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dc.contributor.authorHeider, Paulen_US
dc.contributor.authorPierre-Pierre, Alainen_US
dc.contributor.authorLi, Ruosien_US
dc.contributor.authorGrimm, Cindyen_US
dc.contributor.editorH. Laga and T. Schreck and A. Ferreira and A. Godil and I. Pratikakis and R. Veltkampen_US
dc.date.accessioned2013-04-25T14:10:26Z
dc.date.available2013-04-25T14:10:26Z
dc.date.issued2011en_US
dc.identifier.isbn978-3-905674-31-6en_US
dc.identifier.issn1997-0463en_US
dc.identifier.urihttp://dx.doi.org/10.2312/3DOR/3DOR11/049-056en_US
dc.description.abstractLocal shape descriptors can be used for a variety of tasks, from registration to comparison to shape analysis and retrieval. There have been a variety of local shape descriptors developed for these tasks, which have been evaluated in isolation or in pairs, but not against each other. We provide a survey of existing descriptors and a framework for comparing them. We perform a detailed evaluation of the descriptors using real data sets from a variety of sources. We first evaluate how stable these metrics are under changes in mesh resolution, noise, and smoothing. We then analyze the discriminatory ability of the descriptors for the task of shape matching. Our conclusion is that sampling the normal distribution and the mean curvature, using 25 samples, and reducing this data to 5-10 samples via Principal Components Analysis provides robustness to noise and the best shape discrimination results.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectCategories and Subject Descriptors (according to ACM CCS): I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling -Curve, surface, solid, and object representationsen_US
dc.titleLocal Shape Descriptors, a Survey and Evaluationen_US
dc.description.seriesinformationEurographics Workshop on 3D Object Retrievalen_US
dc.description.sectionheadersLocal Shape Descriptorsen_US


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