Matching Humans with Different Connectivity

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dc.contributor.authorMelzi, S.en_US
dc.contributor.authorMarin, R.en_US
dc.contributor.authorRodolĂ , E.en_US
dc.contributor.authorCastellani, U.en_US
dc.contributor.authorRen, J.en_US
dc.contributor.authorPoulenard, A.en_US
dc.contributor.authorWonka, P.en_US
dc.contributor.authorOvsjanikov, M.en_US
dc.contributor.editorBiasotti, Silvia and Lavoué, Guillaume and Veltkamp, Remcoen_US
dc.date.accessioned2019-05-04T14:06:07Z
dc.date.available2019-05-04T14:06:07Z
dc.date.issued2019
dc.description.abstractObjects Matching is a ubiquitous problem in computer science with particular relevance for many applications; property transfer between 3D models and statistical study for learning are just some remarkable examples. The research community spent a lot of effort to address this problem, and a large and increased set of innovative methods has been proposed for its solution. In order to provide a fair comparison among these methods, different benchmarks have been proposed. However, all these benchmarks are domain specific, e.g., real scans coming from the same acquisition pipeline, or synthetic watertight meshes with the same triangulation. To the best of our knowledge, no cross-dataset comparisons have been proposed to date. This track provides the first matching evaluation in terms of large connectivity changes between models that come from totally different modeling methods. We provide a dataset of 44 shapes with dense correspondence as obtained by a highly accurate shape registration method (FARM). Our evaluation proves that connectivity changes lead to Objects Matching difficulties and we hope this will promote further research in matching shapes with wildly different connectivity.en_US
dc.description.sectionheadersSHREC Session 2
dc.description.seriesinformationEurographics Workshop on 3D Object Retrieval
dc.identifier.doi10.2312/3dor.20191070
dc.identifier.isbn978-3-03868-077-2
dc.identifier.issn1997-0471
dc.identifier.pages121-128
dc.identifier.urihttps://doi.org/10.2312/3dor.20191070
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/3dor20191070
dc.publisherThe Eurographics Associationen_US
dc.subjectComputing methodologies
dc.subjectShape analysis
dc.subjectTheory of computation
dc.subjectComputational geometry
dc.titleMatching Humans with Different Connectivityen_US
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