Structural MAT: Clean and Scalable Medial Axis Simplification via Explicit Surface Correspondence

TL;DR

This paper introduces a scalable method for medial axis transform (MAT) simplification that explicitly maintains surface correspondence, resulting in high-quality, structurally aligned skeletal representations of complex shapes.

Contribution

It presents a novel approach that tracks surface correspondence during MAT simplification, improving structural accuracy and robustness over existing methods.

Findings

Outperforms state-of-the-art algorithms in efficiency and structural quality.

Produces highly expressive MATs with only a few hundred vertices.

Maintains robustness to noise and handles complex geometries effectively.

Abstract

The Medial Axis Transform (MAT) is a complete shape descriptor capable of reconstructing the geometry of the original domain. A high-quality MAT should not only facilitate high-fidelity reconstruction but also capture structural features -- for instance, by aligning the MAT boundary with the locus of rolling ball centers within fillet regions. However, computing such an ideal MAT remains a significant challenge, particularly when the input is a discrete triangle mesh. In this paper, we follow the established technical pipeline of initializing the MAT via a 3D Voronoi diagram of surface samples and subsequently simplifying the Voronoi structure through a QEM-like scheme. Our key insight is to explicitly track the correspondence between MAT vertices and surface regions throughout the progressive simplification process, ensuring that the resulting MAT triangles accurately reflect the intrinsic symmetries between surface patches. We translate these geometric requirements into a suite of priority control strategies that govern the sequencing of edge collapses. Through extensive evaluation against state-of-the-art MAT algorithms, we validate the strong performance of our approach regarding runtime efficiency, structural alignment, boundary regularity, triangle quality, and robustness to noise. Our resulting MATs remain highly expressive for both articulated shapes and CAD models, even under extreme simplification -- effectively capturing the global structure of complex geometries with only a few hundred vertices.