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

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.

arXiv commentIEEE Transactions on Visualization and Computer Graphics (TVCG), to appear

References in corpus (4)

• ShapeNet: An Information-Rich 3D Model Repository
• Robust Watertight Manifold Surface Generation Method for ShapeNet Models
• SCORES: Shape Composition with Recursive Substructure Priors
• Modeling 3D Shapes by Reinforcement Learning

Cited by in corpus (6)

• Unsupervised Learning for Cuboid Shape Abstraction via Joint Segmentation from Point Clouds
• MARF: The Medial Atom Ray Field Object Representation
• The Sum of Its Parts: Visual Part Segmentation for Inertial Parameter Identification of Manipulated Objects
• Point2Skeleton: Learning Skeletal Representations from Point Clouds
• Aligning Instance-Semantic Sparse Representation towards Unsupervised Object Segmentation and Shape Abstraction with Repeatable Primitives
• Deep Medial Voxels: Learned Medial Axis Approximations for Anatomical Shape Modeling