Smartphone-Based Photogrammetry Assessment in Comparison with a Compact Camera for Construction Management Applications

Abstract

Close-range photogrammetry (CRP) has proven to be a remarkable and affordable technique for data modeling and measurements extraction in construction management applications. Nevertheless, it is important to aim for making CRP more accessible by using smartphones on-site directly without a pre-calibration procedure. This study evaluated the potential of smartphones as data acquisition tools in comparison with compact cameras based on the quality and accuracy of their photogrammetric results in extracting geometrical measurements (i.e., surface area and volume). Two concrete specimens of regular shapes (i.e., beam and cylinder) along with an irregular-shaped sand pile were used to conduct this study. The datasets of both cameras were analyzed and compared based on lens distortions, image residuals, and projections multiplicity. Furthermore, the photogrammetric models were compared according to various quality criteria, processing time, and memory utilization. Though both cameras were not pre-calibrated, they both provided highly accurate geometrical estimations. The volumetric estimation error ranged from 0.37% to 2.33% for the compact camera and 0.67% to 3.19% for the smartphone. For surface area estimations, the error ranged from 0.44% to 0.91% for the compact camera and 0.50% to 1.89% for the smartphone. Additionally, the smartphone data required less processing time and memory usage with higher applicability compared with the compact camera. The implication of these findings is that they provide professionals in construction management with an assessment of a more direct and cost-effective 3D data acquisition tool with a good understanding of its reliability. Moreover, the assessment methodology and comparison criteria presented in this study can assist future research in conducting similar studies for different capturing devices in construction management applications. The findings of this study are limited to small quantification applications. Therefore, it is recommended to conduct further research that assesses smartphones as a photogrammetric data acquisition tool for larger construction elements or tracking ongoing construction activities that involve measurements estimation.

A Simple Way to Reduce 3D Model Deformation in Smartphone Photogrammetry

“…The model was then developed using the SfM-MVS method with fixed pre-calibration (IO parameters were determined from the chessboard test and then considered constant). After confirming the IO stability of the Nikon D5200 camera and considering the low noise of Nikon cameras [ 74 ], the 3D model was treated as a reference. The models were compared in CloudCompare [ 105 ] using the cloud-to-cloud distance function.…”

“…The most common solution is on-the-job calibration. Among the many smartphone photogrammetry publications, only a small part of them contain information on calibration [ 30 , 40 , 41 , 59 , 64 , 67 , 72 , 74 , 76 , 78 , 90 , 93 , 97 , 98 , 99 , 100 , 101 , 102 , 103 ].…”

Smartphone-Based Facial Scanning as a Viable Tool for Facially Driven Orthodontics?

“…A recent systematic review paper focusing on stereophotogrammetry and smartphone technology by Quinzi et al, 2022, concluded that “Stationary stereophotogrammetry devices showed a mean accuracy that ranged from 0.087 to 0.860 mm, portable stereophotogrammetry scanners from 0.150 to 0.849 mm, and smartphones from 0.460 to 1.400 mm.” [ 46 ]. The volumetric estimation errors are typically bigger in smartphone scanning than in photogrammetry [ 47 ].…”

A Novel Multimedia Player for International Standard—JPEG Snack

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