Multi-Space Neural Radiance Fields
Ze-Xin Yin Jiaxiong Qiu Ming-Ming Cheng Bo Ren
Existing Neural Radiance Fields (NeRF) methods suffer from the existence of
reflective objects, often resulting in blurry or distorted rendering. Instead
of calculating a single radiance field, we propose a multi-space neural
radiance field (MS-NeRF) that represents the scene using a group of feature
fields in parallel sub-spaces, which leads to a better understanding of the
neural network toward the existence of reflective and refractive objects. Our
multi-space scheme works as an enhancement to existing NeRF methods, with only
small computational overheads needed for training and inferring the extra-space
outputs. We demonstrate the superiority and compatibility of our approach using
three representative NeRF-based models, i.e., NeRF, Mip-NeRF, and Mip-NeRF 360.
Comparisons are performed on a novelly constructed dataset consisting of 25
synthetic scenes and 7 real captured scenes with complex reflection and
refraction, all having 360-degree viewpoints. Extensive experiments show that
our approach significantly outperforms the existing single-space NeRF methods
for rendering high-quality scenes concerned with complex light paths through
mirror-like objects. Our code and dataset will be publicly available at
https://zx-yin.github.io/msnerf.