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RoofN3D: a database for 3D building reconstruction with deep learning / Andreas Wichmann in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 6 (June 2019)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 6 (June 2019) . - pp 435 - 443 Titre : RoofN3D: a database for 3D building reconstruction with deep learning Type de document : Article/Communication Auteurs : Andreas Wichmann, Auteur ; Amgad Agoub, Auteur ; Valentina Schmidt, Auteur Année de publication : 2019 Article en page(s) : pp 435 - 443 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] .Net [Termes IGN] apprentissage profond [Termes IGN] base de données localisées 3D [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] reconstruction 3D du bâti [Termes IGN] semis de points [Termes IGN] toit Résumé : (Auteur) Machine learning methods, in particular those based on deep learning, have gained in importance through the latest development of artificial intelligence and computer hardware. However, the direct application of deep learning methods to improve the results of 3D building reconstruction is often not possible due, for example, to the lack of suitable training data. To address this issue, we present RoofN3D which provides a three-dimensional (3D) point cloud training dataset that can be used to train machine learning models for different tasks in the context of 3D building reconstruction. The details about RoofN3D and the developed framework to automatically derive such training data are described in this paper. Furthermore, we provide an overview of other available 3D point cloud training data and approaches from current literature in which solutions for the application of deep learning to 3D point cloud data are presented. Finally, we exemplarily demonstrate how the provided data can be used to classify building roofs with the PointNet framework. Numéro de notice : A2019-248 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.6.435 En ligne : https://doi.org/10.14358/PERS.85.6.435 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93004 [article]

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Smooth transformations between generalized 3D building models for visualization purposes / Martin Kada in Cartography and Geographic Information Science, Vol 42 n° 4 (September 2015)  

Public [article]  inCartography and Geographic Information Science > Vol 42 n° 4 (September 2015) . - pp 306 - 314 Titre : Smooth transformations between generalized 3D building models for visualization purposes Type de document : Article/Communication Auteurs : Martin Kada, Auteur ; Andreas Wichmann, Auteur ; Tobias Hermes, Auteur Année de publication : 2015 Article en page(s) : pp 306 - 314 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] Bâti-3D [Termes IGN] généralisation sémantique [Termes IGN] visualisation 3D Résumé : (Auteur) The generation of highly detailed 3D city models has significantly matured over the last couple of years. Especially for visualization purposes, these models are needed at several levels of detail (LOD) in fine granular steps that also allow for a smooth transformation from one level to the next to avoid pop-up artifacts. Several methods have been proposed for the automatic generalization of 3D building models that simplify the geometry of given boundary-based models at discrete LODs. In this paper, we present an approach to generate a transformation sequence between two discrete LODs with the purpose to later build a continuous LOD sequence. The method is based on mesh simplification techniques that are extended to a restricted use of edge collapse and vertex move operations applied on triangle meshes augmented by additional breaking lines. The validity of the approach is demonstrated on a case study that is close to reality. Numéro de notice : A2015-513 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2015.1039588 En ligne : https://doi.org/10.1080/15230406.2015.1039588 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77712 [article]

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