pyRad🤘

The all-in-one repo for NeRFs

• Quickstart
• Supported Features
• Benchmarked Model Architectures

Quickstart

The quickstart will help you get started with the default vanilla nerf trained on the classic blender lego scene. For more complex changes (e.g. running with your own data/ setting up a new NeRF graph, please see our docs.

1. Installation: Setup the environment

This repository is tested with cuda 11.3

# Clone the repo
git clone --recurse-submodules git@github.com:plenoptix/pyrad.git

# Create the python environment
conda create --name pyrad python=3.8.13
conda activate pyrad
pip install -r environment/requirements.txt

# Install pyrad as a library
pip install -e .

# Install library with CUDA support. Change setup.py to `USE_CUDA = True` and then
python setup.py develop

# Running the test cases
pytest tests

2. Getting the data

Download the original NeRF dataset and unfold it in the following format:

├── data/
|   ├── blender/
|   |   ├── fern/
|   |   ├── lego/
...

3. Training a model

To run with all the defaults, e.g. vanilla nerf method with the blender lego image:

# Run with default config
python scripts/run_train.py

With support for Hydra, you can run with other configurations by changing appropriate configs defined in configs/ or by setting flags via command-line arguments:

# Run with config changes
python scripts/run_train.py machine.num_gpus=1
python scripts/run_train.py data.dataset.downscale_factor=1

# Run with different datasets
python scripts/run_train.py data/dataset=blender_lego
python scripts/run_train.py data/dataset=friends_TBBT-big_living_room

# Run with different datasets and config changes
python scripts/run_train.py data/dataset=friends_TBBT-big_living_room graph.far_plane=14

# [Experimental] Speed up the dataloading pipeline by caching DatasetInputs.
python scripts/run_data_preprocessor.py
# Then, specify using the cache.
python scripts/run_train.py ++data.dataset.use_cache=true

4. Visualizing training runs

If you run everything with the default configuration, by default, we use TensorBoard to log all training curves, test images, and other stats. Once the job is launched, you will be able to track training by launching the tensorboard in outputs/blender_lego/vanilla_nerf/<timestamp>/<events.tfevents>.

tensorboard --logdir outputs/blender_lego/vanilla_nerf/

5. Rendering a trajectories during inference

TODO(ethan)

6. In-depth guide

For a more in-depth tutorial on how to modify/implement your own NeRF Graph, please see our walk-through.

Supported Features

We provide the following support strucutures to make life easier for getting started with NeRFs. For a full description, please refer to our features page.

If you are looking for a feature that is not currently supported, please do not hesitate to contact the Plenoptix team!

🤘 Support for Hydra config structure

🤘 Support for multiple logging interfaces

🤘 Built-in support for profiling code

🤘 Benchmarking scripts

🤘 Easily run other repos with our data

🤘 Speed up your code with Tiny Cuda

🤘 Support for Jupyter

Benchmarked Model Architectures

Method	PSNR
NeRF	⏳
instant NGP	⏳
Mip NeRF	⏳