This work addresses the challenge of analyzing geometric structures using Kendall's 3D Shape Space. While Riemannian geometry provides a robust framework for shape analysis (independent of scale, position, and orientation) the transition from theoretical manifolds to practical computational workflows remains difficult. Although Geomstats is currently the leading Python library for manifold-based statistics, it lacks specific utilities required for advanced 3D shape analysis. This article introduces tools designed to bridge this gap, translating complex mathematical abstractions into efficient, accessible software solutions for researchers.
@article{arxiv.2603.10687,
title = {A Python implementation of some geometric tools on Kendall 3D shape space for practical applications},
author = {Jorge Valero and Vicent Gimeno i Garcia and M. Victoría Ibáñez and Pau Martinavarro and Amelia Simó},
journal= {arXiv preprint arXiv:2603.10687},
year = {2026}
}