Global and local isometry-invariant descriptor for 3D shape comparison and partial matching

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 meanvalue 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 isometryinvariant, 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 featuresets, 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.

• Type: Journal
• Year: 2010
• Where: CVPR
• Authors: Huai-Yu Wu, Hongbin Zha, Tao Luo, Xulei Wang, Songde Ma
• Keywords: Computer Vision, CVPR 2010, Harmonic, Manifold Harmonic Bases, Shape Similarity Comparison