SOURCE-LINKED INTELLIGENCE
Towards Practical Compression of 3D Gaussian Splatting
3D Gaussian Splatting (3DGS) enables high-quality novel-view synthesis but requires substantial storage. Existing compression methods often rely on spatial context modeling over irregular 3D representations, increasing the complexity of training and coding. Meanwhile, floating-point context inference can introduce numerical inconsistencies across platforms, causing entropy-decoding failures. To address these practical challenges, we propose COSA-GS, which constructs context without spatial aggregation through anchor-wise causal factorization. Specifically, we use geometry context derived from
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-24T17:57:56.000Z
First collected: 2026-09-25T06:12:46.948Z. This is not the publication date.