Protecting NeRFs' Copyright via Plug-and-Play Watermarking Base Model
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 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: https://qsong2001.github.io/NeRFProtector.
Cite
Text
Song et al. "Protecting NeRFs' Copyright via Plug-and-Play Watermarking Base Model." Proceedings of the European Conference on Computer Vision (ECCV), 2024. doi:10.1007/978-3-031-73247-8_4Markdown
[Song et al. "Protecting NeRFs' Copyright via Plug-and-Play Watermarking Base Model." Proceedings of the European Conference on Computer Vision (ECCV), 2024.](https://mlanthology.org/eccv/2024/song2024eccv-protecting/) doi:10.1007/978-3-031-73247-8_4BibTeX
@inproceedings{song2024eccv-protecting,
title = {{Protecting NeRFs' Copyright via Plug-and-Play Watermarking Base Model}},
author = {Song, Qi and Luo, Ziyuan and Cheung, Ka Chun and See, Simon and Wan, Renjie},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2024},
doi = {10.1007/978-3-031-73247-8_4},
url = {https://mlanthology.org/eccv/2024/song2024eccv-protecting/}
}