Relighting Neural Radiance Fields with Shadow and Highlight Hints
Chong Zeng Guojun Chen Yue Dong Pieter Peers Hongzhi Wu Xin Tong
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.