Protecting NeRFs' Copyright via Plug-And-Play Watermarking Base Model
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866910521501941760 |
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| author | Song, Qi Luo, Ziyuan Cheung, Ka Chun See, Simon Wan, Renjie |
| author_facet | Song, Qi Luo, Ziyuan Cheung, Ka Chun See, Simon Wan, Renjie |
| contents | 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}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_07735 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Protecting NeRFs' Copyright via Plug-And-Play Watermarking Base Model Song, Qi Luo, Ziyuan Cheung, Ka Chun See, Simon Wan, Renjie Computer Vision and Pattern Recognition 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}. |
| title | Protecting NeRFs' Copyright via Plug-And-Play Watermarking Base Model |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.07735 |