RoMa: A lightweight library to deal with 3D rotations in PyTorch.

RoMa (which stands for Rotation Manipulation) provides differentiable mappings between 3D rotation representations, mappings from Euclidean to rotation space, and various utilities related to rotations.

It is implemented in PyTorch and aims to be an easy-to-use and reasonably efficient toolbox for Machine Learning and gradient-based optimization.

Documentation

Latest documentation is available here: https://naver.github.io/roma/.

Installation

The easiest way to install RoMa consists in using pip:

pip install roma

We also recommend installing torch-batch-svd to achieve significant speed-up with special_procrustes function on a CUDA GPU.

Alternatively one can install RoMa directly from source repository:

pip install git+https://github.com/naver/roma

or include the source repository (https://github.com/naver/roma) as a Git submodule.

License

RoMa, Copyright (c) 2021 NAVER Corp., is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 license (see license).

Bits of code were adapted from SciPy. Documentation is generated, distributed and displayed with the support of Sphinx and other materials (see notice).

References

For a more in-depth discussion regarding differentiable mappings on the rotation space, please refer to:

• Romain Brégier, Deep Regression on Manifolds: a 3D Rotation Case Study. in 2021 International Conference on 3D Vision (3DV), 2021.

Please cite this work in your publications:

@inproceedings{bregier2021deepregression,
	title={Deep Regression on Manifolds: a {3D} Rotation Case Study},
	author={Br{\'e}gier, Romain},
	journal={2021 International Conference on 3D Vision (3DV)},
	year={2021}
}