Hausdorff and Gromov-Hausdorff stable subsets of the medial axis

Abstract

In this paper we introduce a pruning of the medial axis called the $(\lambda ,\alpha )$-medial axis (${\text{ax}}_{\lambda }^{\alpha }$). We prove that the $(\lambda ,\alpha )$-medial axis of a set $K$ is stable in a Gromov-Hausdorff sense under weak assumptions. More formally we prove that if $K$ and $K^{\prime }$ are close in the Hausdorff ($d_H$) sense then the $(\lambda ,\alpha )$-medial axes of $K$ and $K^{\prime }$ are close as metric spaces, that is the Gromov-Hausdorff distance ($d_{GH}$) between the two is $\frac{1}{4}$-H{ö}lder in the sense that $d_{GH}({\text{ax}}_{\lambda }^{\alpha }(K),{\text{ax}}_{\lambda }^{\alpha }(K^{\prime }))\lesssim d_H(K,K^{\prime }{)}^{1/4}$. The Hausdorff distance between the two medial axes is also bounded, by $d_H({\text{ax}}_{\lambda }^{\alpha }(K),{\text{ax}}_{\lambda }^{\alpha }(K^{\prime }))\lesssim d_H(K,K^{\prime }{)}^{1/2}$. These quantified stability results provide guarantees for practical computations of medial axes from approximations. Moreover, they provide key ingredients for studying the computability of the medial axis in the context of computable analysis.

Full version of a conference paper (with the same name) that was accepted for STOC'23. The conference version will be available at https://doi.org/10.1145/3564246.3585113 around the time the conference will take place