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dc.contributor.authorAlliez, Pierreen_US
dc.contributor.authorCohen-Steiner, Daviden_US
dc.contributor.authorTong, Yiyingen_US
dc.contributor.authorDesbrun, Mathieuen_US
dc.contributor.editorAlexander Belyaev and Michael Garlanden_US
dc.date.accessioned2014-01-29T09:43:05Z
dc.date.available2014-01-29T09:43:05Z
dc.date.issued2007en_US
dc.identifier.isbn978-3-905673-46-3en_US
dc.identifier.issn1727-8384en_US
dc.identifier.urihttp://dx.doi.org/10.2312/SGP/SGP07/039-048en_US
dc.description.abstractWe introduce an algorithm for reconstructing watertight surfaces from unoriented point sets. Using the Voronoi diagram of the input point set, we deduce a tensor field whose principal axes and eccentricities locally represent respectively the most likely direction of the normal to the surface, and the confidence in this direction estimation. An implicit function is then computed by solving a generalized eigenvalue problem such that its gradient is most aligned with the principal axes of the tensor field, providing a best-fitting isosurface reconstruction. Our approach possesses a number of distinguishing features. In particular, the implicit function optimization provides resilience to noise, adjustable fitting to the data, and controllable smoothness of the reconstructed surface. Finally, the use of simplicial meshes (possibly restricted to a thin crust around the input data) and (an)isotropic Laplace operators renders the numerical treatment simple and robust.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectCategories and Subject Descriptors (according to ACM CCS): I.3.5 [CG]: Computational Geometry and Object Modeling.en_US
dc.titleVoronoi-based Variational Reconstruction of Unoriented Point Setsen_US
dc.description.seriesinformationGeometry Processingen_US


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