This is a JAX implementation of Neural Radiance Fields for learning purposes.

I've been curious about NeRF and its follow-up work for a while, but don't have much time to explore it. I learn best by doing, so I'll be implementing stuff here to try to get a feel for it.

The steps to using this codebase are as follows:

1. Generate a dataset - run a simple Go program to turn any .stl 3D model into a series of rendered camera views with associated metadata.
2. Train a model - install the Python dependencies and run the training script.
3. Render a novel view - render a novel view of the object using a model.

I use a simple format for storing rendered views of the scene. Each frame is stored as a PNG file, and each PNG has an accompanying JSON file describing the camera view.

For easy experimentation, I created a Go program to render an arbitrary .stl file as a collection of views in the supported data format. To run this program, install Go and run go get . inside of simple_dataset/ to get the dependencies. Next, run

$ go run . /path/to/model.stl data_dir

This will create a directory data_dir containing rendered views of /path/to/model.stl.

First, install the learn_nerf package by running pip install -e . inside this repository. You should separately make sure jax and Flax are installed in your environment.

The training script is learn_nerf/scripts/train_nerf.py. Here's an example of running this script:

python learn_nerf/scripts/train_nerf.py \
    --lr 1e-5 \
    --batch_size 1024 \
    --save_path model_weights.pkl \
    /path/to/data_dir

This will periodically save model weights to model_weights.pkl. The script may get stuck on training... while it shuffles the dataset and compiles the training graph. Wait a minute or two, and losses should start printing out as training ramps up.

If you get a Segmentation fault on CPU, this may be because you don't have enough memory to run batch size 1024--try something lower.

To render a view from a trained NeRF model, use learn_nerf/scripts/render_nerf.py. Here's an example of the usage:

python learn_nerf/scripts/render_nerf.py \
    --batch_size 1024 \
    --model_path model_weights.pkl \
    --width 128 \
    --height 128 \
    /path/to/data_dir/0000.json \
    output.png

In the above example, we will render the camera view described by /path/to/data_dir/0000.json. Note that the camera view can be from the training set, but doesn't need to be as long as its in the correct JSON format.