# Motion Estimation Techniques for Volumetric Video Attribute Compression

arXiv: https://arxiv.org/abs/2607.03576 · HTML: https://mlsanity.com/paper/2607.03576/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 34 references that are in the map.
- First posted on arXiv 3 Jul 2026.

## Builds on (most cited first, 7 of 7)

- [Graph-based compression of dynamic 3D point cloud sequences](https://mlsanity.com/paper/1506.06096/) (2015)
- [Predictive Generalized Graph Fourier Transform for Attribute Compression of Dynamic Point Clouds](https://mlsanity.com/paper/1908.01970/) (2019)
- [Region adaptive graph fourier transform for 3d point clouds](https://mlsanity.com/paper/2003.01866/) (2020)
- [Recent Advances on HEVC Inter-frame Coding: From Optimization to Implementation and Beyond](https://mlsanity.com/paper/1910.09770/) (2019)
- [A 3D Motion Vector Database for Dynamic Point Clouds](https://mlsanity.com/paper/2008.08438/) (2020)
- [Cylindrical coordinates for LiDAR point cloud compression](https://mlsanity.com/paper/2106.11237/) (2021)
- [U-Motion: Learned Point Cloud Video Compression with U-Structured Temporal Context Generation](https://mlsanity.com/paper/2411.14501/) (2024)

## Abstract (arXiv)

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.