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Planar-based adaptive down-sampling of point clouds / Yun-Jou Lin in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 12 (December 2016)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 82 n° 12 (December 2016) . - pp 955 - 966 Titre : Planar-based adaptive down-sampling of point clouds Type de document : Article/Communication Auteurs : Yun-Jou Lin, Auteur ; Ronald R Benziger, Auteur ; Ayman Habib, Auteur Année de publication : 2016 Article en page(s) : pp 955 - 966 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] densité des points [Termes IGN] segmentation [Termes IGN] semis de points [Termes IGN] surface plane [Termes IGN] traitement automatique de données [Termes IGN] traitement de données localisées Résumé : (auteur) Derived point clouds from laser scanners and image-based dense-matching techniques usually include tremendous number of points. Processing (e.g., segmenting) such huge dataset is time-consuming and might not be necessary. For example, a planar surface just needs few points to be defined. In contrast, linear/cylindrical and rough features require more points for reliable modeling since during the data acquisition process, only a portion of linear/cylindrical features is present in the point cloud. This paper introduces an adaptive down-sampling strategy for removing redundant points from high density planar regions while retaining points in planar areas with sparse points and all the points within linear/cylindrical and rough neighborhoods. To demonstrate the feasibility and performance of the proposed procedure, a comparison of segmentation results using original laser and image-based point clouds as well as the adaptively, uniformly, and point-spacing-based down-sampled point clouds are presented while commenting on the computational efficiency and the segmentation quality. Numéro de notice : A2016-984 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.82.12.955 En ligne : https://doi.org/10.14358/PERS.82.12.955 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83700 [article]