RRM: Relightable assets using Radiance guided Material extraction
Authors: name: Diego Gomez, name: Julien Philip, name: Adrien Kaiser, name: Élie Michel

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.