CopyRNeRF: Protecting the CopyRight of Neural Radiance Fields

Ziyuan Luo, Qing Guo, Ka Chun Cheung, Simon See, Renjie Wan*

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

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.
Original languageEnglish
Title of host publication2023 IEEE/CVF International Conference on Computer Vision (ICCV)
Place of PublicationParis, France
PublisherIEEE
Pages22401-22411
Number of pages11
ISBN (Electronic)9798350307184
ISBN (Print)9798350307191
DOIs
Publication statusPublished - Oct 2023
Event18th IEEE International Conference on Computer Vision, ICCV 2023 - Paris Convention Center, Paris, France
Duration: 2 Oct 20236 Oct 2023
https://iccv2023.thecvf.com/ (Conference website)
https://iccv2023.thecvf.com/iccv2023.main.conference.program-38--MTE.php (Conference programme )
https://openaccess.thecvf.com/ICCV2023 (Conference proceedings)

Publication series

NameInternational Conference on Computer Vision (ICCV)
ISSN (Print)1550-5499
ISSN (Electronic)2380-7504

Conference

Conference18th IEEE International Conference on Computer Vision, ICCV 2023
Country/TerritoryFrance
CityParis
Period2/10/236/10/23
Internet address

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