A Fast Motion Estimation Method With Hamming Distance for LiDAR Point Cloud Compression

Abstract

With more three-dimensional space information, Light detection and ranging (LiDAR) point clouds, which are promising to play more roles in the future, have an urgent need to be efficiently compressed. There are lots of compression methods based on spatial correlations, whereas few studies consider exploiting temporal correlations. In this paper, we propose a different perspective for the motion estimation. In most previous works, geometric distance between matching points was used as the criterion, which has an expensive computational cost and is not accurate. We first propose the Hamming distance between the octree's nodes, instead of the geometric distance between per point which is a more direct criterion. We have implemented our method in the MPEG (Moving Picture Expert Group) Geometry-based PCC (Point Cloud Compression) inter-exploration (G-PCC Inter-EM). Experimental results show our method can provide the average 3.5 % bitrate savings and 92.5 % encoding speed increase in lossless geometric coding, compared to the G-PCC Inter-EM.