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Remote Sens. 2014, 6(6), 5671-5691; doi:10.3390/rs6065671

A Photogrammetric and Computer Vision-Based Approach for Automated 3D Architectural Modeling and Its Typological Analysis

1
Department of Cartographic and Terrain Engineering, Polytechnic School of Avila, University of Salamanca, Hornos Caleros 50, 05003 Avila, Spain
2
Lab of Architectural Photogrammetry, High Technical School of Architecture ,University of Valladolid, Av. Salamanca 32, 47014 Valladolid, Spain
*
Author to whom correspondence should be addressed.
Received: 13 March 2014 / Revised: 12 June 2014 / Accepted: 12 June 2014 / Published: 17 June 2014
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Abstract

Thanks to the advances in integrating photogrammetry and computer vision, as well as in some numeric algorithms and methods, it is possible to aspire to turn 2D (images) into 3D (point clouds) in an automatic, flexible and good-quality way. This article presents a new method through the development of PW (Photogrammetry Workbench) (and how this could be useful for architectural modeling). This tool enables the user to turn images into scale 3D point cloud models, which have a better quality than those of laser systems. Moreover, the point clouds may include the respective orthophotos with photographic texture. The method allows the study of the typology of architecture and has been successfully tested on a sample of ten religious buildings located in the region of Aliste, Zamora (Spain). View Full-Text
Keywords: image-based modeling; photogrammetry; computer vision; matching; cultural heritage; architecture; software development; typological analysis image-based modeling; photogrammetry; computer vision; matching; cultural heritage; architecture; software development; typological analysis
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

García-Gago, J.; González-Aguilera, D.; Gómez-Lahoz, J.; San José-Alonso, J.I. A Photogrammetric and Computer Vision-Based Approach for Automated 3D Architectural Modeling and Its Typological Analysis. Remote Sens. 2014, 6, 5671-5691.

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