Efficient Dynamic Point Cloud Compression Through Adaptive Hierarchical Partitioning

Abstract

Video-based Point Cloud Compression (V-PCC) is a dynamic point cloud coding standard that compresses 3D point clouds by projecting them into 2D frames, involving computationally intensive steps that can hinder real-time applications in augmented and virtual reality. Several studies have endeavoured to mitigate this challenge by employing voxel classification and adaptive selection of voxels, yet the computationally intensive patch generation and refinement procedures persist. This paper presents an innovative approach that utilizes hierarchical hexahedron partitioning, adapting to varying point cloud densities and non-planar surfaces based on transmission requirements. The aim is to enhance projection by directly converting 3D data into 2D frames, bypassing segmentation and refinement. Crucially, the proposed method improves data capture by fine-tuning the projection layer of 2D frames, surpassing the capabilities of V-PCC’s projection layer, and effectively addressing potential patch-boundary discontinuities. Furthermore, this approach enhances the efficiency of 2D frames by producing smaller maps than V-PCC. Experimental results demonstrate the efficacy of the proposed method by significantly reducing patch generation time in V-PCC while preserving quality and achieving significant bitrate savings. By employing the proposed method, it is achievable to attain approximately a 7.32% improvement in geometry BD-Rate (D1) and 6.61% (D2), along with a notable reduction in time complexity ranging from 33% to 59% based on high/low bitrate scenarios compared to the V-PCC anchor. This research thus tackles the pressing challenges of V-PCC by proposing a promising solution for the real-time application of dynamic point cloud compression.

Keywords

• 3D compression
• complexity
• dynamic point cloud compression
• hexahedron
• partitioning