PyGeometry is a Python package that implements common operations on the differentiable manifolds usually encountered in computer vision and robotics. Implemented manifolds: R^n, S^n, SO(n), SE(n), T(n). Implemented operations: conversion between representations, geodesic distances, interpolation, random sampling. The design goal is to have a set of well-tested primitives: I’ve been burned too many times from having used buggy functions. PyGeometry is paranoid on program correctness. It uses PyContracts to validate input and return values. stochastic_testing (another experimental library) is used to check the correctness of the random sampling operations.
Keywords for this software
References in zbMATH (referenced in 3 articles )
Showing results 1 to 3 of 3.
- Absil, Pierre-Antoine (ed.); Herzog, Roland (ed.); Steidl, Gabriele (ed.): Mini-workshop: Computational optimization on manifolds. Abstracts from the mini-workshop held November 15--21, 2020 (online meeting) (2020)
- Miolane, Nina; Guigui, Nicolas; Le Brigant, Alice; Mathe, Johan; Hou, Benjamin; Thanwerdas, Yann; Heyder, Stefan; Peltre, Olivier; Koep, Niklas; Zaatiti, Hadi; Hajri, Hatem; Cabanes, Yann; Gerald, Thomas; Chauchat, Paul; Shewmake, Christian; Brooks, Daniel; Kainz, Bernhard; Donnat, Claire; Holmes, Susan; Pennec, Xavier: Geomstats: a Python package for Riemannian geometry in machine learning (2020)
- Nina Miolane, Johan Mathe, Claire Donnat, Mikael Jorda, Xavier Pennec: geomstats: a Python Package for Riemannian Geometry in Machine Learning (2018) arXiv