Mesh Statistics for Robust Curvature Estimation

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dc.contributor.authorVáša, Liboren_US
dc.contributor.authorVaněček, Petren_US
dc.contributor.authorPrantl, Martinen_US
dc.contributor.authorSkorkovská, Věraen_US
dc.contributor.authorMartínek, Petren_US
dc.contributor.authorKolingerová, Ivanaen_US
dc.contributor.editorMaks Ovsjanikov and Daniele Panozzoen_US
dc.date.accessioned2016-06-17T14:12:12Z
dc.date.available2016-06-17T14:12:12Z
dc.date.issued2016en_US
dc.description.abstractWhile it is usually not difficult to compute principal curvatures of a smooth surface of sufficient differentiability, it is a rather difficult task when only a polygonal approximation of the surface is available, because of the inherent ambiguity of such representation. A number of different approaches has been proposed in the past that tackle this problem using various techniques. Most papers tend to focus on a particular method, while an comprehensive comparison of the different approaches is usually missing. We present results of a large experiment, involving both common and recently proposed curvature estimation techniques, applied to triangle meshes of varying properties. It turns out that none of the approaches provides reliable results under all circumstances. Motivated by this observation, we investigate mesh statistics, which can be computed from vertex positions and mesh connectivity information only, and which can help in deciding which estimator will work best for a particular case. Finally, we propose a meta-estimator, which makes a choice between existing algorithms based on the value of the mesh statistics, and we demonstrate that such meta-estimator, despite its simplicity, provides considerably more robust results than any existing approach.en_US
dc.description.number5en_US
dc.description.sectionheadersDifferential Propertiesen_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.description.volume35en_US
dc.identifier.doi10.1111/cgf.12982en_US
dc.identifier.issn1467-8659en_US
dc.identifier.pages271-280en_US
dc.identifier.urihttps://doi.org/10.1111/cgf.12982en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectI.3.5 [Computer Graphics]en_US
dc.subjectComputational Geometry and Object Modelingen_US
dc.subjectGeometric algorithmsen_US
dc.subjectlanguagesen_US
dc.subjectand systemsen_US
dc.subjectcurveen_US
dc.subjectsurfaceen_US
dc.subjectsoliden_US
dc.subjectand object representationsen_US
dc.titleMesh Statistics for Robust Curvature Estimationen_US
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35-Issue 5
SGP16: Eurographics Symposium on Geometry Processing (CGF 35-5)