New motion estimation cuts point cloud video bitrate by 55%
Researchers achieve 55.3% bitrate savings over G-PCC using graph-based motion estimation
Deep Dive
Haoran Hong and team propose a geometry-based inter-coding scheme for dynamic point cloud attribute compression. Their method uses graph-based motion estimation and interpolation-free fractional-voxel refinement. On MPEG datasets, it achieves average bitrate savings of 55.3% over G-PCC, 42.3% over GeS-TM, and 16.5% over V-PCC under lossy geometry conditions.
Key Points
- Proposes first motion-based inter-coding scheme specifically for point cloud attribute compression (color, reflectance)
- Introduces graph-based motion estimation and interpolation-free fractional-voxel refinement to improve temporal redundancy handling
- Achieves 55.3% average bitrate savings over G-PCC, 42.3% over GeS-TM, and 16.5% over V-PCC on MPEG datasets
Why It Matters
Enables more bandwidth-efficient volumetric video, accelerating adoption of high-quality AR/VR and immersive streaming.