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.

With the proliferation of sensor devices, the extensive utilization of three-dimensional data in multimedia continues to grow. Point clouds are widely adopted within this domain because they are one of the most intuitive representations of three-dimensional data. However, the substantial volume of point cloud data poses significant challenges for storage and transmission. Moreover, a considerable portion of the data loses its semantic information during transmission. Consequently, how can we ensure both the perceptual quality for the human and the performance of downstream tasks during the transmission? To address this issue, we propose a scalable point cloud geometry compression framework (SPCGC) for machine perception. This framework tackles the fidelity issues associated with point cloud compression and preserves more semantic information, enhancing the performance of machine vision tasks. Our solution consists of a base layer bitstream and an enhancement layer bitstream. The base layer bitstream contains geometry data, while the enhancement layer bitstream utilizes semantic-guided residual data. Additionally, we introduce two modules for extracting and coding residual features. And incorporate classification and segmentation losses from downstream tasks into the Rate-Distortion (RD) optimization. Our approach outperforms existing learning-based lossy point cloud coding methods through empirical validation in downstream tasks without sacrificing point cloud compression performance.