title: Multi-Space Neural Radiance Fields
summary: 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.
paper_authors._id: 6459c34fabdbb77c4c6e247e
paper_authors.name: Ze-Xin Yin
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-05-25T15:21:01.020Z
paper_authors._id: 637de917425b0c9abbacc9d8
paper_authors.avatarUrl: /avatars/8f780c58bc0d4529474a438498823cfe.svg
paper_authors.fullname: Ze-Xin Yin
paper_authors.type: user
paper_authors.user: JasonYinnnn
paper_authors._id: 6459c34fabdbb77c4c6e247f
paper_authors.name: Jiaxiong Qiu
paper_authors._id: 6459c34fabdbb77c4c6e2480
paper_authors.name: Ming-Ming Cheng
paper_authors._id: 6459c34fabdbb77c4c6e2481
paper_authors.name: Bo Ren
paper_title: Multi-Space Neural Radiance Fields
paper_summary: 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.
paper_ai_keywords: Neural Radiance Fields (NeRF) radiance field multi-space subsets radiance field (MS-NeRF) feature fields parallel sub-spaces reflective objects refractive objects neural understanding neural network multi-space scheme computational overheads training inferring extra-space outputs NeRF Mip-NeRF Mip-NeRF 360 rendering high-quality scenes complex light paths mirror-like objects NeRF-based models synthetic scenes real captured scenes 7 real captured scenes 7 real captured scenes with complex reflection and refraction 360-degree viewpoints