Spectral Surface Reconstruction From Noisy Point Clouds

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dc.contributor.authorKolluri, Ravikrishnaen_US
dc.contributor.authorShewchuk, Jonathan Richarden_US
dc.contributor.authorO'Brien, James F.en_US
dc.contributor.editorRoberto Scopigno and Denis Zorinen_US
dc.date.accessioned2014-01-29T09:19:45Z
dc.date.available2014-01-29T09:19:45Z
dc.date.issued2004en_US
dc.description.abstractWe introduce a noise-resistant algorithm for reconstructing a watertight surface from point cloud data. It forms a Delaunay tetrahedralization, then uses a variant of spectral graph partitioning to decide whether each tetrahedron is inside or outside the original object. The reconstructed surface triangulation is the set of triangular faces where inside and outside tetrahedra meet. Because the spectral partitioner makes local decisions based on a global view of the model, it can ignore outliers, patch holes and undersampled regions, and surmount ambiguity due to measurement errors. Our algorithm can optionally produce a manifold surface. We present empirical evidence that our implementation is substantially more robust than several closely related surface reconstruction programs.en_US
dc.description.seriesinformationSymposium on Geometry Processingen_US
dc.identifier.isbn3-905673-13-4en_US
dc.identifier.issn1727-8384en_US
dc.identifier.urihttps://doi.org/10.2312/SGP/SGP04/011-022en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectCategories and Subject Descriptors (according to ACM CCS): I.3.5 [Computing Methodologies]: Computer Graphics Computational Geometry and Object Modelingen_US
dc.titleSpectral Surface Reconstruction From Noisy Point Cloudsen_US
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SGP04: Eurographics Symposium on Geometry Processing