Improved Geometry Coding for Spinning LiDAR Point Cloud Compression

Point cloud compression has emerged as a hot research topic in recent years. Due to applications such as autonomous driving, LiDAR point cloud compression is an important research aspect of this field. Moving Picture Experts Group (MPEG) is developing a standard called Geometry-based Point Cloud Compression (G-PCC) to meet the compression requirements of point clouds from different collection devices including LiDAR. In current G-PCC, the prior information of spinning LiDAR is not fully utilized in octree geometry coding. In this paper, we address this issue and effectively account for the prior information of spinning LiDAR to improve the compression efficiency of octree geometry coding. Specifically, the angle information provided by capture laser scanner is utilized for Inferred Direct Coding Mode (IDCM) eligibility criterion and z coordinate compensation of the reconstructed point cloud. Experimental results demonstrate that the proposed method achieves 6.7% and 16.4% average coding gain under D1 and D2 quality metrics, respectively, with a negligible increase in complexity. The major part of the proposed method has been adopted in G-PCC.