Dense-SfM: Structure from Motion with Dense Consistent Matching
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912855161307136 |
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| author | Lee, JongMin Yoo, Sungjoo |
| author_facet | Lee, JongMin Yoo, Sungjoo |
| contents | We present Dense-SfM, a novel Structure from Motion (SfM) framework designed for dense and accurate 3D reconstruction from multi-view images. Sparse keypoint matching, which traditional SfM methods often rely on, limits both accuracy and point density, especially in texture-less areas. Dense-SfM addresses this limitation by integrating dense matching with a Gaussian Splatting (GS) based track extension which gives more consistent, longer feature tracks. To further improve reconstruction accuracy, Dense-SfM is equipped with a multi-view kernelized matching module leveraging transformer and Gaussian Process architectures, for robust track refinement across multi-views. Evaluations on the ETH3D and Texture-Poor SfM datasets show that Dense-SfM offers significant improvements in accuracy and density over state-of-the-art methods. Project page: https://icetea-cv.github.io/densesfm/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_14277 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Dense-SfM: Structure from Motion with Dense Consistent Matching Lee, JongMin Yoo, Sungjoo Computer Vision and Pattern Recognition We present Dense-SfM, a novel Structure from Motion (SfM) framework designed for dense and accurate 3D reconstruction from multi-view images. Sparse keypoint matching, which traditional SfM methods often rely on, limits both accuracy and point density, especially in texture-less areas. Dense-SfM addresses this limitation by integrating dense matching with a Gaussian Splatting (GS) based track extension which gives more consistent, longer feature tracks. To further improve reconstruction accuracy, Dense-SfM is equipped with a multi-view kernelized matching module leveraging transformer and Gaussian Process architectures, for robust track refinement across multi-views. Evaluations on the ETH3D and Texture-Poor SfM datasets show that Dense-SfM offers significant improvements in accuracy and density over state-of-the-art methods. Project page: https://icetea-cv.github.io/densesfm/. |
| title | Dense-SfM: Structure from Motion with Dense Consistent Matching |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2501.14277 |