2024/09/19 by Guoqing Zhang, Zhang, Guoqing, Wenbo Zhao +9
Earth and Planetary Sciences · Engineering · Environmental Science · #3D Modeling in Geospatial Applications #3D Surveying and Cultural Heritage #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Remote Sensing and LiDAR Applications #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2409.12724
openalex publication_date 2024/09/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Efficient storage of large-scale point cloud data has become increasingly challenging due to advancements in scanning technology. Recent deep learning techniques have revolutionized this field; However, most existing approaches rely on single-modality contexts, such as octree nodes or voxel occupancy, limiting their ability to capture information across large regions. In this paper, we propose PVContext, a hybrid context model for effective octree-based point cloud compression. PVContext comprises two components with distinct modalities: the Voxel Context, which accurately represents local geometric information using voxels, and the Point Context, which efficiently preserves global shape information from point clouds. By integrating these two contexts, we retain detailed information across large areas while controlling the context size. The combined context is then fed into a deep entropy model to accurately predict occupancy. Experimental results demonstrate that, compared to G-PCC, our method reduces the bitrate by 37.95% on SemanticKITTI LiDAR point clouds and by 48.98% and 36.36% on dense object point clouds from MPEG 8i and MVUB, respectively.