CopyRNeRF: Protecting the CopyRight of Neural Radiance Fields

Ziyuan Luo, Qing Guo, K. Cheung, S. See, Renjie Wan
2/14/2026

No official code found. Best implementation: luo-ziyuan/CopyRNeRF-code (33 stars, last commit 2023-11-04).

Abstract

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. Project page: https://luo-ziyuan.github.io/copyrnerf.

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Code Implementations(1)

luo-ziyuan/CopyRNeRF-codeCommunity100%
334PythonSep 30, 20232 years ago

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Cite this paper

@article{luo2026copyrnerf,
  title  = {CopyRNeRF: Protecting the CopyRight of Neural Radiance Fields},
  author = {Ziyuan Luo and Qing Guo and K. Cheung and S. See and Renjie Wan},
  year   = {2026},
  eprint = {2307.11526},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/pdf/2307.11526},
  journal = {ICCV 2023 2023}
}

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