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dc.contributor.authorAndersen, Vedranaen_US
dc.contributor.authorAanæs, Henriken_US
dc.contributor.authorBærentzen, Jacob Andreasen_US
dc.contributor.editorJohn Collomosse and Ian Grimsteaden_US
dc.date.accessioned2014-01-31T20:11:52Z
dc.date.available2014-01-31T20:11:52Z
dc.date.issued2010en_US
dc.identifier.isbn978-3-905673-75-3en_US
dc.identifier.urihttp://dx.doi.org/10.2312/LocalChapterEvents/TPCG/TPCG10/039-044en_US
dc.description.abstractWe propose a method for retrieving a piecewise smooth surface from noisy data. In data acquired by a scanning process sampled points are almost never on the discontinuities making reconstruction of surfaces with sharp features difficult. Our method is based on a Markov Random Field (MRF) formulation of a surface prior, with the surface represented as a collection of small planar patches, the surfels, associated with each data point. The main advantage of using surfels is that we avoid treating data points as vertices. MRF formulation of the surface prior allows us to separately model the likelihood (related to the mesh formation process) and the local surface properties. We chose to model the smoothness by considering two terms: the parallelism between neighboring surfels, and their overlap. We have demonstrated the feasibility of this approach on both synthetical and scanned data. In both cases sharp features were precisely located and planar regions smoothed.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 Modelingen_US
dc.titleSurfel Based Geometry Reconstructionen_US
dc.description.seriesinformationTheory and Practice of Computer Graphicsen_US


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