title: RRM: Relightable assets using Radiance guided Material extraction
summary: 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.
paper_authors._id: 6694f7600e08a6505b344cf5
paper_authors.name: Diego Gomez
paper_authors._id: 6694f7600e08a6505b344cf6
paper_authors.name: Julien Philip
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-17T15:08:21.742Z
paper_authors._id: 63e0dbb35ba41def8794dab8
paper_authors.avatarUrl: /avatars/38a28103b15d93c7d9243bc5418e623e.svg
paper_authors.fullname: Julien Philip
paper_authors.type: user
paper_authors.user: Jovphi
paper_authors._id: 6694f7600e08a6505b344cf7
paper_authors.name: Adrien Kaiser
paper_authors._id: 6694f7600e08a6505b344cf8
paper_authors.name: Élie Michel
paper_title: RRM: Relightable assets using Radiance guided Material extraction
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: NeRFs physically-based parameters radiance field Laplacian Pyramid environment lighting relighting novel view synthesis reflective objects