错误:搜索内容不能为空,请输入英文关键词
错误:关键词超出字数限制,请精简
高级检索

Demystifying Latschev’s Theorem: Manifold Reconstruction from Noisy Data

  • Sushovan Majhi

摘要

For a closed Riemannian manifold \(\mathcal {M}\) M and a metric space S with a small Gromov–Hausdorff distance to it, Latschev’s theorem guarantees the existence of a sufficiently small scale \(\beta >0\) β > 0 at which the Vietoris–Rips complex of S is homotopy equivalent to \(\mathcal {M}\) M . Despite being regarded as a stepping stone to the topological reconstruction of Riemannian manifolds from noisy data, the result is only a qualitative guarantee. Until now, it had been elusive how to quantitatively choose such a proximity scale \(\beta \) β in order to provide sampling conditions for S to be homotopy equivalent to \(\mathcal {M}\) M . In this paper, we prove a stronger and pragmatic version of Latschev’s theorem, facilitating a simple description of \(\beta \) β using the sectional curvatures and convexity radius of \(\mathcal {M}\) M as the sampling parameters. Our study also delves into the topological recovery of a closed Euclidean submanifold from the Vietoris–Rips complexes of a Hausdorff close Euclidean subset. As already known for Čech complexes, we show that Vietoris–Rips complexes also provide topologically faithful reconstruction guarantees for submanifolds.