SEG-MAT: 3D Shape Segmentation Using Medial Axis Transform

TL;DR

SEG-MAT is an efficient 3D shape segmentation method leveraging the medial axis transform to identify structural junctions, outperforming existing methods in quality and speed.

Contribution

The paper introduces SEG-MAT, a novel shape segmentation approach using medial axis transform for improved efficiency and structural understanding.

Findings

Outperforms state-of-the-art in segmentation quality

One order of magnitude faster than existing methods

Effectively identifies junctions between shape parts

Abstract

Segmenting arbitrary 3D objects into constituent parts that are structurally meaningful is a fundamental problem encountered in a wide range of computer graphics applications. Existing methods for 3D shape segmentation suffer from complex geometry processing and heavy computation caused by using low-level features and fragmented segmentation results due to the lack of global consideration. We present an efficient method, called SEG-MAT, based on the medial axis transform (MAT) of the input shape. Specifically, with the rich geometrical and structural information encoded in the MAT, we are able to develop a simple and principled approach to effectively identify the various types of junctions between different parts of a 3D shape. Extensive evaluations and comparisons show that our method outperforms the state-of-the-art methods in terms of segmentation quality and is also one order of magnitude faster.