Skeletonization via Local Separators

TL;DR

This paper introduces a robust, detail-preserving curve skeletonization algorithm based on local separators, applicable to various shape representations like meshes, volumes, and point clouds.

Contribution

It presents a novel skeletonization method leveraging local separators, improving detail capture and robustness across diverse shape data types.

Findings

Effective on meshes, volumetric shapes, and point clouds

Outperforms existing methods in performance and quality

Simple pipeline from geometric data to skeleton

Abstract

We propose a new algorithm for curve skeleton computation which differs from previous algorithms by being based on the notion of local separators. The main benefits of this approach are that it is able to capture relatively fine details and that it works robustly on a range of shape representations. Specifically, our method works on shape representations that can be construed as a spatially embedded graphs. Such representations include meshes, volumetric shapes, and graphs computed from point clouds. We describe a simple pipeline where geometric data is initially converted to a graph, optionally simplified, local separators are computed and selected, and finally a skeleton is constructed. We test our pipeline on polygonal meshes, volumetric shapes, and point clouds. Finally, we compare our results to other methods for skeletonization according to performance and quality.