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A Local Graph-Based Structure for Processing Gigantic Aggregated 3D Point Clouds

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Bibliographic Details
Title: A Local Graph-Based Structure for Processing Gigantic Aggregated 3D Point Clouds
Authors: Bletterer, Arnaud, Payan, Frédéric, Antonini, Marc
Contributors: Payan, Frédéric
Source: IEEE Transactions on Visualization and Computer Graphics. 28:2822-2833
Publisher Information: Institute of Electrical and Electronics Engineers (IEEE), 2022.
Publication Year: 2022
Subject Terms: Three-Dimensional Graphics and Realism, Out-Of-Core Algorithms, Data Structure, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 3D Point Clouds, Computational Geometry and Object Modeling, Graphs, Poisson-disk sampling, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Description: 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.
Document Type: Article
File Description: application/pdf
ISSN: 2160-9306
1077-2626
DOI: 10.1109/tvcg.2020.3042588
Access URL: https://hal.archives-ouvertes.fr/hal-03106333/file/BPA_TVCG_Preprint.pdf
https://pubmed.ncbi.nlm.nih.gov/33275583
https://hal.science/hal-03106333v1
https://doi.org/10.1109/tvcg.2020.3042588
https://hal.science/hal-03106333v1/document
https://www.ncbi.nlm.nih.gov/pubmed/33275583
https://ieeexplore.ieee.org/document/9282203
https://hal.archives-ouvertes.fr/hal-03106333
Rights: IEEE Copyright
Accession Number: edsair.doi.dedup.....80094e4d9ea4e51e63e4e5e6da378a1d
Database: OpenAIRE
Description
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.
ISSN:21609306
10772626
DOI:10.1109/tvcg.2020.3042588