This paper presents a new method for 2-D and 3-D shape retrieval based on geodesic signatures. These signatures are high dimensional statistical distributions computed by extracting several features from the set of geodesic distance maps to each point. The resulting high dimensional distributions are matched to perform retrieval using a fast approximate Wasserstein metric. This allows to propose a unifying framework for the compact description of planar shapes and 3-D surfaces. © 2010 Springer-Verlag.
CITATION STYLE
Rabin, J., Peyré, G., & Cohen, L. D. (2010). Geodesic shape retrieval via optimal mass transport. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6315 LNCS, pp. 771–784). Springer Verlag. https://doi.org/10.1007/978-3-642-15555-0_56
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