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Fast Inter-frame Motion Prediction for Compressed Dynamic Point Cloud Attribute Enhancement
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Proposes DAE-MP, a multi-frame deep attribute enhancement method for compressed dynamic point clouds that uses a supervised fast inter-frame motion prediction module (IFMP) with a motion consistency loss, plus a single-frame model DAE-S.
Problem
Quality enhancement of attributes in compressed dynamic point clouds, where existing deep learning methods handle only single static frames and inter-frame motion between point cloud frames is hard to establish.
Approach
An explicit, supervised inter-frame motion prediction module that estimates per-point geometry offsets (trained with Chamfer distance and a motion consistency loss) to align reference-frame features with the target frame, combined with a frequency component perception module that adaptively separates and fuses multi-frequency features.
Key results
- DAE-MP reaches up to 0.52 dB PSNR gain on Loot at QP51, versus 0.32 dB for MS-GAT.
- IFMP reduces geometry Chamfer distance on Redandblack from 30.30 to 3.51 at QP51.
- DAE-MP has 3.77M / 3.29M parameters and faster end-to-end processing than MS-GAT, which processes spatial blocks.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use DAE-MP instead of MS-GAT | compressed dynamic point cloud attribute enhancement · Loot, QP 51 | ΔPSNR (dB): 0.32 -> 0.52 | Helped |
| use DAE-MP instead of MS-GAT | compressed dynamic point cloud attribute enhancement · Redandblack, QP 51 | ΔPSNR (dB): 0.20 -> 0.35 | Helped |
| use DAE-MP instead of MS-GAT | compressed dynamic point cloud attribute enhancement · Loot, QP 46 | ΔPSNR (dB): 0.34 -> 0.45 | Helped |
| use DAE-MP instead of MS-GAT | compressed dynamic point cloud attribute enhancement · Redandblack, QP 46 | ΔPSNR (dB): 0.26 -> 0.39 | Helped |
| add explicit IFMP to DAE-M (ablation) | DAE-M · Loot, QP 51 | ΔPSNR (dB): 0.41 -> 0.52 | Helped |
| add explicit IFMP to DAE-M (ablation) | DAE-M · Redandblack, QP 51 | ΔPSNR (dB): 0.27 -> 0.35 | Helped |
| add FCP (ablation) | DAE-MP · Loot, QP 51 | ΔPSNR (dB): 0.44 -> 0.52 | Helped |
| add FCP (ablation) | DAE-MP · Redandblack, QP 51 | ΔPSNR (dB): 0.29 -> 0.35 | Helped |
| add relative positional embedding (RPE) (ablation) | IFMP · Loot, QP 46 | CD: 3.26 -> 2.83 | Helped |
| add relative positional embedding (RPE) (ablation) | IFMP · Redandblack, QP 46 | CD: 4.89 -> 3.67 | Helped |
| add motion consistency loss (MCL) (ablation) | IFMP · Loot, QP 46 | CD: 5.01 -> 2.83 | Helped |
| add motion consistency loss (MCL) (ablation) | IFMP · Redandblack, QP 46 | CD: 7.26 -> 3.67 | Helped |
Surprising findings
- Removing the explicit motion prediction (DAE-M, implicit frame concatenation) causes a significant performance drop, suggesting distorted attributes hinder implicit inter-frame fusion.
Limitations
- Evaluated only on 8iVFB with Octree lossless geometry and RAHT lossy attribute mode.
- Chamfer-based supervision leaves outlier motion offsets that require an additional consistency loss.
- Only N=1 reference frames used; two-stage training required.
Experimental setup
Datasets: 8iVFB
Benchmarks: 8iVFB
Metrics: PSNR gain (dB) Chamfer distance standard deviation of PSNR inference time
Baselines: MS-GAT DAE-S DAE-M G-PCC
Builds on: D-DPCC Minkowski Engine Inception-ResNet U-Net
Closest papers by meaning
- patchDPCC: A Patchwise Deep Compression Framework for Dynamic Point Clouds (AAAI 2024 · similarity 0.86)
- AdaDPCC: Adaptive Rate Control and Rate-Distortion-Complexity Optimization for Dynamic Point Cloud Compression (AAAI 2025 · similarity 0.85)
- Adapting Pre-trained 3D Models for Point Cloud Video Understanding via Cross-frame Spatio-temporal Perception (CVPR 2025 · similarity 0.83)
- PvNeXt: Rethinking Network Design and Temporal Motion for Point Cloud Video Recognition (ICLR 2025 · similarity 0.83)
- SPU-IMR: Self-supervised Arbitrary-scale Point Cloud Upsampling via Iterative Mask-recovery Network (AAAI 2025 · similarity 0.83)
- DAPoinTr: Domain Adaptive Point Transformer for Point Cloud Completion (AAAI 2025 · similarity 0.82)
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