Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
439811 | Computer-Aided Design | 2007 | 10 Pages |
Abstract
We present an approach for robust shape retrieval from databases containing articulated 3D models. Each shape is represented by the eigenvectors of an appropriately defined affinity matrix, forming a spectral embedding which achieves normalization against rigid-body transformations, uniform scaling, and shape articulation (i.e., bending). Retrieval is performed in the spectral domain using global shape descriptors. On the McGill database of articulated 3D shapes, the spectral approach leads to an absolute improvement in retrieval performance for both the spherical harmonic and the light field shape descriptors. The best retrieval results are obtained using a simple and novel eigenvalue-based descriptor we propose.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Graphics and Computer-Aided Design
Authors
Varun Jain, Hao Zhang,