P2MAT-NET: Learning medial axis transform from sparse point clouds

Abstract

The medial axis transform (MAT) of a 3D shape includes the set of centers and radii of the maximally inscribed spheres, and is a complete shape descriptor that can be used to reconstruct the original shape. It is a compact representation that jointly describes geometry, topology, and symmetry properties of a given shape. In this work, we present P2MAT-NET, a neural network which learns the pattern of sparse point clouds and transform them into spheres approximating MAT. The experimental results illustrate that P2MAT-NET demonstrates better performance than state-of-the-art methods in computing MAT from point clouds, in terms of MAT quality to approximate the 3D shapes. The computed MAT can be used as an intermediate descriptor for downstream applications such as 3D shape recognition from point clouds. Our results show that it can achieve competitive performance in recognition with state-of-the-art methods.