Use of photomodeler as a measuring and management tool in construction projects

Basha, Ali; Fawzy, Hossam El-Din; Farhan, None Magda; Sherif, Mohammed; Botross, Marco N.; Selim, Reem; Sherbini, Eman El; Roshdy, Amira; Reda, M.; Gharib, None Rehab; Qazzaz, Ahmed El; Hamdeen, Mohammed; Lotfy, Norhan; Galil, Omnia Abdel; Gamal, Esraa; Shabaan, Asmaa; Fouda, Nourhan

doi:10.30574/gjeta.2022.10.2.0034

Global J. Eng. Tech. Adv.

2022

DOI: 10.30574/gjeta.2022.10.2.0034

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Use of photomodeler as a measuring and management tool in construction projects

Ali Basha¹,

Hossam El-Din Fawzy²,

None Magda Farhan³

et al.

Abstract: Photogrammetry can contribute not only with precision, but especially with speed and efficiency regarding to documentation; it is an ideal system for producing 3D models, for creating data bases, and offering efficient solutions in emergency cases, etc. There are here some examples of the use of digital photogrammetry based on systems and methods which make possible to operate reducing the working time in the site, and operating very light and inexpensive equipment. This system is specially adapted to emergenc… Show more

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2024

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Filtering of 3D point clouds using maximum likelihood algorithm

Salah,

Farhan,

Basha

et al. 2024

Discov Appl Sci

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Recently, the 3D point cloud (PC) has become more popular as an innovative object representation. However, there is usually noise and outliers in the raw point cloud. It is essential to eliminate the noise from the point cloud and outlier data while maintaining the features and finer details intact. This paper presents a comprehensive method for filtering and classification point clouds using a maximum likelihood algorithm (ML). TOPCON GLS-2000 3D terrestrial laser scanners (TLS) have been used to collect the 3D PC data set; the scan range is up to 350 m. About 30 m apart from the study area. ScanMaster software has been used to import, view, and filter point cloud information. The position information of the points is linked with the training point cloud and the filtered point cloud to derive the nonlinear model using MATLAB software. To evaluate the quality of the denoising results, two error metrics have been used: the average angle (δ) and distance (Dmean) between the ground truth point and the resulting point. The experimental findings demonstrate that the suggested approach can effectively filter out background noise while improving feature preservation. The filtering and classifying technique is more effective and efficient compared to the selected filtering methods when applied to 3D point clouds containing a large number of points and a variety of natural characteristics.

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Filtering of 3D point clouds using maximum likelihood algorithm

Salah,

Farhan,

Basha

et al. 2024

Discov Appl Sci

View full text Add to dashboard Cite

show abstract

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Use of photomodeler as a measuring and management tool in construction projects

Cited by 1 publication

References 3 publications

Filtering of 3D point clouds using maximum likelihood algorithm

Filtering of 3D point clouds using maximum likelihood algorithm

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