RRM: Relightable assets using Radiance guided Material extraction
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866914862756528128 |
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| author | Gomez, Diego Philip, Julien Kaiser, Adrien Michel, Élie |
| author_facet | Gomez, Diego Philip, Julien Kaiser, Adrien Michel, Élie |
| contents | Synthesizing NeRFs under arbitrary lighting has become a seminal problem in the last few years. Recent efforts tackle the problem via the extraction of physically-based parameters that can then be rendered under arbitrary lighting, but they are limited in the range of scenes they can handle, usually mishandling glossy scenes. We propose RRM, a method that can extract the materials, geometry, and environment lighting of a scene even in the presence of highly reflective objects. Our method consists of a physically-aware radiance field representation that informs physically-based parameters, and an expressive environment light structure based on a Laplacian Pyramid. We demonstrate that our contributions outperform the state-of-the-art on parameter retrieval tasks, leading to high-fidelity relighting and novel view synthesis on surfacic scenes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_06397 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | RRM: Relightable assets using Radiance guided Material extraction Gomez, Diego Philip, Julien Kaiser, Adrien Michel, Élie Computer Vision and Pattern Recognition Synthesizing NeRFs under arbitrary lighting has become a seminal problem in the last few years. Recent efforts tackle the problem via the extraction of physically-based parameters that can then be rendered under arbitrary lighting, but they are limited in the range of scenes they can handle, usually mishandling glossy scenes. We propose RRM, a method that can extract the materials, geometry, and environment lighting of a scene even in the presence of highly reflective objects. Our method consists of a physically-aware radiance field representation that informs physically-based parameters, and an expressive environment light structure based on a Laplacian Pyramid. We demonstrate that our contributions outperform the state-of-the-art on parameter retrieval tasks, leading to high-fidelity relighting and novel view synthesis on surfacic scenes. |
| title | RRM: Relightable assets using Radiance guided Material extraction |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.06397 |