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Review

Detailed Three-Dimensional Building Façade Reconstruction: A Review on Applications, Data and Technologies

1
Nottingham Geospatial Institute, University of Nottingham, Jubilee Campus, Nottingham NG7 2TU, UK
2
Ordnance Survey, Adanac Drive, Southampton SO16 0AS, UK
3
Department of Mechanical, Materials and Manufacturing Engineering, University of Nottingham, University Park, Nottingham NG7 2RD, UK
*
Author to whom correspondence should be addressed.
Academic Editor: Csaba Benedek
Remote Sens. 2022, 14(11), 2579; https://doi.org/10.3390/rs14112579
Received: 31 March 2022 / Revised: 9 May 2022 / Accepted: 15 May 2022 / Published: 27 May 2022
(This article belongs to the Special Issue 3D Urban Modeling by Fusion of Lidar Point Clouds and Optical Imagery)
Urban environments are regions of complex and diverse architecture. Their reconstruction and representation as three-dimensional city models have attracted the attention of many researchers and industry specialists, as they increasingly recognise the potential for new applications requiring detailed building models. Nevertheless, despite being investigated for a few decades, the comprehensive reconstruction of buildings remains a challenging task. While there is a considerable body of literature on this topic, including several systematic reviews summarising ways of acquiring and reconstructing coarse building structures, there is a paucity of in-depth research on the detection and reconstruction of façade openings (i.e., windows and doors). In this review, we provide an overview of emerging applications, data acquisition and processing techniques for building façade reconstruction, emphasising building opening detection. The use of traditional technologies from terrestrial and aerial platforms, along with emerging approaches, such as mobile phones and volunteered geography information, is discussed. The current status of approaches for opening detection is then examined in detail, separated into methods for three-dimensional and two-dimensional data. Based on the review, it is clear that a key limitation associated with façade reconstruction is process automation and the need for user intervention. Another limitation is the incompleteness of the data due to occlusion, which can be reduced by data fusion. In addition, the lack of available diverse benchmark datasets and further investigation into deep-learning methods for façade openings extraction present crucial opportunities for future research. View Full-Text
Keywords: façade parsing; building openings; object detection; images; point cloud; platforms; sensors façade parsing; building openings; object detection; images; point cloud; platforms; sensors
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MDPI and ACS Style

Klimkowska, A.; Cavazzi, S.; Leach, R.; Grebby, S. Detailed Three-Dimensional Building Façade Reconstruction: A Review on Applications, Data and Technologies. Remote Sens. 2022, 14, 2579. https://doi.org/10.3390/rs14112579

AMA Style

Klimkowska A, Cavazzi S, Leach R, Grebby S. Detailed Three-Dimensional Building Façade Reconstruction: A Review on Applications, Data and Technologies. Remote Sensing. 2022; 14(11):2579. https://doi.org/10.3390/rs14112579

Chicago/Turabian Style

Klimkowska, Anna, Stefano Cavazzi, Richard Leach, and Stephen Grebby. 2022. "Detailed Three-Dimensional Building Façade Reconstruction: A Review on Applications, Data and Technologies" Remote Sensing 14, no. 11: 2579. https://doi.org/10.3390/rs14112579

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