Coverage Axis: Inner Point Selection for 3D Shape Skeletonization

TL;DR

This paper introduces Coverage Axis, a novel shape skeletonization method that efficiently covers surface points with medial balls, producing robust, compact skeletal representations from various 3D inputs by leveraging a global set cover formulation.

Contribution

It proposes a new global set cover-based formulation for 3D shape skeletonization that improves robustness and handles diverse input types unlike previous local-error methods.

Findings

Outperforms existing methods in producing compact skeletons

Effective on point clouds and poor-quality meshes

Demonstrates robustness and efficiency in evaluations

Abstract

In this paper, we present a simple yet effective formulation called Coverage Axis for 3D shape skeletonization. Inspired by the set cover problem, our key idea is to cover all the surface points using as few inside medial balls as possible. This formulation inherently induces a compact and expressive approximation of the Medial Axis Transform (MAT) of a given shape. Different from previous methods that rely on local approximation error, our method allows a global consideration of the overall shape structure, leading to an efficient high-level abstraction and superior robustness to noise. Another appealing aspect of our method is its capability to handle more generalized input such as point clouds and poor-quality meshes. Extensive comparisons and evaluations demonstrate the remarkable effectiveness of our method for generating compact and expressive skeletal representation to approximate the MAT.