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

2026-10-30 00:41:25
Ge Gao, Siyue Teng, Chanqgi Wang, Fan Zhang, Nantheera Anantrasirichai, Jui Chiu Chiang, Wen-Hsiao Peng, David Bull

Abstract

Immersive video communication requires photorealistic, compact dynamic scene representations that support efficient rendering. 3D Gaussian Splatting (3DGS) is a promising approach, but dynamic 3DGS remains difficult to compress due to its dense primitives and spatiotemporal redundancy. Anchor-based formulations improve compactness through sparse scaffolds that share geometry and appearance across primitives. However, existing designs typically deform a single canonical scaffold and condition each primitive solely on its associated anchor, limiting their ability to model non-local dynamics and disocclusions and exploit inter-anchor correlations. To address these limitations, we propose SAGA, a volumetric video codec built on Sparse Anchor-assisted GAussian splatting representations. SAGA represents a dynamic scene using hierarchical sparse anchors in 4D space-time. Coordinate-based INR decoders generate fine anchors and Gaussian primitives from features obtained through kernel interpolation among nearby anchors, enabling parameter sharing across spatiotemporal structures. A fixed-size memory with a novel orthogonality-informed slot-update strategy further captures long-range dependencies among unstructured anchors for entropy coding. Empirically, \name achieves PSNR-based BD-rate reductions of 90.88%, 64.30% and 65.14% relative to GIFStream, TED-4DGS, and OMG4, respectively, on the Neu3D testset.

Abstract (translated)

URL

https://arxiv.org/abs/2609.33969

PDF

https://arxiv.org/pdf/2609.33969.pdf


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