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3D tree modeling from incomplete point clouds via optimization and L1-MST / Jie Mei in International journal of geographical information science IJGIS, vol 31 n° 5-6 (May-June 2017)
[article]
Titre : 3D tree modeling from incomplete point clouds via optimization and L1-MST Type de document : Article/Communication Auteurs : Jie Mei, Auteur ; Liqiang Zhang, Auteur ; Shihao Wu, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 999 - 1021 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] algorithme STA
[Termes IGN] arbre (flore)
[Termes IGN] branche (arbre)
[Termes IGN] densité des points
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] méthode robuste
[Termes IGN] modèle numérique d'objet
[Termes IGN] optimisation (mathématiques)
[Termes IGN] semis de points
[Termes IGN] semis de points clairsemés
[Termes IGN] squelettisationRésumé : (auteur) Reconstruction of 3D trees from incomplete point clouds is a challenging issue due to their large variety and natural geometric complexity. In this paper, we develop a novel method to effectively model trees from a single laser scan. First, coarse tree skeletons are extracted by utilizing the L1-median skeleton to compute the dominant direction of each point and the local point density of the point cloud. Then we propose a data completion scheme that guides the compensation for missing data. It is an iterative optimization process based on the dominant direction of each point and local point density. Finally, we present a L1-minimum spanning tree (MST) algorithm to refine tree skeletons from the optimized point cloud, which integrates the advantages of both L1-median skeleton and MST algorithms. The proposed method has been validated on various point clouds captured from single laser scans. The experiment results demonstrate the effectiveness and robustness of our method for coping with complex shapes of branching structures and occlusions. Numéro de notice : A2017-239 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2016.1264075 En ligne : http://dx.doi.org/10.1080/13658816.2016.1264075 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85173
in International journal of geographical information science IJGIS > vol 31 n° 5-6 (May-June 2017) . - pp 999 - 1021[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2017031 RAB Revue Centre de documentation En réserve L003 Disponible Street-side vehicle detection, classification and change detection using mobile laser scanning data / Wen Xiao in ISPRS Journal of photogrammetry and remote sensing, vol 114 (April 2016)
[article]
Titre : Street-side vehicle detection, classification and change detection using mobile laser scanning data Type de document : Article/Communication Auteurs : Wen Xiao, Auteur ; Bruno Vallet , Auteur ; Konrad Schindler, Auteur ; Nicolas Paparoditis , Auteur Année de publication : 2016 Projets : Terra Mobilita / Article en page(s) : pp 166 - 178 Note générale : bibliogaphie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage dirigé
[Termes IGN] classification dirigée
[Termes IGN] détection d'objet
[Termes IGN] détection de changement
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] modèle numérique d'objet
[Termes IGN] rectangle englobant minimum
[Termes IGN] semis de points
[Termes IGN] véhicule automobileRésumé : (auteur) Statistics on street-side car parks, e.g. occupancy rates, parked vehicle types, parking durations, are of great importance for urban planning and policy making. Related studies, e.g. vehicle detection and classification, mostly focus on static images or video. Whereas mobile laser scanning (MLS) systems are increasingly utilized for urban street environment perception due to their direct 3D information acquisition, high accuracy and movability. In this paper, we design a complete system for car park monitoring, including vehicle recognition, localization, classification and change detection, from laser scanning point clouds. The experimental data are acquired by an MLS system using high frequency laser scanner which scans the streets vertically along the system’s moving trajectory. The point clouds are firstly classified as ground, building façade, and street objects which are then segmented using state-of-the-art methods. Each segment is treated as an object hypothesis, and its geometric features are extracted. Moreover, a deformable vehicle model is fitted to each object. By fitting an explicit model to the vehicle points, detailed information, such as precise position and orientation, can be obtained. The model parameters are also treated as vehicle features. Together with the geometric features, they are applied to a supervised learning procedure for vehicle or non-vehicle recognition. The classes of detected vehicles are also investigated. Whether vehicles have changed across two datasets acquired at different times is detected to estimate the durations. Here, vehicles are trained pair-wisely. Two same or different vehicles are paired up as training samples. As a result, the vehicle recognition, classification and change detection accuracies are 95.9%, 86.0% and 98.7%, respectively. Vehicle modelling improves not only the recognition rate, but also the localization precision compared to bounding boxes. Numéro de notice : A2016--090 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Autre URL associée : vers HAL Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.02.007 Date de publication en ligne : 03/03/2016 En ligne : http://doi.org/10.1016/j.isprsjprs.2016.02.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84612
in ISPRS Journal of photogrammetry and remote sensing > vol 114 (April 2016) . - pp 166 - 178[article]Creating large-scale city models from 3D-Point clouds : a robust approach with hybrid representation / Florent Lafarge in International journal of computer vision, vol 99 n° 1 (August 2012)
[article]
Titre : Creating large-scale city models from 3D-Point clouds : a robust approach with hybrid representation Type de document : Article/Communication Auteurs : Florent Lafarge, Auteur ; Clément Mallet , Auteur Année de publication : 2012 Article en page(s) : pp 69 - 85 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] champ aléatoire de Markov
[Termes IGN] détection d'objet
[Termes IGN] données localisées 3D
[Termes IGN] méthode de réduction d'énergie
[Termes IGN] méthode robuste
[Termes IGN] modèle 3D de l'espace urbain
[Termes IGN] modèle numérique d'objet
[Termes IGN] scène urbaine
[Termes IGN] semis de pointsRésumé : (Auteur) We present a novel and robust method for modeling cities from 3D-point data. Our algorithm provides a more complete description than existing approaches by reconstructing simultaneously buildings, trees and topologically complex grounds. A major contribution of our work is the original way of modeling buildings which guarantees a high generalization level while having semantized and compact representations. Geometric 3D-primitives such as planes, cylinders, spheres or cones describe regular roof sections, and are combined with mesh-patches that represent irregular roof components. The various urban components interact through a non-convex energy minimization problem in which they are propagated under arrangement constraints over a planimetric map. Our approach is experimentally validated on complex buildings and large urban scenes of millions of points, and is compared to state-of-the-art methods. Numéro de notice : A2012-731 Affiliation des auteurs : MATIS+Ext (1993-2011) Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s11263-012-0517-8 Date de publication en ligne : 29/02/2012 En ligne : https://doi.org/10.1007/s11263-012-0517-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91109
in International journal of computer vision > vol 99 n° 1 (August 2012) . - pp 69 - 85[article]