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A local graph-based structure for processing gigantic aggregated 3D point clouds

Abstract : We present an original workflow for structuring a point cloud generated from several scans. Our representation is based on a set of local graphs. Each graph is constructed from the depth map provided by each scan. The graphs are then connected together via the overlapping areas, and careful consideration of the redundant points in these regions leads to a piecewise and globally consistent structure for the underlying surface sampled by the point cloud. The proposed workflow allows structuring aggregated point clouds, scan after scan, whatever the number of acquisitions and the number of points per acquisition, even on computers with very limited memory capacities. To show that our structure can be highly relevant for the community, where the gigantic amount of data represents a real scientific challenge per se, we present an algorithm based on this structure capable of resampling billions of points on standard computers. This application is particularly attractive for simplifying and visualizing gigantic point clouds representing very large-scale scenes (buildings, urban scenes, historical sites...), which often require a prohibitive number of points to describe them accurately.
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Contributor : Frédéric Payan Connect in order to contact the contributor
Submitted on : Monday, January 25, 2021 - 2:11:17 PM
Last modification on : Tuesday, January 4, 2022 - 6:35:34 AM
Long-term archiving on: : Monday, April 26, 2021 - 7:03:56 PM


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Arnaud Bletterer, Frédéric Payan, Marc Antonini. A local graph-based structure for processing gigantic aggregated 3D point clouds. IEEE Transactions on Visualization and Computer Graphics, Institute of Electrical and Electronics Engineers, 2020, pp.1-1. ⟨10.1109/TVCG.2020.3042588⟩. ⟨hal-03106333⟩



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