CopyRNeRF: Protecting the CopyRight of Neural Radiance Fields

Ziyuan Luo, Qing Guo, Ka Chun Cheung, Simon See, Renjie Wan; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023, pp. 22401-22411

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.

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[bibtex]
@InProceedings{Luo_2023_ICCV, author = {Luo, Ziyuan and Guo, Qing and Cheung, Ka Chun and See, Simon and Wan, Renjie}, title = {CopyRNeRF: Protecting the CopyRight of Neural Radiance Fields}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2023}, pages = {22401-22411} }