3D model generation using oblique images acquired by UAV

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Abstract

In recent years, many studies revealed the advantages of using airborne oblique images for obtaining improved 3D city models (including façades and building footprints). Here the acquisition and use of oblique images from a low cost and open source Unmanned Aerial Vehicle (UAV) for the 3D high-level-of-detail reconstruction of historical architectures is evaluated. The critical issues of such acquisitions (flight planning strategies, ground control points distribution, etc.) are described. Several problems should be considered in the flight planning: best approach to cover the whole object with the minimum time of flight; visibility of vertical structures; occlusions due to the context; acquisition of all the parts of the objects (the closest and the farthest) with similar resolution; suitable camera inclination, and so on. In this paper a solution is proposed in order to acquire oblique images with one only flight. The data processing was realized using Structure-from-Motion-based approach for point cloud generation using dense image-matching algorithms implemented in an open source software. The achieved results are analysed considering some check points and some reference LiDAR data. The system was tested for surveying a historical architectonical complex: the "Sacro Monte di Varallo Sesia" in north-west of Italy. This study demonstrates that the use of oblique images acquired from a low cost U AV system and processed through an open source software is an effective methodology to survey cultural heritage, characterized by limited accessibility, need for detail and rapidity of the acquisition phase, and often reduced budgets.

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APA

Lingua, A., Noardo, F., Spanò, A., Sanna, S., & Matrone, F. (2017). 3D model generation using oblique images acquired by UAV. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives (Vol. 42, pp. 107–115). International Society for Photogrammetry and Remote Sensing. https://doi.org/10.5194/isprs-archives-XLII-4-W2-107-2017

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