Dynamic Point Cloud Compression with Cross-Sectional Approach

A dynamic point cloud (DPC) is a set of points irregularly sampled from the continuous surfaces of objects or scenes, comprising texture (i.e., colour) and geometry (i.e., coordinate data). The DPC has made it possible to closely mimic the real world's natural reality and significantly improve training, safety, entertainment, and quality of life. However, to be even more effective, more realistic, and broadcast successfully, the dynamic point clouds require higher compression due to their massive volume of data compared to the traditional video. Recently, MPEG finalized a Video-based Point Cloud Compression (V-PCC) standard as the latest method of compressing both geometric and texture dynamic point clouds, which has achieved the best rate-distortion performance for DPC so far. However, V-PCC requires huge computational time due to expensive normal calculation and segmentation, sacrifices some points to limit the number of 2D patches, and cannot occupy all spaces in the 2D frame, resulting in the inefficiency of video compression. The proposed method addresses these limitations using a novel cross-sectional approach to cut the whole DPC frame into different sections considering the main view, shape, and size. This approach reduces expensive normal estimation and segmentation, retains more points, and utilizes more space for 2D frame generation, leading to more compression compared to the VPCC. The experimental results using standard video sequences show that the proposed technique can achieve better compression in both geometric and texture data compared to the latest V-PCC standard.