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Paper · arXiv 2307.11526

CopyRNeRF: Protecting the CopyRight of Neural Radiance Fields

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

13 upvotesJuly 21, 2023arXiv 预印本
AI 摘要

A method is proposed to protect NeRF models' copyright by watermarking the color representation with a distortion-resistant rendering scheme, maintaining high rendering quality and bit accuracy.

Neural Radiance FieldsNeRFcopyright protectionwatermarkingdistortion-resistant renderingrendering qualitybit accuracy

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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