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Gaussian Splatting-based Volumetric Video Compression with Sparse 4D Anchors

NeurIPS 2026 · 3D vision › Gaussian splatting · approach: Deformable 4D Gaussian splatting · 0 citations (OpenAlex)

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Proposes SAGA, a Gaussian-splatting volumetric video codec using hierarchical sparse 4D anchors with INR decoders for inter-anchor interpolation and an orthogonality-updated memory bank for entropy context modeling.

Problem

Compact, render-efficient compression of dynamic 3D Gaussian Splatting scenes for immersive volumetric video, where single canonical scaffolds and isolated anchor conditioning limit handling of non-local dynamics, disocclusion, and inter-anchor redundancy.

Approach

Organize anchors hierarchically directly in spacetime (coarse and fine 4D anchors), decode fine anchors and Gaussian primitives from kernel-interpolated neighbor-anchor features via shared coordinate-based INR decoders, and use a fixed-size memory bank whose slots are updated with orthogonal write components for long-range entropy context.

Key results

Surprising findings

Limitations

Experimental setup

Datasets: Neu3D Panoptic Sports MPEG MIV CTC

Benchmarks: Neu3D Panoptic Sports MPEG MIV CTC

Metrics: PSNR SSIM LPIPS BD-rate FPS model size training time

Baselines: 3DGStream 4DGS STG E-D3DGS GIFStream MEGA TMIV-24.0 4DGC ADC-GS

Builds on: ScaffoldGS STG NVRC GIFStream uniform-noise quantization relaxation

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