Semantic-Aware Visual Decomposition for Point Cloud Geometry Compression

Abstract

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.