PlenOctrees_NeRF-SH

This is an implementation of the Paper PlenOctrees for Real-time Rendering of Neural Radiance Fields. Not only the code provides the implementation of the NeRF-SH，but also provides the conversion code from NeRF-SH to PlenOctree. You can use the code to generate the .npz file so as to run the C++ renderer by the PlenOctrees for Real-time Rendering of Neural Radiance Fields. And the conversion code is in the tools/PlenOctrees.ipynb. But before using the code, you must train the NeRF-SH model. If you don't want to train the model, please concat the mail:[email protected].

Quick Start

The implementation of dataloader is from the Multi-view Neural Human Rendering (NHR). So the datasets format should be the same as theNHR.
To train the code:

    
cd tools && python train_net.py <gpu id>     

And you can run the tools/PlenOctrees.ipynb to generate the .npz file which can run the C++ renderer by the PlenOctrees for Real-time Rendering of Neural Radiance Fields.

Requirements

• yacs (Yet Another Configuration System)

• PyTorch (An open source deep learning platform)

• ignite (High-level library to help with training neural networks in PyTorch)

• If you have any questions, you can contact [email protected].

Citation

@inproceedings{yu2021plenoctrees,
      title={PlenOctrees for Real-time Rendering of Neural Radiance Fields},
      author={Alex Yu and Ruilong Li and Matthew Tancik and Hao Li and Ren Ng and Angjoo Kanazawa},
      year={2021},
      booktitle={arXiv},
}