A lightweight library to deal with 3D rotations in PyTorch.
Project description
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/.
We test RoMa with PyTorch versions 1.6.0 and latest:
Installation
The easiest way to install RoMa is to use 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 the latest version of RoMa directly from the 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:
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}
}
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