Centre dedocumentation
scientifique

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]

Réservation

Réserver ce document

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
105-2019061	SL	Revue	Centre de documentation	Revues en salle	Disponible