GaussFusion: Improving 3D Reconstruction in the Wild with A Geometry-Informed Video Generator
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866911549561503744 |
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| author | Zhu, Liyuan Narayana, Manjunath Stary, Michal Hutchcroft, Will Wetzstein, Gordon Armeni, Iro |
| author_facet | Zhu, Liyuan Narayana, Manjunath Stary, Michal Hutchcroft, Will Wetzstein, Gordon Armeni, Iro |
| contents | We present GaussFusion, a novel approach for improving 3D Gaussian splatting (3DGS) reconstructions in the wild through geometry-informed video generation. GaussFusion mitigates common 3DGS artifacts, including floaters, flickering, and blur caused by camera pose errors, incomplete coverage, and noisy geometry initialization. Unlike prior RGB-based approaches limited to a single reconstruction pipeline, our method introduces a geometry-informed video-to-video generator that refines 3DGS renderings across both optimization-based and feed-forward methods. Given an existing reconstruction, we render a Gaussian primitive video buffer encoding depth, normals, opacity, and covariance, which the generator refines to produce temporally coherent, artifact-free frames. We further introduce an artifact synthesis pipeline that simulates diverse degradation patterns, ensuring robustness and generalization. GaussFusion achieves state-of-the-art performance on novel-view synthesis benchmarks, and an efficient variant runs in real time at 15 FPS while maintaining similar performance, enabling interactive 3D applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_25053 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | GaussFusion: Improving 3D Reconstruction in the Wild with A Geometry-Informed Video Generator Zhu, Liyuan Narayana, Manjunath Stary, Michal Hutchcroft, Will Wetzstein, Gordon Armeni, Iro Computer Vision and Pattern Recognition We present GaussFusion, a novel approach for improving 3D Gaussian splatting (3DGS) reconstructions in the wild through geometry-informed video generation. GaussFusion mitigates common 3DGS artifacts, including floaters, flickering, and blur caused by camera pose errors, incomplete coverage, and noisy geometry initialization. Unlike prior RGB-based approaches limited to a single reconstruction pipeline, our method introduces a geometry-informed video-to-video generator that refines 3DGS renderings across both optimization-based and feed-forward methods. Given an existing reconstruction, we render a Gaussian primitive video buffer encoding depth, normals, opacity, and covariance, which the generator refines to produce temporally coherent, artifact-free frames. We further introduce an artifact synthesis pipeline that simulates diverse degradation patterns, ensuring robustness and generalization. GaussFusion achieves state-of-the-art performance on novel-view synthesis benchmarks, and an efficient variant runs in real time at 15 FPS while maintaining similar performance, enabling interactive 3D applications. |
| title | GaussFusion: Improving 3D Reconstruction in the Wild with A Geometry-Informed Video Generator |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.25053 |