RRM: Relightable assets using Radiance guided Material extraction
<|reference_start|>RRM: Relightable assets using Radiance guided Material extraction: 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.<|reference_end|>
@article{gomez2024rrm:,
    title={RRM: Relightable assets using Radiance guided Material extraction},
    author={Diego Gomez, Julien Philip, Adrien Kaiser, 'Elie Michel},
    journal={arXiv preprint arXiv:2407.06397},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.06397},
    primaryClass={cs.CV}
}