Surface Reconstruction using Local Shape Priors

dc.contributor.authorGal, Ranen_US
dc.contributor.authorShamir, Arielen_US
dc.contributor.authorHassner, Talen_US
dc.contributor.authorPauly, Marken_US
dc.contributor.authorOr, Daniel Cohenen_US
dc.contributor.editorAlexander Belyaev and Michael Garlanden_US
dc.date.accessioned2014-01-29T09:43:16Z
dc.date.available2014-01-29T09:43:16Z
dc.date.issued2007en_US
dc.description.abstractWe present an example-based surface reconstruction method for scanned point sets. Our approach uses a database of local shape priors built from a set of given context models that are chosen specifically to match a specific scan. Local neighborhoods of the input scan are matched with enriched patches of these models at multiple scales. Hence, instead of using a single prior for reconstruction, our method allows specific regions in the scan to match the most relevant prior that fits best. Such high confidence matches carry relevant information from the prior models to the scan, including normal data and feature classification, and are used to augment the input point-set. This allows to resolve many ambiguities and difficulties that come up during reconstruction, e.g., distinguishing between signal and noise or between gaps in the data and boundaries of the model. We demonstrate how our algorithm, given suitable prior models, successfully handles noisy and under-sampled point sets, faithfully reconstructing smooth regions as well as sharp features.en_US
dc.description.seriesinformationGeometry Processingen_US
dc.identifier.isbn978-3-905673-46-3en_US
dc.identifier.issn1727-8384en_US
dc.identifier.urihttps://doi.org/10.2312/SGP/SGP07/253-262en_US
dc.publisherThe Eurographics Associationen_US
dc.titleSurface Reconstruction using Local Shape Priorsen_US
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