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3D building façade reconstruction using deep learning

Bacharidis Konstantinos, Sarri Effrosyni, Ragia Lemonia

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URIhttp://purl.tuc.gr/dl/dias/35686651-9B07-4D74-9223-E1795E64A259-
Identifierhttps://doi.org/10.3390/ijgi9050322-
Identifierhttps://www.mdpi.com/2220-9964/9/5/322/htm-
Languageen-
Extent24 pagesen
Extent6,76 megabytesen
Title3D building façade reconstruction using deep learningen
CreatorBacharidis Konstantinosen
CreatorΜπαχαριδης Κωνσταντινοςel
CreatorSarri Effrosynien
CreatorΣαρρη Ευφροσυνηel
CreatorRagia Lemoniaen
CreatorΡαγια Λεμονιαel
PublisherMDPIen
Content SummaryIn recent years, advances in computer hardware, graphics rendering algorithms and computer vision have enabled the utilization of 3D building reconstructions in the fields of archeological structure restoration and urban planning. This paper deals with the reconstruction of realistic 3D models of buildings façades, in the urban environment for cultural heritage. The proposed approach is an extension of our previous work in this research topic, which introduced a methodology for accurate 3D realistic façade reconstruction by defining and exploiting a relation between stereoscopic image and tacheometry data. In this work, we re-purpose well known deep neural network architectures in the fields of image segmentation and single image depth prediction, for the tasks of façade structural element detection, depth point-cloud generation and protrusion estimation, with the goal of alleviating drawbacks in our previous design, resulting in a more light-weight, robust, flexible and cost-effective design. en
Type of ItemPeer-Reviewed Journal Publicationen
Type of ItemΔημοσίευση σε Περιοδικό με Κριτέςel
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2021-08-04-
Date of Publication2020-
Subject3D façade reconstructionen
SubjectDeep learningen
SubjectComputer visionen
SubjectGeo-referenced dataen
Bibliographic CitationK. Bacharidis, F. Sarri, and L. Ragia, “3D building facade reconstruction using deep learning,” ISPRS Int. Geo-Inf., vol. 9, no. 5, May 2020. doi: 10.3390/ijgi9050322en

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