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Open AccessArticle

3D Building Façade Reconstruction Using Deep Learning

by Konstantinos Bacharidis 1,2,†, Froso Sarri 3,† and Lemonia Ragia 4,*,†
1
Department of Computer Science, University of Crete, 70013 Heraklion, Greece
2
Foundation of Research and Technology Hellas (FORTH), 70013 Heraklion, Greece
3
School of Electrical and Computer Engineering, Technical University of Crete, 73100 Chania, Greece
4
ATHENA Research and Innovation Information Technologies, 15125 Marousi, Greece
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
ISPRS Int. J. Geo-Inf. 2020, 9(5), 322; https://doi.org/10.3390/ijgi9050322
Received: 24 March 2020 / Revised: 1 May 2020 / Accepted: 11 May 2020 / Published: 13 May 2020
In 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. View Full-Text
Keywords: 3D façade reconstruction; deep learning; computer vision; geo-referenced data 3D façade reconstruction; deep learning; computer vision; geo-referenced data
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Bacharidis, K.; Sarri, F.; Ragia, L. 3D Building Façade Reconstruction Using Deep Learning. ISPRS Int. J. Geo-Inf. 2020, 9, 322.

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