NeRF as Non-Distant Environment Emitter in Physics-based Inverse
  Rendering
Jingwang Ling Ruihan Yu Feng Xu Chun Du Shuang Zhao
Physics-based inverse rendering aims to jointly optimize shape, materials,
and lighting from captured 2D images. Here lighting is an important part of
achieving faithful light transport simulation. While the environment map is
commonly used as the lighting model in inverse rendering, we show that its
distant lighting assumption leads to spatial invariant lighting, which can be
an inaccurate approximation in real-world inverse rendering. We propose to use
NeRF as a spatially varying environment lighting model and build an inverse
rendering pipeline using NeRF as the non-distant environment emitter. By
comparing our method with the environment map on real and synthetic datasets,
we show that our NeRF-based emitter models the scene lighting more accurately
and leads to more accurate inverse rendering. Project page and video:
https://nerfemitterpbir.github.io/.