no-cuda-ngp-nerf

An educational implementation of Instant NGP NeRF using JAX and Numba instead of custom CUDA kernels.

Currently a work-in-progress.

Notes

• Checkout derivation_of_derivatives.md to see how the volume rendering derivatives are derived.

Installation

First clone the repository.

https://github.com/hayden-donnelly/no-cuda-ngp-nerf.git

Then cd into it and install with pip.

cd no-cuda-ngp-nerf

python3 -m pip install -e .

The -e flag will let you edit the project without having to reinstall.

Development Environment

Docker

• To build the docker image, use docker-compose build.
• To start the docker container, use docker-compose up -d.
• To open a shell inside the container, use docker-compose exec ngp bash.
• To open Jupyter Lab inside the container instead of a shell, go to http://localhost:7070/.
• To stop the container, use docker-compose down.

Nix

TODO

TODO

• Switch to a Nix development environment.
• Add occupancy grid bitfield.
• Add screenshot/video of a trained NeRF render.

Acknowledgement

Kwea123's video lecture and PyTorch implementation of Instant NGP were both very helpful during the development of this project.

Citation

@article{mueller2022instant,
    author={Thomas M\"uller and Alex Evans and Christoph Schied and Alexander Keller},
    title={Instant Neural Graphics Primitives with a Multiresolution Hash Encoding},
    journal={ACM Trans. Graph.},
    issue_date={July 2022},
    volume={41},
    number={4},
    month=jul,
    year={2022},
    pages={102:1--102:15},
    articleno={102},
    numpages={15},
    url={https://doi.org/10.1145/3528223.3530127},
    doi={10.1145/3528223.3530127},
    publisher={ACM},
    address={New York, NY, USA},
}