Relighting Neural Radiance Fields with Shadow and Highlight Hints
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.
64ebfb98ece58a9799b32bf4
Chong Zeng
claimed_verified
2023-08-28T07:11:29.172Z
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Chong Zeng
user
NCJ
64ebfb98ece58a9799b32bf5
Guojun Chen
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Yue Dong
extracted_pending
2025-04-25T09:25:18.941Z
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doyleconan
user
doyleconan
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Pieter Peers
extracted_pending
2025-05-29T13:00:36.601Z
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Pieter Peers
user
PieterPeers
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Hongzhi Wu
64ebfb98ece58a9799b32bf9
Xin Tong
Relighting Neural Radiance Fields with Shadow and Highlight Hints
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.
A new neural implicit radiance representation models object relighting from a small set of photographs using signed distance functions and two MLPs, incorporating shadow and highlight hints for high-frequency light transport effects.
neural implicit radiance representation signed distance function multi layer perceptron relightable implicit neural representations shape material properties global illumination light transport shadow highlight hints