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| Format: | Preprint |
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2026
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| Online Access: | https://arxiv.org/abs/2603.17227 |
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| _version_ | 1866912986333970432 |
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| author | Dahal, Ashim Abdelfattah, Rabab Rahimi, Nick |
| author_facet | Dahal, Ashim Abdelfattah, Rabab Rahimi, Nick |
| contents | Dynamic scene reconstruction with Gaussian Splatting has enabled efficient streaming for real-time rendering and free-viewpoint video. However, most pipelines rely on fixed anchor selection such as Farthest Point Sampling (FPS), typically using 8,192 anchors regardless of scene complexity, which over-allocates computation under strict budgets. We propose Efficient Gaussian Streaming (EGS), a plug-in, budget-aware anchor sampler that replaces FPS with a reinforcement-learned policy while keeping the Gaussian streaming reconstruction backbone unchanged. The policy jointly selects an anchor budget and a subset of informative anchors under discrete constraints, balancing reconstruction quality and runtime using spatial features of the Gaussian representation. We evaluate EGS in two settings: fast rendering, which prioritizes runtime efficiency, and high-quality refinement, which enables additional optimization. Experiments on dynamic multi-view datasets show consistent improvements in the quality--efficiency trade-off over FPS sampling. On unseen data, in fast rendering at 256 anchors ($32\times$ fewer than 8,192), EGS improves PSNR by $+0.52$--$0.61$\,dB while running $1.29$--$1.35\times$ faster than IGS@8192 (N3DV and MeetingRoom). In high-quality refinement, EGS remains competitive with the full-anchor baseline at substantially lower anchor budgets. \emph{Code and pretrained checkpoints will be released upon acceptance.} \keywords{4D Gaussian Splatting \and 4D Gaussian Streaming \and Reinforcement Learning} |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_17227 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Adaptive Anchor Policies for Efficient 4D Gaussian Streaming Dahal, Ashim Abdelfattah, Rabab Rahimi, Nick Computer Vision and Pattern Recognition Dynamic scene reconstruction with Gaussian Splatting has enabled efficient streaming for real-time rendering and free-viewpoint video. However, most pipelines rely on fixed anchor selection such as Farthest Point Sampling (FPS), typically using 8,192 anchors regardless of scene complexity, which over-allocates computation under strict budgets. We propose Efficient Gaussian Streaming (EGS), a plug-in, budget-aware anchor sampler that replaces FPS with a reinforcement-learned policy while keeping the Gaussian streaming reconstruction backbone unchanged. The policy jointly selects an anchor budget and a subset of informative anchors under discrete constraints, balancing reconstruction quality and runtime using spatial features of the Gaussian representation. We evaluate EGS in two settings: fast rendering, which prioritizes runtime efficiency, and high-quality refinement, which enables additional optimization. Experiments on dynamic multi-view datasets show consistent improvements in the quality--efficiency trade-off over FPS sampling. On unseen data, in fast rendering at 256 anchors ($32\times$ fewer than 8,192), EGS improves PSNR by $+0.52$--$0.61$\,dB while running $1.29$--$1.35\times$ faster than IGS@8192 (N3DV and MeetingRoom). In high-quality refinement, EGS remains competitive with the full-anchor baseline at substantially lower anchor budgets. \emph{Code and pretrained checkpoints will be released upon acceptance.} \keywords{4D Gaussian Splatting \and 4D Gaussian Streaming \and Reinforcement Learning} |
| title | Adaptive Anchor Policies for Efficient 4D Gaussian Streaming |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.17227 |