R-PCC: A Baseline for Range Image-based Point Cloud Compression

In autonomous vehicles or robots, point clouds from LiDAR can provide accurate depth information of objects compared with 2D images, but they also suffer a large volume of data, which is inconvenient for data storage or transmission. In this paper, we propose a Range image-based Point Cloud Compression method, R-PCC, which can reconstruct the point cloud with uniform or non-uniform accuracy loss. We segment the original large-scale point cloud into small and compact regions for spatial redundancy and salient region classification. Our range image-based method can keep and align all points from the original point cloud in the reconstructed point cloud, and the setting of the quantization module restricts the maximum reconstruction error. In the experiments, we prove that our easier FPS-based segmentation method can achieve better performance than instance-based segmentation methods such as DBSCAN, and our non-uniform compression framework shows a great improvement on the downstream tasks compared with the state-of-the-art large-scale point cloud compression methods. Our real-time method can achieve 40 × compression ratio without affecting downstream tasks, to act as a baseline for range image-based point cloud compression. The code is available on https://github.com/StevenWang30/R-PCC.git.