3D laser scanning and close-range photogrammetry for buildings documentation: A hybrid technique towards a better accuracy

Fawzy, Hossam El-Din

doi:10.1016/j.aej.2019.10.003

Cited by 56 publications

(30 citation statements)

References 24 publications

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“…Furthermore, out-of-plane elements, such as ornaments, friezes, and balconies, may cause shadows in laser scanning clouds. TLS and close-range photogrammetry can be integrated to overcome some of the issues above [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Quality Assessment of Photogrammetric Models for Façade and Building Reconstruction Using DJI Phantom 4 RTK

et al. 2020

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Aerial photogrammetry by Unmanned Aerial Vehicles (UAVs) is a widespread method to perform mapping tasks with high-resolution to reconstruct three-dimensional (3D) building and façade models. However, the survey of Ground Control Points (GCPs) represents a time-consuming task, while the use of Real-Time Kinematic (RTK) drones allows for one to collect camera locations with an accuracy of a few centimeters. DJI Phantom 4 RTK (DJI-P4RTK) combines this with the possibility to acquire oblique images in stationary conditions and it currently represents a versatile drone widely used from professional users together with commercial Structure-from-Motion software, such as Agisoft Metashape. In this work, we analyze the architectural application of this drone to the photogrammetric modeling of a building with particular regard to metric survey specifications for cultural heritage for 1:20, 1:50, 1:100, and 1:200 scales. In particular, we designed an accuracy assessment test signalizing 109 points, surveying them with total station and adjusting the measurements through a network approach in order to achieve millimeter-level accuracy. Image datasets with a designed Ground Sample Distance (GSD) of 2 mm were acquired in Network RTK (NRTK) and RTK modes in manual piloting and processed both as single façades (S–F) and as an overall block (4–F). Subsequently, we compared the results of photogrammetric models generated in Agisoft Metashape to the Signalized Point (SP) coordinates. The results highlight the importance of processing an overall photogrammetric block, especially whenever part of camera locations exhibited a poorer accuracy due to multipath effects. No significant differences were found between the results of network real-time kinematic (NRTK) and real-time kinematic (RTK) datasets. Horizontal residuals were generally comparable to GNSS accuracy in NRTK/RTK mode, while vertical residuals were found to be affected by an offset of about 5 cm. We introduced an external GCP or used one SP per façade as GCP, assuming a poorer camera location accuracy at the same time, in order to fix this issue and comply with metric survey specifications for the widest architectural scale range. Finally, both S–F and 4–F projects satisfied the metric survey requirements of a scale of 1:50 in at least one of the approaches tested.

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Section: Introductionmentioning

confidence: 99%

Quality Assessment of Photogrammetric Models for Façade and Building Reconstruction Using DJI Phantom 4 RTK

et al. 2020

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“…There are positive experiences in 3D photogrammetry for crack monitoring in exterior infrastructures (e.g. [8,16]). However, in this paper we focus on techniques to be used inside historic buildings, where the natural light conditions are inadequate and images do not have sufficient photographic resolution to build the points cloud.…”

Section: Discussionmentioning

confidence: 99%

“…3D laser scanning is a technique to capture free-form shapes to generate highly accurate 3D point clouds. This technique is widely developed and is highly recommended to survey the geometry properties of buildings (e.g., [1,7,8]), but its use is restricted to buildings in which a pathological process is already confirmed because the associated costs for its installation are high and non-advantageous, especially when monitoring a single and localised set of cracks for preventive monitoring [9].…”

Section: Introductionmentioning

confidence: 99%

2D image-based crack monitoring: an affordable, sufficient and non-invasive technique for the democratization of preventive conservation of listed buildings

2022

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In order to democratize the preventive conservation of most listed buildings, we need to have an affordable tool that allows controlling their main vital signs. Complex and costly control installations, such as large digital data collection campaigns or 3D laser scanning, are not always possible. Methods for analogue data collection, such as comparators and callipers, are much more affordable and discreet but require the use of auxiliary lifting platforms for both installation and reading and control, hence resulting too invasive. In this paper we study techniques for digital indoor monitoring of cracks of listed masonry buildings. 2D image-based crack monitoring technique was found to be sufficient, affordable, traceable, and respectful with the use of the building. To verify its effectiveness, we performed laboratory tests and implement it in a real case study, a church in Zaragoza, Spain, currently undergoing a reparation process. The obtained results show that the proposed 2D image-based inspection technique provides measurements closer to the comparator, used as a reference, than the calliper, with a mean difference of 0.15 mm with respect to the reference, and a standard deviation of 0.17 mm, and its use can be up to 56% cheaper than the comparator. Therefore, the technique is easily generalizable to different heritage buildings.

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“…Currently, the decay mapping is a manual practice, with low accuracy and huge amounts of time, due to the complexity of architectural heritage environments. The wide diffusion of digital survey techniques, such as close-range photogrammetry and laser scanning, leads to a significant improvement in the survey of ancient buildings ( Remondino, 2011;Aicardi et al, 2018;El-Din Fawzy, 2019). In Computer Aided Design, reverse engineering consists in the measuring, analysing and testing a real object, in order to virtually reconstruct it into a 3D model (Wang, 2011).…”

Section: Introductionmentioning

confidence: 99%

Automatic Point Cloud Segmentation for the Detection of Alterations on Historical Buildings Through an Unsupervised and Clustering-Based Machine Learning Approach

Musicco

Galantucci

Bruno

et al. 2021

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The article describes an innovative procedure for the three-dimensional analysis of decay morphologies of ancient buildings, through the application of machine learning methods for the automatic segmentation of point clouds. In the field of Cultural Heritage conservation, photogrammetric data can be exploited, for diagnostic and monitoring support, to recognize different typologies of alterations visible on the masonry surface, starting from colour information. Actually, certain stone and plaster surface pathologies (biological patina, biological colonization, chromatic alterations, spots,…) are typically characterized by chromatic variations. To this purpose, colour-based segmentation with hierarchical clustering has been implemented on colour data of point clouds, considered in the HSV colour-space. In addition, geometry-based segmentation of 3D reconstructions has been performed, in order to identify the main architectural elements (walls, vaults), and to associate them to the detected defects. The proposed workflow has been applied to some ancient buildings’ environments, chosen because of their irregularity both in geometrical and colorimetric characteristics.

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3D laser scanning and close-range photogrammetry for buildings documentation: A hybrid technique towards a better accuracy

Cited by 56 publications

References 24 publications

Quality Assessment of Photogrammetric Models for Façade and Building Reconstruction Using DJI Phantom 4 RTK

Quality Assessment of Photogrammetric Models for Façade and Building Reconstruction Using DJI Phantom 4 RTK

2D image-based crack monitoring: an affordable, sufficient and non-invasive technique for the democratization of preventive conservation of listed buildings

Automatic Point Cloud Segmentation for the Detection of Alterations on Historical Buildings Through an Unsupervised and Clustering-Based Machine Learning Approach

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