Motion Estimation Techniques for Volumetric Video Attribute Compression

2026-07-03

AI Summary

This work addresses the challenge of inefficient temporal redundancy removal in dynamic point cloud attribute compression and the limited support of existing motion estimation methods for attribute coding. To overcome these limitations, the authors propose a geometry-guided inter-frame coding framework that, for the first time, integrates geometric information with graph signal processing. They design a graph-structured motion estimation algorithm and introduce a sub-voxel-level motion compensation mechanism that operates without interpolation. Implemented within the G-PCC framework, the proposed method significantly improves compression efficiency. Experimental results on MPEG standard datasets demonstrate substantial bitrate savings under lossy geometry conditions, achieving average reductions of 55.3%, 42.3%, and 16.5% compared to G-PCC, GeS-TM, and V-PCC, respectively.

Abstract

Point cloud compression relies on techniques to compress both geometry and attributes. Motion-based approaches for dynamic solid point cloud geometry compression within the geometry-based point cloud compression (G-PCC) framework have achieved significant reductions in geometry rate. However, motion-based techniques for attribute compression remain underexplored, making it challenging to achieve significant reductions in the temporal redundancy of attributes. Firstly, this paper proposes a geometry-based inter-coding scheme to compress the attributes of dynamic solid point clouds. Secondly, a graph-based motion-estimation scheme for point-cloud attribute compression is proposed. Thirdly, an interpolation-free fractional-voxel motion estimation method is proposed to refine motion accuracy to fractional-voxel precision. Our experimental results on the MPEG point cloud dataset show that the proposed scheme outperforms G-PCC, GeS-TM, and V-PCC in lossless and lossy geometry conditions. We achieve average bitrate savings of $55.3\%$, $42.3\%$, and $16.5\%$ over G-PCC, GeS-TM, and V-PCC, respectively, under lossy-geometry conditions.

Problem

• point cloud compression
• attribute compression
• motion estimation
• temporal redundancy
• volumetric video

Innovation

• motion estimation
• point cloud attribute compression
• fractional-voxel precision
• graph-based coding
• inter-frame coding