CopyRNeRF: Protecting the CopyRight of Neural Radiance Fields
Ziyuan Luo, Qing Guo, Ka Chun Cheung, Simon See, Renjie Wan
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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Install the CLIlune papers fulltext 97ca5711-cf48-4e04-a3cb-e40ad34dc06fCited by top-tier papers18
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Builds on20
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- Plenoxels: Radiance Fields without Neural NetworksSara Fridovich-Keil, Alex Yu, Matthew Tancik, Qinhong Chen et al.CVPR 2022 · 1,237 citations
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- Multiview Neural Surface Reconstruction by Disentangling Geometry and AppearanceLior Yariv, Yoni Kasten, Dror Moran, Meirav Galun et al.NeurIPS 2020 · 1,010 citations
- GRAF: Generative Radiance Fields for 3D-Aware Image SynthesisKatja Schwarz, Yiyi Liao, Michael Niemeyer, Andreas GeigerNeurIPS 2020 · 1,001 citations
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