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.

翻译：本文旨在解决利用Kendall三维形状空间分析几何结构时面临的挑战。尽管黎曼几何为形状分析（独立于尺度、位置和方向）提供了稳健的理论框架，但从理论流形向实际计算工作流的转化仍存在困难。虽然Geomstats是目前基于流形统计学的领先Python库，但其缺乏高级三维形状分析所需的特定工具。本文介绍了为填补这一空白而设计的工具，将复杂的数学抽象转化为高效、易用的软件解决方案，以服务于研究人员。