion en continu WO2024043406A1 (fr)

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KR101820359B1 (ko) 2016-10-21 2018-01-19 서울과학기술대학교 산학협력단 360도 영상 전송 방법 및 장치

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160027208A1 (en) * 2014-07-25 2016-01-28 Kabushiki Kaisha Toshiba Image analysis method
US20190197767A1 (en) * 2017-12-26 2019-06-27 Htc Corporation Surface extrction method, apparatus, and non-transitory computer readable storage medium thereof
CN109409437A (zh) * 2018-11-06 2019-03-01 安徽农业大学 一种点云分割方法、装置、计算机可读存储介质及终端
KR102277098B1 (ko) * 2020-02-25 2021-07-15 광운대학교 산학협력단 포인트 클라우드 및 메쉬를 이용한 체적형 홀로그램 생성 방법
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