Reconstruction with 3D Geometric Bilateral Filter

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dc.contributor.authorMiropolsky, A.en_US
dc.contributor.authorFischer, A.en_US
dc.contributor.editorGershon Elber and Nicholas Patrikalakis and Pere Bruneten_US
dc.date.accessioned2016-02-17T18:02:46Z
dc.date.available2016-02-17T18:02:46Z
dc.date.issued2004en_US
dc.description.abstractIn recent years, reverse engineering (RE) techniques have been developed for surface reconstruction from 3D scanned data. Typical sampling data, however, usually is large scale and contains unorganized points, thus leading to some anomalies in the reconstructed object. To improve performance and reduce processing time, Hierarchical Space Decomposition (HSD) methods can be applied. These methods are based on reducing the sampled data by replacing a set of original points in each voxel by a representative point, which is later connected in a mesh structure. This operation is analogous to smoothing with a simple low- pass filter (LPF). Unfortunately, this principle also smoothes sharp geometrical features, an effect that is not desired. The high performance results of bilateral filtering for removing noise from 2D images while preserving details motivated us to extend this filtering and apply it to 3D scan points. This paper introduces anisotropic 3D scan point filtering, which we have defined as 3D Geometric Bilateral Filtering (GBF). The proposed GBF method smoothes low curvature regions while preserving sharp geometric features, and it is robust, simple and fast.en_US
dc.description.sectionheadersPosters Sessionen_US
dc.description.seriesinformationSolid Modelingen_US
dc.identifier.doi10.2312/sm.20041393en_US
dc.identifier.isbn3-905673-55-Xen_US
dc.identifier.issn1811-7783en_US
dc.identifier.pages225-229en_US
dc.identifier.urihttps://doi.org/10.2312/sm.20041393en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectHierarchical Space Decompositionen_US
dc.subjectsurface reconstructionen_US
dc.subjectbilateral filteringen_US
dc.titleReconstruction with 3D Geometric Bilateral Filteren_US
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