title: CopyRNeRF: Protecting the CopyRight of Neural Radiance Fields
summary: Neural Radiance Fields (NeRF) have the potential to be a major representation
  of media. Since training a NeRF has never been an easy task, the protection of
  its model copyright should be a priority. In this paper, by analyzing the pros
  and cons of possible copyright protection solutions, we propose to protect the
  copyright of NeRF models by replacing the original color representation in NeRF
  with a watermarked color representation. Then, a distortion-resistant rendering
  scheme is designed to guarantee robust message extraction in 2D renderings of
  NeRF. Our proposed method can directly protect the copyright of NeRF models
  while maintaining high rendering quality and bit accuracy when compared among
  optional solutions.
paper_authors._id: 64be09151a62149c5e825ca8
paper_authors.name: Ziyuan Luo
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-07-24T14:15:16.608Z
paper_authors._id: 64be5b4c4d2052b1aa39efba
paper_authors.avatarUrl: /avatars/1d81c71217dddec386cd22427158cc3b.svg
paper_authors.fullname: Ziyuan Luo
paper_authors.type: user
paper_authors.user: ziyluo
paper_authors._id: 64be09151a62149c5e825ca9
paper_authors.name: Qing Guo
paper_authors._id: 64be09151a62149c5e825caa
paper_authors.name: Ka Chun Cheung
paper_authors._id: 64be09151a62149c5e825cab
paper_authors.name: Simon See
paper_authors._id: 64be09151a62149c5e825cac
paper_authors.name: Renjie Wan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-07-24T11:31:28.772Z
paper_authors._id: 64be571df671da974e4d4ee4
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64be571df671da974e4d4ee4/KRgRhP0_07LXWDPLMgLHu.jpeg
paper_authors.fullname: WAN
paper_authors.type: user
paper_authors.user: wanpeoplejie
paper_title: CopyRNeRF: Protecting the CopyRight of Neural Radiance Fields
paper_summary: Neural Radiance Fields (NeRF) have the potential to be a major representation
  of media. Since training a NeRF has never been an easy task, the protection of
  its model copyright should be a priority. In this paper, by analyzing the pros
  and cons of possible copyright protection solutions, we propose to protect the
  copyright of NeRF models by replacing the original color representation in NeRF
  with a watermarked color representation. Then, a distortion-resistant rendering
  scheme is designed to guarantee robust message extraction in 2D renderings of
  NeRF. Our proposed method can directly protect the copyright of NeRF models
  while maintaining high rendering quality and bit accuracy when compared among
  optional solutions.
paper_ai_keywords: Neural Radiance Fields (NeRF) color representation watermark distortion-resistant rendering message extraction rendering quality bit accuracy