68
GitHub - jonas-koehler/s2cnn: Spherical CNNs
source link: https://github.com/jonas-koehler/s2cnn
Go to the source link to view the article. You can view the picture content, updated content and better typesetting reading experience. If the link is broken, please click the button below to view the snapshot at that time.
README.md
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.
Recommend
About Joyk
Aggregate valuable and interesting links.
Joyk means Joy of geeK