Density-Based Geometry Compression for LiDAR Point Clouds

LiDAR (Light Detection and Ranging) sensors produce 3D point clouds that capture the surroundings, and these data are used in applications such as autonomous driving, tra � c monitoring, and remote surveys. LiDAR point clouds are usually compressed for e � cient transmission and storage. However, to achieve a high compression ratio, existing work often sacri � ces the geometric accuracy of the data, which hurts the e � ectiveness of downstream applications. Therefore, we propose a system that achieves a high compression ratio while preserving geometric accuracy. In our method, we � rst perform density-based clustering to distinguish the dense points from the sparse ones, because they are suitable for di � erent compression methods. The clustering algorithm is optimized for our purpose and its parameter values are set to preserve accuracy. We then compress the dense points with an octree, and organize the sparse ones into polylines to reduce the redundancy. We further propose to compress the sparse points on the polylines by their spherical coordinates considering the properties of both the LiDAR sensors and the real-world scenes. Finally, we design suitable schemes to compress the remaining sparse points not on any polyline. Experimental results on DBGC, our prototype system, show that our scheme compressed large-scale real-world datasets by up to 19 times with an error bound under 0.02 meters for scenes of thousands of cubic meters. This result, together with the fast compression speed of DBGC, demonstrates the online compression of LiDAR data with high accuracy. Our source code is publicly available at https://github.com/RapidsAtHKUST/DBGC.