Centre de documentation
scientifique

Octree-based segmentation for terrestrial LiDAR point cloud data in industrial applications / Yun-Ting Su in ISPRS Journal of photogrammetry and remote sensing, vol 113 (March 2016)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 113 (March 2016) . - pp 59 - 74 Titre : Octree-based segmentation for terrestrial LiDAR point cloud data in industrial applications Type de document : Article/Communication Auteurs : Yun-Ting Su, Auteur ; James Bethel, Auteur ; Shuowen Hu, Auteur Année de publication : 2016 Article en page(s) : pp 59 - 74 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] métrologie industrielle [Termes IGN] octree [Termes IGN] segmentation d'image [Termes IGN] semis de points [Termes IGN] télémétrie laser terrestre Résumé : (auteur) Automated and efficient algorithms to perform segmentation of terrestrial LiDAR data is critical for exploitation of 3D point clouds, where the ultimate goal is CAD modeling of the segmented data. In this work, a novel segmentation technique is proposed, starting with octree decomposition to recursively divide the scene into octants or voxels, followed by a novel split and merge framework that uses graph theory and a series of connectivity analyses to intelligently merge components into larger connected components. The connectivity analysis, based on a combination of proximity, orientation, and curvature connectivity criteria, is designed for the segmentation of pipes, vessels, and walls from terrestrial LiDAR data of piping systems at industrial sites, such as oil refineries, chemical plants, and steel mills. The proposed segmentation method is exercised on two terrestrial LiDAR datasets of a steel mill and a chemical plant, demonstrating its ability to correctly reassemble and segregate features of interest. Numéro de notice : A2016-530 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.01.001 En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2016.01.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81612 [article]