3D building façade reconstruction using deep learning3D building façade reconstruction using deep learning
Peer-Reviewed Journal Publication
Δημοσίευση σε Περιοδικό με Κριτές
2021-08-042020enIn 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. http://creativecommons.org/licenses/by/4.0/ISPRS International Journal of Geo-Information95Bacharidis_et_al_ISPRS Int. Geo-Inf._9(5)_2020.pdfChania [Greece]Library of TUC2021-08-03application/pdf7.1 MBfree
Bacharidis Konstantinos
Μπαχαριδης Κωνσταντινος
Sarri Effrosyni
Σαρρη Ευφροσυνη
Ragia Lemonia
Ραγια Λεμονια
MDPI
3D façade reconstruction
Deep learning
Computer vision
Geo-referenced data