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
this https URL

提出一种基于 SpectralNeRF 的光谱透视的高质量物理渲染方法，通过修改经典的光谱渲染为两个主要步骤，即生成一系列不同波长的光谱图并将其组合为 RGB 输出，通过提出的多层感知机 (SpectralMLP) 和光谱注意力 UNet (SAUNet) 实现这两个步骤。评估结果表明，相对于最近的 NeRF-based 方法，所提出的 SpectralNeRF 在合成新视图时，在合成和真实数据集上具有更好的性能。