NeRFProtector

This is the official implementation of Protecting NeRFs’ Copyright via Plug-And-Play Watermarking Base Model (ECCV 2024) [Arxiv]

Install

git clone --recursive https://github.com/qsong2001/NeRFProtector-code.git
cd NeRFProtector-code

Install with conda and pip

conda create -n ngp python==3.11
conda activate ngp

pip install torch==2.2.0 torchvision==0.17.0 torchaudio==2.2.0 --index-url https://download.pytorch.org/whl/cu121

pip install -r requirements.txt

Usage

We implement our NeRFProtector on torch-ngp.

Please refer to torch-ngp for more details of usage.

For example, in the Lego scene, we can use the script below:

python main_nerf.py data/nerf_synthetic/lego --workspace out/lego -O --bound 1.0 --scale 0.8 --dt_gamma 0 --lambda1 0.001 --iters 10000 --lr 4e-2 --ml_sample True

Other related projects

• torch-ngp: PyTorch-based Instant NGP

• stable signature: Rooting Watermarks in Latent Diffusion Models

Citation

If you find this work useful, a citation will be appreciated via:

@inproceedings{song2024protecting,
  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={ECCV},
  year={2024}
}

Acknowledgement

• Credits to Thomas Müller for the amazing tiny-cuda-nn and instant-ngp:

  @misc{tiny-cuda-nn,
      Author = {Thomas M\"uller},
      Year = {2021},
      Note = {https://github.com/nvlabs/tiny-cuda-nn},
      Title = {Tiny {CUDA} Neural Network Framework}
  }
  
  @article{mueller2022instant,
      title = {Instant Neural Graphics Primitives with a Multiresolution Hash Encoding},
      author = {Thomas M\"uller and Alex Evans and Christoph Schied and Alexander Keller},
      journal = {arXiv:2201.05989},
      year = {2022},
      month = jan
  }
  
  @misc{torch-ngp,
  Author = {Jiaxiang Tang},
  Year = {2022},
  Note = {https://github.com/ashawkey/torch-ngp},
  Title = {Torch-ngp: a PyTorch implementation of instant-ngp}
  

} ```