nerfbaselines 0.0.2

A collection of NeRF baselines for benchmarking

NerfBaselines

The goal of this project is to provide a simple uniform way to benchmark different NeRF methods on standard datasets to allow for an easy comparison. The implemented methods use the original code published by the authors and, therefore, the resulting performance matches the original implementation. DISCLAIMER: This project is at a very early stage of its development. Stay tuned!

Getting started

Start by installing the nerfbaselines pip package on your host system.

pip install nerfbaselines

Now you can use the nerfbaselines cli to interact with NerfBaselines.

The next step is to choose the backend which will be used to install different methods. At the moment there are the following backends implemented:

• docker: Offers good isolation, requires docker to be installed and the user to have access to it (being in the docker user group).
• apptainer: Similar level of isolation as docker, but does not require the user to have privileged access.
• conda (not recommended): Does not require docker/apptainer to be installed, but does not offer the same level of isolation and some methods require additional dependencies to be installed. Also, some methods are not implemented for this backend because they rely on dependencies not found on conda.
• python (not recommended): Will run everything directly in the current environment. Everything needs to be installed in the environment for this backend to work.

The backend can be set as the --backend <backend> argument or using the NB_DEFAULT_BACKEND environment variable.

Training

To start the training use the nerfbaselines train --method <method> command. Use --help argument to learn about all implemented methods and supported features.

Rendering

The nerfbaselines render --checkpoint <checkpoint> command can be used to render images from a trained checkpoint. Again, use --help to learn about the arguments.

Implementation status

Methods:

• Nerfacto
• Instant-NGP
• Gaussian Splatting
• Tetra-NeRF
• Mip-NeRF 360
• NeRF
• Mip-NeRF
• Zip-NeRF

Datasets/features:

• Mip-NeRF 360
• any COLMAP datasets
• HDR images support
• RAW images support
• handling large datasets
• Tanks and Temples
• Blender
• any NerfStudio datasets

Contributing

Contributions are very much welcome. Please open a PR with a dataset/method/feature that you want to contribute. The goal of this project is to slowly expand by implementing more and more methods.

License

This project is licensed under the MIT license.

Thanks

A big thanks to the authors of all implemented methods for the great work they have done.