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A generative statistical approach to automatic 3D building roof reconstruction from laser scanning data / Hai Huang in ISPRS Journal of photogrammetry and remote sensing, vol 79 (May 2013)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 79 (May 2013) . - pp 29 - 53 Titre : A generative statistical approach to automatic 3D building roof reconstruction from laser scanning data Type de document : Article/Communication Auteurs : Hai Huang, Auteur ; Claus Brenner, Auteur ; Monika Sester, Auteur Année de publication : 2013 Article en page(s) : pp 29 - 53 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] bibliothèque de formes [Termes IGN] chaîne de Markov [Termes IGN] détection du bâti [Termes IGN] données lidar [Termes IGN] primitive géométrique [Termes IGN] reconstruction 3D du bâti [Termes IGN] segmentation [Termes IGN] semis de points [Termes IGN] toit Résumé : (Auteur) This paper presents a generative statistical approach to automatic 3D building roof reconstruction from airborne laser scanning point clouds. In previous works, bottom-up methods, e.g., points clustering, plane detection, and contour extraction, are widely used. Due to the data artefacts caused by tree clutter, reflection from windows, water features, etc., the bottom-up reconstruction in urban areas may suffer from a number of incomplete or irregular roof parts. Manually given geometric constraints are usually needed to ensure plausible results. In this work we propose an automatic process with emphasis on top-down approaches. The input point cloud is firstly pre-segmented into subzones containing a limited number of buildings to reduce the computational complexity for large urban scenes. For the building extraction and reconstruction in the subzones we propose a pure top-down statistical scheme, in which the bottom-up efforts or additional data like building footprints are no more required. Based on a predefined primitive library we conduct a generative modeling to reconstruct roof models that fit the data. Primitives are assembled into an entire roof with given rules of combination and merging. Overlaps of primitives are allowed in the assembly. The selection of roof primitives, as well as the sampling of their parameters, is driven by a variant of Markov Chain Monte Carlo technique with specified jump mechanism. Experiments are performed on data-sets of different building types (from simple houses, high-rise buildings to combined building groups) and resolutions. The results show robustness despite the data artefacts mentioned above and plausibility in reconstruction. Numéro de notice : A2013-232 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.02.004 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.02.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32370 [article]

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