Intrinsic spatial pyramid matching for deformable 3D shape retrieval

Abstract

In this paper, we present an intrinsic spatial pyramid matching approach for 3D shape retrieval. Motivated by the fact that the second eigenfunction of Laplace–Beltrami operator not only can capture the global topological structure information, but also is intrinsic, we propose to adopt its level sets as cuts to perform surface partition. The resulting matching scheme is able to consistently estimate the approximate global geometric correspondence among 3D shapes. In particular, we can leverage recent developments in intrinsic shape analysis and perform intrinsic spatial pyramid matching based on dense spectral shape descriptors such as scale-invariant heat kernel signature. Our experiments demonstrate a significant improvement of 3D shape retrieval on two standard benchmarks.