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3D Point Cloud Attribute Compression Based On Geometric Transformation Prediction

With the rapid development of 3D point clouds, it can better represent the surface of objects in real life. However, the data volume of point cloud is huge, which requires an effective compression algorithm to compress the point cloud. In this paper, we propose a point cloud attribute compression method based on bidirectional prediction and color weighted graph transformation (CWGT). First, the k-d tree is designed for the partition processing of the point clouds. This hierarchical representation provides roughly the same number of levels for each leaf node. Second, we introduce a block-based bidirectional intra prediction scheme, which proposes a reasonable prediction model to eliminate spatial redundancy. Finally, in order to adapt to the irregularity of the point cloud, we apply graph transformation to deal with the prediction residual of the transform block, where the edge weight of the graph is weighted by the color attribute and the geometric space distance. The experimental results show that the average bit rate of this scheme is reduced by about 24.58% on the upper body dataset. In terms of visual quality, it can effectively improve the fineness of point cloud model compression reconstruction.