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4DGCPro: Efficient Hierarchical 4D Gaussian Compression for Progressive Volumetric Video Streaming
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Proposes 4DGCPro, a hierarchical 4D Gaussian compression framework that yields multiple bitrates from a single model and streams progressively via H.264 2D-codec bitstreams with real-time mobile decoding.
Problem
Volumetric video streaming needs quality and bitrate flexibility within one model plus real-time decoding and rendering on lightweight mobile devices, which existing dynamic NeRF and 3DGS compression methods lack.
Approach
A significance metric (opacity plus Gaussian volume) partitions keyframe Gaussians into hierarchical layers; inter-frames use rigid transformation plus residual deformation with motion-aware adaptive grouping; layer-wise rate-distortion supervision and attribute-specific entropy models (KDE for keyframes, Gaussian for residuals) are trained end-to-end, and quantized layers are packed into 2D singl
Key results
- On the 4DGCPro dataset, Ours(High) reaches 29.47 dB PSNR and 0.963 SSIM at 1.31 MB, versus V^3 at 28.11 dB and 1.60 MB.
- BD-PSNR over ReRF is 4.20 dB on 4DGCPro, 7.87 dB on HiFi4G, and 2.07 dB on N3DV, exceeding HPC (3.42 dB on 4DGCPro) and V^3 (1.90 dB).
- Medium-quality decoding takes 19 ms and rendering 2.5 ms on desktop, and the full mobile pipeline runs in 34-43 ms per frame across quality levels.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use 4DGCPro instead of V³ | 4DGCPro · 4DGCPro | PSNR (dB): 28.11 -> 29.47 | Helped |
| use 4DGCPro instead of V³ | 4DGCPro · HiFi4G | PSNR (dB): 36.26 -> 36.38 | Helped |
| use 4DGCPro instead of 4DGC | 4DGCPro · N3DV | PSNR (dB): 31.58 -> 31.64 | Helped |
| combine opacity and volume additively with the significance weight rather than omit either factor or multiply them (ablation) | 4DGCPro low quality · 4DGCPro low quality | PSNR (dB): 26.71 -> 27.69; 25.83 -> 27.69; 26.36 -> 27.69 | Helped |
| set λΨ to 1×10^5 rather than 2×10^5 or 5×10^4 (ablation) | 4DGCPro low quality · 4DGCPro low quality | PSNR (dB): 27.51 -> 27.69; 27.57 -> 27.69 | Helped |
| use motion-aware adaptive Gaussian grouping rather than fixed group lengths (ablation) | 4DGCPro · 4DGCPro | BD-PSNR (dB): -0.96 to -0.25 | Helped |
| use 6 Gaussian layers rather than 4 (ablation) | 4DGCPro · 4DGCPro | BD-PSNR (dB): -0.87 -> 0 | Helped |
| increase the hierarchy from 6 to 7 or 8 Gaussian layers (ablation) | 4DGCPro · 4DGCPro | BD-PSNR (dB): 0.06; 0.09 | Helped |
| include rate-entropy modeling (ablation) | 4DGCPro · 4DGCPro | BD-PSNR (dB): -0.60 -> 0 | Helped |
| include hierarchical supervision (ablation) | 4DGCPro · 4DGCPro | BD-PSNR (dB): -2.89 -> 0 | Helped |
| include simulated quantization (ablation) | 4DGCPro · 4DGCPro | BDBR (%): 4.36% increase | Helped |
| use KDE plus Gaussian entropy modeling rather than KDE alone (ablation) | 4DGCPro · 4DGCPro | BD-PSNR (dB): -0.02 | No clear effect |
Surprising findings
- Removing layer-wise supervision yields higher High-level PSNR (29.53 dB) than the full model (29.47 dB), but much worse Mid and Low levels.
- Only KDE entropy estimation achieves similar performance to the full model while lengthening training, and the pure Gaussian assumption causes training failures.
Limitations
- Lengthy Gaussian optimization time (several minutes) due to hierarchical supervision and repeated rendering passes.
- Requires multi-view video input and struggles with sparse-view reconstruction.
- Underperforms on spatially extensive scenes.
Experimental setup
Datasets: N3DV HiFi4G 4DGCPro
Benchmarks: 4DGCPro dataset HiFi4G N3DV
Metrics: PSNR SSIM model size (MB) BD-PSNR BDBR encode/decode/render time
Baselines: ReRF HPC 3DGStream 4DGC HiCoM V^3
Builds on: 3DGS V^3 4DGC 3DGStream NeuS2
Closest papers by meaning
- 4DGC: Rate-Aware 4D Gaussian Compression for Efficient Streamable Free-Viewpoint Video (CVPR 2025 · similarity 0.90)
- Temporal Smoothness-Aware Rate-Distortion Optimized 4D Gaussian Splatting (NeurIPS 2025 · similarity 0.89)
- GIFStream: 4D Gaussian-based Immersive Video with Feature Stream (CVPR 2025 · similarity 0.88)
- Gaussian Splatting-based Volumetric Video Compression with Sparse 4D Anchors (NeurIPS 2026 · similarity 0.87)
- Locality-aware Gaussian Compression for Fast and High-quality Rendering (ICLR 2025 · similarity 0.87)
- Compressed 3D Gaussian Splatting for Accelerated Novel View Synthesis (CVPR 2024 · similarity 0.87)
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