mbrukman / nerf-pytorch

A PyTorch re-implementation of Neural Radiance Fields

nerf-pytorch

A PyTorch re-implementation

Project | Video | Paper

A PyTorch re-implementation of Neural Radiance Fields.

Tiny-NeRF on Google Colab

The NeRF code release has an accompanying Colab notebook, that showcases training a feature-limited version of NeRF on a "tiny" scene. It's equivalent PyTorch notebook can be found at the following URL:

https://colab.research.google.com/drive/1rO8xo0TemN67d4mTpakrKrLp03b9bgCX

How to train your NeRF

To train a "full" NeRF model (i.e., using 3D coordinates as well as ray directions, and the hierarchical sampling procedure), first setup dependencies. In a new conda or virtualenv environment, run

pip install requirements.txt

Importantly, install torchsearchsorted by following instructions from their README.

Once everything is setup, to run experiments, first edit config/default.yml to specify your own parameters.

The training script can be invoked by running

python train_nerf.py --config config/default.yml

Optionally, if resuming training from a previous checkpoint, run

python train_nerf.py --config config/default.yml --load-checkpoint path/to/checkpoint.ckpt

(Full) NeRF on Google Colab

A Colab notebook for the full NeRF model (albeit on low-resolution data) can be accessed here.

A note on reproducibility

All said, this is not an official code release, and is instead a reproduction from the original code (released by the authors here).

I have currently ensured (to the best of my abilities, but feel free to open issues if you feel something's wrong :) ) that

• Every individual module exactly (numerically) matches that of the TensorFlow implementation. This Colab notebook has all the tests, matching op for op (but is very scratchy to look at)!
• Training works as expected for fairly small resolutions (100 x 100).

However, this implementation still lacks the following:

• I have not run all the full experiments devised in the paper.
• I've only tested on the lego sequence of the synthetic (Blender) datasets.

The organization of code WILL change around a lot, because I'm actively experimenting with this.

Pretrained models: I am running a few large-scale experiments, and I hope to release models sometime in the end of April.

Contributing / Issues?

Feel free to raise GitHub issues if you find anything concerning. Pull requests adding additional features are welcome too.

LICENSE

nerf-pytorch is available under the MIT License. For more details see: LICENSE and ACKNOWLEDGEMENTS.