Rate-Distortion Optimized Geometry Compression for Spinning LiDAR Point Cloud

Point cloud is a major representation format of 3D objects and scenes. It has been increasingly applied in various applications due to the rapid advances in 3D sensing and rendering technologies. In the field of autonomous driving, point clouds captured by spinning Light Detection And Ranging (LiDAR) devices have become an informative data source for road environment perception and intelligent vehicle control. On the other hand, the massive data volume of point clouds also brings huge challenges to point cloud transmission and storage. Therefore, establishing compression frameworks and algorithms that conform to the characteristics of point cloud data has become an important research topic for both academia and industry. In this paper, a geometry compression method dedicated to spinning LiDAR point cloud was proposed taking advantage of the prior information of the LiDAR acquisition procedure. Rate-distortion optimizations were further integrated into the coding pipeline according to the characteristics of the prediction residuals. Experimental results obtained on different datasets show that the proposed method consistently outperforms the state-of-the-art G-PCC predictive geometry coding method with reduced runtime at both the encoder and decoder sides.