An Adaptive MLS Surface for Reconstruction with Guarantees

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dc.contributor.authorDey, Tamal K.en_US
dc.contributor.authorSun, Jianen_US
dc.contributor.editorMathieu Desbrun and Helmut Pottmannen_US
dc.date.accessioned2014-01-29T09:31:06Z
dc.date.available2014-01-29T09:31:06Z
dc.date.issued2005en_US
dc.description.abstractRecent work have shown that moving least squares (MLS) surfaces can be used effectively to reconstruct surfaces from possibly noisy point cloud data. Several variants of MLS surfaces have been suggested, some of which have been analyzed theoretically for guarantees. These analyses, so far, have assumed uniform sampling density. We propose a new variant of the MLS surface that, for the first time, incorporates local feature sizes in its formulation, and we analyze it for reconstruction guarantees using a non-uniform sampling density. The proposed variant of the MLS surface has several computational advantages over existing MLS methods.en_US
dc.description.seriesinformationEurographics Symposium on Geometry Processing 2005en_US
dc.identifier.isbn3-905673-24-Xen_US
dc.identifier.issn1727-8384en_US
dc.identifier.urihttps://doi.org/10.2312/SGP/SGP05/043-052en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectCategories and Subject Descriptors (according to ACM CCS): I.3.3 [Computer Graphics]: Line and Curve Generationen_US
dc.titleAn Adaptive MLS Surface for Reconstruction with Guaranteesen_US
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