SpectralNeRF: Physically Based Spectral Rendering with Neural Radiance
  Field
Ru Li Jia Liu Guanghui Liu Shengping Zhang Bing Zeng Shuaicheng Liu
In this paper, we propose SpectralNeRF, an end-to-end Neural Radiance Field
(NeRF)-based architecture for high-quality physically based rendering from a
novel spectral perspective. We modify the classical spectral rendering into two
main steps, 1) the generation of a series of spectrum maps spanning different
wavelengths, 2) the combination of these spectrum maps for the RGB output. Our
SpectralNeRF follows these two steps through the proposed multi-layer
perceptron (MLP)-based architecture (SpectralMLP) and Spectrum Attention UNet
(SAUNet). Given the ray origin and the ray direction, the SpectralMLP
constructs the spectral radiance field to obtain spectrum maps of novel views,
which are then sent to the SAUNet to produce RGB images of white-light
illumination. Applying NeRF to build up the spectral rendering is a more
physically-based way from the perspective of ray-tracing. Further, the spectral
radiance fields decompose difficult scenes and improve the performance of
NeRF-based methods. Comprehensive experimental results demonstrate the proposed
SpectralNeRF is superior to recent NeRF-based methods when synthesizing new
views on synthetic and real datasets. The codes and datasets are available at
https://github.com/liru0126/SpectralNeRF.