Global and Local Isometry-Invariant Descriptor for 3D Shape Comparison and Partial Matching

Abstract

In this paper, based on manifold harmonics, we propose a novel framework for 3D shape similarity comparison and partial matching. First, we propose a novel symmetric mean-value representation to robustly construct high-quality manifold harmonic bases on nonuniform-sampling meshes. Then, based on the manifold harmonic bases constructed, a novel shape descriptor is presented to capture both of global and local features of 3D shape. This feature descriptor is isometry-invariant, i.e., invariant to rigid-body transformations and non-rigid bending. After characterizing 3D models with the shape features, we perform 3D retrieval with a up-to-date discriminative kernel. This kernel is a dimension-free approach to quantifying the similarity between two unordered feature-sets, thus especially suitable for our high-dimensional feature data. Experimental results show that our framework can be effectively used for both comprehensive comparison and partial matching among non-rigid 3D shapes.