Shape comparison through mutual distances of real functions

Spectral analysis provides a library of shape description elements intrinsically defined by the shape itself. Among all, the eigenfunctions of the Laplace-Beltrami operator can be thought as a set of real valued functions that implicitly abstract and code the shape. In this scenario, this paper introduces a new shape signature derived from the mutual distances between couples of Laplace-Beltrami eigenfunctions. This signature can be seen as a feature vector that acts as an intrinsic shape pattern. Experiments show that it can be effectively used for shape retrieval and its robustness with respect to changes in topology, model resampling, small perturbations and pose variations