3DFIRES: Few Image 3D REconstruction for Scenes with Hidden Surface
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866910366106124288 |
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| author | Jin, Linyi Kulkarni, Nilesh Fouhey, David |
| author_facet | Jin, Linyi Kulkarni, Nilesh Fouhey, David |
| contents | This paper introduces 3DFIRES, a novel system for scene-level 3D reconstruction from posed images. Designed to work with as few as one view, 3DFIRES reconstructs the complete geometry of unseen scenes, including hidden surfaces. With multiple view inputs, our method produces full reconstruction within all camera frustums. A key feature of our approach is the fusion of multi-view information at the feature level, enabling the production of coherent and comprehensive 3D reconstruction. We train our system on non-watertight scans from large-scale real scene dataset. We show it matches the efficacy of single-view reconstruction methods with only one input and surpasses existing techniques in both quantitative and qualitative measures for sparse-view 3D reconstruction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_08768 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | 3DFIRES: Few Image 3D REconstruction for Scenes with Hidden Surface Jin, Linyi Kulkarni, Nilesh Fouhey, David Computer Vision and Pattern Recognition This paper introduces 3DFIRES, a novel system for scene-level 3D reconstruction from posed images. Designed to work with as few as one view, 3DFIRES reconstructs the complete geometry of unseen scenes, including hidden surfaces. With multiple view inputs, our method produces full reconstruction within all camera frustums. A key feature of our approach is the fusion of multi-view information at the feature level, enabling the production of coherent and comprehensive 3D reconstruction. We train our system on non-watertight scans from large-scale real scene dataset. We show it matches the efficacy of single-view reconstruction methods with only one input and surpasses existing techniques in both quantitative and qualitative measures for sparse-view 3D reconstruction. |
| title | 3DFIRES: Few Image 3D REconstruction for Scenes with Hidden Surface |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.08768 |