Octree-Based Learned Point Cloud Geometry Compression: A Lossy Perspective

In this paper, we mainly research lossy octree-based point cloud geometry compression. We analyze data characteristics of different point clouds and propose lossy approaches specifically (Fig. 1 (d-f)). For object point clouds that suffer from quantization step adjustment, we propose a new leaf nodes lossy compression method (Fig. 1 (a-b)), which achieves lossy compression by performing bit-wise coding and binary prediction on leaf nodes. For LiDAR point clouds, we discover the occupancy distribution similarity for octrees in the same depth. Therefore, we present variable rate approaches and propose a simple but effective rate control method. Experimental results demonstrate that the proposed leaf nodes lossy compression method significantly outperforms the previous octree-based method on object point clouds, and the proposed rate control method achieves about 1% bit error without finetuning on LiDAR point clouds.