  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. Compared with other voxel-based or image-based compression
methods, our method can keep and align all points from the original point cloud
in the reconstructed point cloud. It can also control the maximum
reconstruction error for each point through a quantization module. In the
experiments, we prove that our easier FPS-based segmentation method can achieve
better performance than instance-based segmentation methods such as DBSCAN. To
verify the advantages of our proposed method, we evaluate the reconstruction
quality and fidelity for 3D object detection and SLAM, as the downstream tasks.
The experimental results show that our elegant framework can achieve 30$\times$
compression ratio without affecting downstream tasks, 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 is efficient and effective enough to act as a baseline for
range image-based point cloud compression. The code is available on
https://github.com/StevenWang30/R-PCC.git.
