PVContext: Hybrid Context Model for Point Cloud Compression

TL;DR

PVContext introduces a hybrid context model combining local voxel and global point information to significantly improve octree-based point cloud compression efficiency, reducing bitrate substantially.

Contribution

The paper presents PVContext, a novel hybrid context model that integrates voxel and point contexts for enhanced point cloud compression.

Findings

Reduces bitrate by 37.95% on SemanticKITTI data

Achieves 48.98% bitrate reduction on MPEG 8i point clouds

Improves compression efficiency over existing methods

Abstract

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.