Metadata-Version: 2.1
Name: roma
Version: 1.2.1
Summary: A lightweight library to deal with 3D rotations in PyTorch.
Home-page: https://github.com/naver/roma
Author: Romain Brégier
Author-email: romain.bregier@naverlabs.com
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: Free for non-commercial use
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE
License-File: NOTICE

# *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](https://github.com/KinglittleQ/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](https://github.com/naver/roma/blob/master/LICENSE)).

Bits of code were adapted from SciPy. Documentation is generated, distributed and displayed with the support of Sphinx and other materials (see [notice](https://github.com/naver/roma/blob/master/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.__ _Technical report arXiv 2103.16317_, 2021.](https://arxiv.org/abs/2103.16317)
```
@article{bregier2021deep,
	title={Deep regression on manifolds: a {3D} rotation case study},
	author={Br{\'e}gier, Romain},
	journal={arXiv preprint arXiv:2103.16317},
	year={2021}
}
```

For *RoMa*-related inquiries, feel free to get in touch using GitHub issues.




