Protecting NeRFs' Copyright via Plug-And-Play Watermarking Base Model
- URL: http://arxiv.org/abs/2407.07735v1
- Date: Wed, 10 Jul 2024 15:06:52 GMT
- Title: Protecting NeRFs' Copyright via Plug-And-Play Watermarking Base Model
- Authors: Qi Song, Ziyuan Luo, Ka Chun Cheung, Simon See, Renjie Wan,
- Abstract summary: Neural Radiance Fields (NeRFs) have become a key method for 3D scene representation.
We propose textbfNeRFProtector, which adopts a plug-and-play strategy to protect NeRF's copyright during its creation.
- Score: 29.545874014535297
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Neural Radiance Fields (NeRFs) have become a key method for 3D scene representation. With the rising prominence and influence of NeRF, safeguarding its intellectual property has become increasingly important. In this paper, we propose \textbf{NeRFProtector}, which adopts a plug-and-play strategy to protect NeRF's copyright during its creation. NeRFProtector utilizes a pre-trained watermarking base model, enabling NeRF creators to embed binary messages directly while creating their NeRF. Our plug-and-play property ensures NeRF creators can flexibly choose NeRF variants without excessive modifications. Leveraging our newly designed progressive distillation, we demonstrate performance on par with several leading-edge neural rendering methods. Our project is available at: \url{https://qsong2001.github.io/NeRFProtector}.
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