nerf-pytorch

A PyTorch re-implementation

Project | Video | Paper

NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
Ben Mildenhall*¹, Pratul P. Srinivasan*¹, Matthew Tancik*¹, Jonathan T. Barron², Ravi Ramamoorthi³, Ren Ng^1
1UC Berkeley, ²Google Research, ³UC San Diego
*denotes equal contribution

A PyTorch re-implementation of Neural Radiance Fields.

The current implementation is blazingly fast! (Thorough benchmark to come, but ~2x faster)

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

What is a NeRF?

A neural radiance field is a simple fully connected network (weights are ~5MB) trained to reproduce input views of a single scene using a rendering loss. The network directly maps from spatial location and viewing direction (5D input) to color and opacity (4D output), acting as the "volume" so we can use volume rendering to differentiably render new views.

Optimizing a NeRF takes between a few hours and a day or two (depending on resolution) and only requires a single GPU. Rendering an image from an optimized NeRF takes somewhere between less than a second and ~30 seconds, again depending on resolution.

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/lego.yml to specify your own parameters.

The training script can be invoked by running

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

Optionally, if resuming training from a previous checkpoint, run

python train_nerf.py --config config/lego.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.

Render fun videos

Once you've trained your NeRF, it's time to use that to render the scene. Use the eval_nerf.py script to do that.

python eval_nerf.py --config logs/experiment_id/config.yml --checkpoint logs/experiment_id/checkpoint100000.ckpt --savedir cache/rendered/experiment_id

You can create a gif out of the saved images, for instance, by using Imagemagick.

convert cache/rendered/experiment_id/*.png cache/rendered/experiment_id.gif

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).

The code is thoroughly tested (to the best of my abilities) to match the original implementation (and be much faster)! In particular, I have ensured 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 Lego and LLFF scenes).

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.

Misc

Also, a shoutout to yenchenlin for his cool PyTorch implementation, whose volume rendering function replaced mine (my initial impl was inefficient in comparison).