Protecting NeRFs' Copyright via Plug-And-Play Watermarking Base Model

Autor:	Song, Qi, Luo, Ziyuan, Cheung, Ka Chun, See, Simon, Wan, Renjie
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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}. Comment: Accepted by ECCV2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2407.07735 Zobrazit plný text záznamu View this record from Arxiv