This folder is the headless CARFF NeRF decoder implementation based on the pytorch implementation of instant-ngp, as described in Instant Neural Graphics Primitives with a Multiresolution Hash Encoding.

pip install -r requirements.txt
pip install git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch

conda env create -f environment.yml
conda activate torch-ngp

We use the same data format as instant-ngp, e.g., armadillo and fox. Please download and put them under ./data.

First time running will take some time to compile the CUDA extensions.

python main_nerf.py path/to/data --workspace workspace_name

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

@misc{torch-ngp,
    Author = {Jiaxiang Tang},
    Year = {2022},
    Note = {https://github.com/ashawkey/torch-ngp},
    Title = {Torch-ngp: a PyTorch implementation of instant-ngp}
}

@article{tang2022compressible,
    title = {Compressible-composable NeRF via Rank-residual Decomposition},
    author = {Tang, Jiaxiang and Chen, Xiaokang and Wang, Jingbo and Zeng, Gang},
    journal = {arXiv preprint arXiv:2205.14870},
    year = {2022}
}

• 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
  }
  

• The framework of NeRF is adapted from nerf_pl:

  @misc{queianchen_nerf,
      author = {Quei-An, Chen},
      title = {Nerf_pl: a pytorch-lightning implementation of NeRF},
      url = {https://github.com/kwea123/nerf_pl/},
      year = {2020},
  }
  

• The NeRF GUI is developed with DearPyGui.