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

arXiv: https://arxiv.org/abs/2010.11488 · HTML: https://mlsanity.com/paper/2010.11488/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2020.

## Built on by (most cited first, 7 of 7)

- [Unsupervised Learning for Cuboid Shape Abstraction via Joint Segmentation from Point Clouds](https://mlsanity.com/paper/2106.03437/) (2021)
- [Coverage Axis++: Efficient Inner Point Selection for 3D Shape Skeletonization](https://mlsanity.com/paper/2401.12946/) (2024)
- [Medial Spectral Coordinates for 3D Shape Analysis](https://mlsanity.com/paper/2111.13295/) (2021)
- [The Sum of Its Parts: Visual Part Segmentation for Inertial Parameter Identification of Manipulated Objects](https://mlsanity.com/paper/2302.06685/) (2023)
- [MARF: The Medial Atom Ray Field Object Representation](https://mlsanity.com/paper/2307.00037/) (2023)
- [Aligning Instance-Semantic Sparse Representation towards Unsupervised Object Segmentation and Shape Abstraction with Repeatable Primitives](https://mlsanity.com/paper/2503.06947/) (2025)
- [Deep Medial Voxels: Learned Medial Axis Approximations for Anatomical Shape Modeling](https://mlsanity.com/paper/2403.11790/) (2024)

## Abstract (arXiv)

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.