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Graph-based 3D object classification

In this paper, we propose a novel method for the classification of 3D shapes, based on topo-geometric shape descriptors. Topo-geometric models have an advantage over existing shape descriptors that they capture complete shape information - capturing topology through skeletal graphs, and geometry via edge weights. The resulting weighted graph representation allows shape classification by establishing error correcting subgraph isomorphisms between the test graph and model graphs, where the best match is the one that corresponds to largest subgraph isomorphism. We propose various cost assignments for graph edit operations for error correction, which in turn takes into account any shape variations arising due to noise and measurement errors.