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    • 5. 发明公开
    • POINT CLOUD ATTRIBUTE ENCODING AND DECODING METHOD AND DEVICE
    • EP4199520A1
    • 2023-06-21
    • EP21857586.8
    • 2021-08-13
    • Zhejiang University
    • YU, LuCHEN, JiafengWANG, Wenyi
    • H04N19/62
    • Provided are a novel processing order-based point cloud attribute encoding and decoding method and device, addressing the problem of periodic discontinuity of three-dimensional spatial distances between adjacent points in the Morton order, so as to further improve encoding efficiency of point cloud attributes. The Hilbert order is used as an encoding order of point cloud attributes. In the Hilbert order, several points preceding a current point are searched so as to find nearest neighbor points of the current point in a three-dimensional space, and the nearest neighbor points are used as prediction points of the current point. Since near points in the Hilbert order are also near in the three-dimensional space, even in the worst case, two points having a distance of n in the Hilbert order have a distance of less than or equal to n 1/2 in the three-dimensional space. However, a distance of two points having a distance of n in the Morton order could have a distance that is greater than a maximum length of a point cloud dimension in the three-dimensional space. In addition, for a small K, the average distance from the current point for K points that precede the current point is less when using the Hilbert order than when using the Morton order . Therefore, the Hilbert order can be used to increase the probability of finding nearest neighbor points of the current point in the three-dimensional space, such that attribute values of the prediction points are closely related to an attribute value of the current point, thereby improving encoding efficiency of point cloud attributes.