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.

Focusing on encoding the Region of Interest (ROI) in point clouds and allocating more bitstream is a crucial area of research. In processing point cloud data, the foreground ROI region typically contains critical information, making it essential for applications like autonomous driving and robot navigation. However, previous point cloud compression methods often treat the entire point cloud uniformly and fail to fully harness the significance of the ROI. This study is dedicated to preserving vital information in point cloud by optimizing the Point Cloud Compression (PCC) process and allocating more bitstream to the foreground ROI region. To achieve this goal, we introduce a Semantic-Aware Visual Decomposition Point Cloud Geometry Compression (SAVD-PCGC) strategy. It involves the initial identification of foreground and background regions, followed by allocating of additional bitstream resources to machine vision critical areas by controlling compression model parameters. We also propose corresponding compensation methods to reduce distortion loss in compression. This separation of foreground and background coding strategy aims to maintain compression performance while ensuring high-quality of the ROI region, thereby improving the performance of downstream tasks. Experimental results demonstrate that our approach significantly enhances the performance of point cloud object detection compared to traditional PCC methods.