ReCoSplat: Autoregressive Feed-Forward Gaussian Splatting Using Render-and-Compare
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866917330324291584 |
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| author | Cheng, Freeman Ye, Botao Li, Xueting You, Junqi Zhan, Fangneng Yang, Ming-Hsuan |
| author_facet | Cheng, Freeman Ye, Botao Li, Xueting You, Junqi Zhan, Fangneng Yang, Ming-Hsuan |
| contents | Online novel view synthesis remains challenging, requiring robust scene reconstruction from sequential, often unposed, observations. We present ReCoSplat, an autoregressive feed-forward Gaussian Splatting model supporting posed or unposed inputs, with or without camera intrinsics. While assembling local Gaussians using camera poses scales better than canonical-space prediction, it creates a dilemma during training: using ground-truth poses ensures stability but causes a distribution mismatch when predicted poses are used at inference. To address this, we introduce a Render-and-Compare (ReCo) module. ReCo renders the current reconstruction from the predicted viewpoint and compares it with the incoming observation, providing a stable conditioning signal that compensates for pose errors. To support long sequences, we propose a hybrid KV cache compression strategy combining early-layer truncation with chunk-level selective retention, reducing the KV cache size by over 90% for 100+ frames. ReCoSplat achieves state-of-the-art performance across different input settings on both in- and out-of-distribution benchmarks. Code and pretrained models will be released. Our project page is at https://freemancheng.com/ReCoSplat . |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_09968 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ReCoSplat: Autoregressive Feed-Forward Gaussian Splatting Using Render-and-Compare Cheng, Freeman Ye, Botao Li, Xueting You, Junqi Zhan, Fangneng Yang, Ming-Hsuan Computer Vision and Pattern Recognition Online novel view synthesis remains challenging, requiring robust scene reconstruction from sequential, often unposed, observations. We present ReCoSplat, an autoregressive feed-forward Gaussian Splatting model supporting posed or unposed inputs, with or without camera intrinsics. While assembling local Gaussians using camera poses scales better than canonical-space prediction, it creates a dilemma during training: using ground-truth poses ensures stability but causes a distribution mismatch when predicted poses are used at inference. To address this, we introduce a Render-and-Compare (ReCo) module. ReCo renders the current reconstruction from the predicted viewpoint and compares it with the incoming observation, providing a stable conditioning signal that compensates for pose errors. To support long sequences, we propose a hybrid KV cache compression strategy combining early-layer truncation with chunk-level selective retention, reducing the KV cache size by over 90% for 100+ frames. ReCoSplat achieves state-of-the-art performance across different input settings on both in- and out-of-distribution benchmarks. Code and pretrained models will be released. Our project page is at https://freemancheng.com/ReCoSplat . |
| title | ReCoSplat: Autoregressive Feed-Forward Gaussian Splatting Using Render-and-Compare |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.09968 |