Multi-Space Neural Radiance Fields
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.
6459c34fabdbb77c4c6e247e
Ze-Xin Yin
claimed_verified
2023-05-25T15:21:01.020Z
637de917425b0c9abbacc9d8
/avatars/8f780c58bc0d4529474a438498823cfe.svg
Ze-Xin Yin
user
JasonYinnnn
6459c34fabdbb77c4c6e247f
Jiaxiong Qiu
6459c34fabdbb77c4c6e2480
Ming-Ming Cheng
6459c34fabdbb77c4c6e2481
Bo Ren
Multi-Space Neural Radiance Fields
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.
A multi-space neural radiance field (MS-NeRF) enhances rendering quality for scenes with reflective and refractive objects by using multiple feature fields in parallel sub-spaces.
Neural Radiance Fields (NeRF) multi-space neural radiance field (MS-NeRF) feature fields radiance field reflective objects refractive objects Mip-NeRF Mip-NeRF 360