PMNI: Pose-free Multi-view Normal Integration for Reflective and Textureless Surface Reconstruction
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866913790676697088 |
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| author | Pei, Mingzhi Cao, Xu Wang, Xiangyi Guo, Heng Ma, Zhanyu |
| author_facet | Pei, Mingzhi Cao, Xu Wang, Xiangyi Guo, Heng Ma, Zhanyu |
| contents | Reflective and textureless surfaces remain a challenge in multi-view 3D reconstruction. Both camera pose calibration and shape reconstruction often fail due to insufficient or unreliable cross-view visual features. To address these issues, we present PMNI (Pose-free Multi-view Normal Integration), a neural surface reconstruction method that incorporates rich geometric information by leveraging surface normal maps instead of RGB images. By enforcing geometric constraints from surface normals and multi-view shape consistency within a neural signed distance function (SDF) optimization framework, PMNI simultaneously recovers accurate camera poses and high-fidelity surface geometry. Experimental results on synthetic and real-world datasets show that our method achieves state-of-the-art performance in the reconstruction of reflective surfaces, even without reliable initial camera poses. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_08410 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PMNI: Pose-free Multi-view Normal Integration for Reflective and Textureless Surface Reconstruction Pei, Mingzhi Cao, Xu Wang, Xiangyi Guo, Heng Ma, Zhanyu Computer Vision and Pattern Recognition Reflective and textureless surfaces remain a challenge in multi-view 3D reconstruction. Both camera pose calibration and shape reconstruction often fail due to insufficient or unreliable cross-view visual features. To address these issues, we present PMNI (Pose-free Multi-view Normal Integration), a neural surface reconstruction method that incorporates rich geometric information by leveraging surface normal maps instead of RGB images. By enforcing geometric constraints from surface normals and multi-view shape consistency within a neural signed distance function (SDF) optimization framework, PMNI simultaneously recovers accurate camera poses and high-fidelity surface geometry. Experimental results on synthetic and real-world datasets show that our method achieves state-of-the-art performance in the reconstruction of reflective surfaces, even without reliable initial camera poses. |
| title | PMNI: Pose-free Multi-view Normal Integration for Reflective and Textureless Surface Reconstruction |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.08410 |