RNb-NeuS: Reflectance and Normal-based Multi-View 3D Reconstruction
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866917625595953152 |
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| author | Brument, Baptiste Bruneau, Robin Quéau, Yvain Mélou, Jean Lauze, François Bernard Jean-Denis Durou, Jean-Denis Calvet, Lilian |
| author_facet | Brument, Baptiste Bruneau, Robin Quéau, Yvain Mélou, Jean Lauze, François Bernard Jean-Denis Durou, Jean-Denis Calvet, Lilian |
| contents | This paper introduces a versatile paradigm for integrating multi-view reflectance (optional) and normal maps acquired through photometric stereo. Our approach employs a pixel-wise joint re-parameterization of reflectance and normal, considering them as a vector of radiances rendered under simulated, varying illumination. This re-parameterization enables the seamless integration of reflectance and normal maps as input data in neural volume rendering-based 3D reconstruction while preserving a single optimization objective. In contrast, recent multi-view photometric stereo (MVPS) methods depend on multiple, potentially conflicting objectives. Despite its apparent simplicity, our proposed approach outperforms state-of-the-art approaches in MVPS benchmarks across F-score, Chamfer distance, and mean angular error metrics. Notably, it significantly improves the detailed 3D reconstruction of areas with high curvature or low visibility. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_01215 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | RNb-NeuS: Reflectance and Normal-based Multi-View 3D Reconstruction Brument, Baptiste Bruneau, Robin Quéau, Yvain Mélou, Jean Lauze, François Bernard Jean-Denis Durou, Jean-Denis Calvet, Lilian Computer Vision and Pattern Recognition This paper introduces a versatile paradigm for integrating multi-view reflectance (optional) and normal maps acquired through photometric stereo. Our approach employs a pixel-wise joint re-parameterization of reflectance and normal, considering them as a vector of radiances rendered under simulated, varying illumination. This re-parameterization enables the seamless integration of reflectance and normal maps as input data in neural volume rendering-based 3D reconstruction while preserving a single optimization objective. In contrast, recent multi-view photometric stereo (MVPS) methods depend on multiple, potentially conflicting objectives. Despite its apparent simplicity, our proposed approach outperforms state-of-the-art approaches in MVPS benchmarks across F-score, Chamfer distance, and mean angular error metrics. Notably, it significantly improves the detailed 3D reconstruction of areas with high curvature or low visibility. |
| title | RNb-NeuS: Reflectance and Normal-based Multi-View 3D Reconstruction |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2312.01215 |