title: Relighting Neural Radiance Fields with Shadow and Highlight Hints
summary: This paper presents a novel neural implicit radiance representation for free
  viewpoint relighting from a small set of unstructured photographs of an object
  lit by a moving point light source different from the view position. We express
  the shape as a signed distance function modeled by a multi layer perceptron. In
  contrast to prior relightable implicit neural representations, we do not
  disentangle the different reflectance components, but model both the local and
  global reflectance at each point by a second multi layer perceptron that, in
  addition, to density features, the current position, the normal (from the
  signed distace function), view direction, and light position, also takes shadow
  and highlight hints to aid the network in modeling the corresponding high
  frequency light transport effects. These hints are provided as a suggestion,
  and we leave it up to the network to decide how to incorporate these in the
  final relit result. We demonstrate and validate our neural implicit
  representation on synthetic and real scenes exhibiting a wide variety of
  shapes, material properties, and global illumination light transport.
paper_authors._id: 64ebfb98ece58a9799b32bf4
paper_authors.name: Chong Zeng
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-08-28T07:11:29.172Z
paper_authors._id: 60747cbf3ea03830676542b5
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/60747cbf3ea03830676542b5/wGr1Jzz520JM9nZ-UcLyb.png
paper_authors.fullname: Chong Zeng
paper_authors.type: user
paper_authors.user: NCJ
paper_authors._id: 64ebfb98ece58a9799b32bf5
paper_authors.name: Guojun Chen
paper_authors._id: 64ebfb98ece58a9799b32bf6
paper_authors.name: Yue Dong
paper_authors._id: 64ebfb98ece58a9799b32bf7
paper_authors.name: Pieter Peers
paper_authors._id: 64ebfb98ece58a9799b32bf8
paper_authors.name: Hongzhi Wu
paper_authors._id: 64ebfb98ece58a9799b32bf9
paper_authors.name: Xin Tong
paper_title: Relighting Neural Radiance Fields with Shadow and Highlight Hints
paper_summary: This paper presents a novel neural implicit radiance representation for free
  viewpoint relighting from a small set of unstructured photographs of an object
  lit by a moving point light source different from the view position. We express
  the shape as a signed distance function modeled by a multi layer perceptron. In
  contrast to prior relightable implicit neural representations, we do not
  disentangle the different reflectance components, but model both the local and
  global reflectance at each point by a second multi layer perceptron that, in
  addition, to density features, the current position, the normal (from the
  signed distace function), view direction, and light position, also takes shadow
  and highlight hints to aid the network in modeling the corresponding high
  frequency light transport effects. These hints are provided as a suggestion,
  and we leave it up to the network to decide how to incorporate these in the
  final relit result. We demonstrate and validate our neural implicit
  representation on synthetic and real scenes exhibiting a wide variety of
  shapes, material properties, and global illumination light transport.
paper_ai_keywords: neural implicit radiance representation free viewpoint relighting signed distance function multi layer perceptron relightable implicit neural representations local and global reflectance density features view direction light position shadow and highlight hints high frequency light transport effects global illumination light transport