MS-NeRF: Multi-Space Neural Radiance Fields
Abstract
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 design different multi-space modules for representative MLP-based and grid-based NeRF methods, which improve Mip-NeRF 360 by 4.15 dB in PSNR with 0.5% extra parameters and further improve TensoRF by 2.71 dB with 0.046% extra parameters on reflective regions without degrading the rendering quality on other regions. We further construct a novel dataset consisting of 33 synthetic scenes and 7 real captured scenes with complex reflection and refraction, where we design complex camera paths to fully benchmark the robustness of NeRF-based methods. 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.
Method Pipeline for MLP-based methods
Method Pipeline for grid-based methods
Novel View Rendering(Mip-NeRF 360 v.s. MS-Mip-NeRF 360)
Novel View Rendering(Mip-NeRF v.s. MS-Mip-NeRFB)
Sub-space RGB and weights(based on MS-NeRFB)
BibTeX
@ARTICLE{msnerf_2025_TPAMI,
author={Yin, Ze-Xin and Jiao, Peng-Yi and Qiu, Jiaxiong and Cheng, Ming-Ming and Ren, Bo},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
title={MS-NeRF: Multi-Space Neural Radiance Fields},
year={2025},
volume={},
number={},
pages={1-18},
doi={10.1109/TPAMI.2025.3540074}
}