Spherical CNNs

Equivariant CNNs for the sphere and SO(3) implemented in PyTorch

Overview

This library contains a PyTorch implementation of the SO(3) equivariant CNNs for spherical signals (e.g. omnidirectional cameras, signals on the globe) as presented in [1].

Dependencies

• PyTorch: http://pytorch.org/
• cupy: https://github.com/cupy/cupy
• lie_learn: https://github.com/AMLab-Amsterdam/lie_learn
• pynvrtc: https://github.com/NVIDIA/pynvrtc

Installation

To install, run

$ python setup.py install

Structure

• nn: PyTorch nn.Modules for the S(2) and SO(3) CNN layers
• ops: Low-level operations used for computing the FFT
• examples: Example code for using the library within a PyTorch project

Usage

Please have a look into the examples.

Please cite [1] in your work when using this library in your experiments.

Feedback

For questions and comments, feel free to contact Taco Cohen.

License

MIT

References

[1] Taco Cohen, Mario Geiger, Jonas Köhler, Max Welling (2017). 
Convolutional Networks for Spherical Signals. 
In ICML Workshop on Principled Approaches to Deep Learning.