A Novel Grid-Based Geometry Compression Framework for Spinning Lidar Point Clouds

Point clouds captured by Light Detection And Ranging (Li-DAR) devices have played a significant role in autonomous driving and high-precision mapping. The massive amount of point cloud data, however, challenges the capacity of current data storage and transmission networks, which confines the development of LiDAR point cloud applications. To alleviate this situation, a novel grid-based geometry compression framework dedicated to spinning LiDAR point cloud is proposed in this paper. Firstly, a 2D grid-based point cloud representation is built taking advantage of the LiDAR acquisition pattern. Then, a projection is performed to effectively represent the 3D geometry as multiple 2D geometry components. Finally, dedicated prediction and entropy coding methods are designed for each 2D geometry component according to its characteristics. Experimental results show that the proposed method outperforms MPEG G-PCC with an average gain of 18.81% and 8.03% for lossy and lossless coding respectively.