SEG-MAT: 3D Shape Segmentation Using Medial Axis Transform
This solves the problem of efficient and high-quality 3D shape segmentation for computer graphics applications, but it appears incremental as it builds on the medial axis transform.
The paper tackled the problem of segmenting 3D objects into meaningful parts by addressing issues like complex geometry processing and fragmented results in existing methods, resulting in a method that outperforms state-of-the-art in segmentation quality and is one order of magnitude faster.
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.