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.
6694f7600e08a6505b344cf5
Diego Gomez
6694f7600e08a6505b344cf6
Julien Philip
claimed_verified
2024-09-17T15:08:21.742Z
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/avatars/38a28103b15d93c7d9243bc5418e623e.svg
Julien Philip
user
Jovphi
6694f7600e08a6505b344cf7
Adrien Kaiser
6694f7600e08a6505b344cf8
Élie Michel
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.
A novel method, RRM, effectively extracts materials, geometry, and lighting for relighting and novel view synthesis even in scenes with highly reflective objects using a physically-aware radiance field and Laplacian Pyramid based environment light.
NeRFs physically-based parameters radiance field Laplacian Pyramid environment lighting relighting novel view synthesis reflective objects