A Dynamic Surface Reconstruction Framework for Large Unstructured Point Sets

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dc.contributor.authorAllègre, Rémien_US
dc.contributor.authorChaine, Raphaëlleen_US
dc.contributor.authorAkkouche, Samiren_US
dc.contributor.editorMario Botsch and Baoquan Chen and Mark Pauly and Matthias Zwickeren_US
dc.date.accessioned2014-01-29T16:38:11Z
dc.date.available2014-01-29T16:38:11Z
dc.date.issued2006en_US
dc.description.abstractWe present a method to reconstruct simplified mesh surfaces from large unstructured point sets, extending recent work on dynamic surface reconstruction. The method consists of two core components: an efficient selective reconstruction algorithm, based on geometric convection, that simplifies the input point set while reconstructing a surface, and a local update algorithm that dynamically refines or coarsens the reconstructed surface according to specific local sampling constraints. We introduce a new data-structure that significantly accelerates the original selective reconstruction algorithm and makes it possible to handle point set models with millions of sample points. Our data-structure mixes a kd-tree with the Delaunay triangulation of the selected points enriched with a sparse subset of landmark sample points. This design efficiently responds to the specific spatial location issues of the geometric convection algorithm. We also develop an out-of-core implementation of the method, that permits to seamlessly reconstruct and interactively update simplified mesh surfaces from point sets that do not fit into main memory.en_US
dc.description.seriesinformationSymposium on Point-Based Graphicsen_US
dc.identifier.isbn3-905673-32-0en_US
dc.identifier.issn1811-7813en_US
dc.identifier.urihttps://doi.org/10.2312/SPBG/SPBG06/017-026en_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.titleA Dynamic Surface Reconstruction Framework for Large Unstructured Point Setsen_US
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