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Reza Maalek is the endowed professor and chair of Digital Engineering and Construction at the Institute of Technology and Management in Construction in the Karlsruhe Institute of Technology, Germany. In addition to multiple international awards during his M.Sc. and Ph.D. at the University of Calgary, he was the recipient of the ASTech award, the province of Alberta’s (in Canada) highest honor in science and technology for his contributions in creating innovative and scientific solutions to practical problems, pertaining to construction automation. His professorship and chair is supported by the renowned GOLDBECK GmbH, Germany's largest family-owned construction company.
Projective transformation of spheres onto images produce ellipses, whose centers do not coincide with the projected center of the sphere. This results in an eccentricity error, which must be treated in high precision metrology. This article provides closed formulations for modeling this error in images to enable 3-dimensional (3D) reconstruction of the center of spherical objects. The article also provides a new direct robust method for detecting spherical pattern in point clouds. It was shown that the eccentricity error in an image has only one component in the direction of the major axis of the ellipse. It was also revealed that the eccentricity is zero if and only if the center of the projected sphere lies on the camera’s perspective center. The effectiveness of the robust sphere detection and the eccentricity error modeling method was evaluated on simulated point clouds of spheres and real-world images, respectively. It was observed that the proposed robust sphere fitting method outperformed the popular M-estimator sample consensus in terms of radius and center estimation accuracy by a factor of 13, and 14 on average, respectively. Using the proposed eccentricity adjustment, the estimated 3D center of the sphere using modeled eccentricity was superior to the unmodeled case. It was also observed that the accuracy of the estimated 3D center using modeled eccentricity continuously improved as the number of images increased, whereas the unmodeled eccentricity did not show improvements after eight image views. The results of the investigation show that: (i) the proposed method effectively modeled the eccentricity error, and (ii) the effects of eliminating the eccentricity error in the 3D reconstruction become even more pronounced in a larger number of image views.
Reza Maalek; Derek D. Lichti. Correcting the Eccentricity Error of Projected Spherical Objects in Perspective Cameras. Remote Sensing 2021, 13, 3269 .
AMA StyleReza Maalek, Derek D. Lichti. Correcting the Eccentricity Error of Projected Spherical Objects in Perspective Cameras. Remote Sensing. 2021; 13 (16):3269.
Chicago/Turabian StyleReza Maalek; Derek D. Lichti. 2021. "Correcting the Eccentricity Error of Projected Spherical Objects in Perspective Cameras." Remote Sensing 13, no. 16: 3269.
This paper outlines a new framework for the calibration of optical instruments, in particular smartphone cameras, using highly redundant circular black-and-white target fields. New methods were introduced for (i) matching targets between images; (ii) adjusting the systematic eccentricity error of target centres; and (iii) iteratively improving the calibration solution through a free-network self-calibrating bundle adjustment. The proposed method effectively matched circular targets in 270 smartphone images, taken within a calibration laboratory, with robustness to type II errors (false negatives). The proposed eccentricity adjustment, which requires only camera projective matrices from two views, behaved comparably to available closed-form solutions, which require additional a priori object-space target information. Finally, specifically for the case of mobile devices, the calibration parameters obtained using the framework were found to be superior compared to in situ calibration for estimating the 3D reconstructed radius of a mechanical pipe (approximately 45% improvement on average).
Reza Maalek; Derek D. Lichti. Automated calibration of smartphone cameras for 3D reconstruction of mechanical pipes. The Photogrammetric Record 2021, 36, 124 -146.
AMA StyleReza Maalek, Derek D. Lichti. Automated calibration of smartphone cameras for 3D reconstruction of mechanical pipes. The Photogrammetric Record. 2021; 36 (174):124-146.
Chicago/Turabian StyleReza Maalek; Derek D. Lichti. 2021. "Automated calibration of smartphone cameras for 3D reconstruction of mechanical pipes." The Photogrammetric Record 36, no. 174: 124-146.
Detection of non-overlapping ellipses from 2-dimensional (2D) edge points is an essential step towards solving typical photogrammetry problems pertaining to feature detection, calibration, and registration of optical instruments. For instance, circular and spherical black and white calibration and registration targets are represented as ellipses in images. Furthermore, the intersection of a cut plane with cylindrical point clouds generates 2D points following elliptic patterns. To this end, this study proposes a collection of new methods for the automatic and robust detection of non-overlapping ellipses from 2D points. These methods will first be applied to detect circular and spherical targets in images and, second, to detect cylinders in 3D point clouds. The method utilizes the Euclidian ellipticity and a new systematic and generalizable threshold to decide if a set of connected points follow an elliptic pattern. When connected points include outliers, the newly proposed robust Monte Carlo-based ellipse fitting method will be deployed. This method includes three new developments: (i) selecting initial subsamples using a bucketing strategy based on the polar angle of the points; (ii) detecting inlier points by reducing the robust ellipse fitting to a robust circle fitting problem; and (iii) choosing the best inlier set amongst all subsamples using adaptive, systematic, and generalizable selection criteria. A new process is presented to extract cylinders from a point cloud by detecting non-overlapping ellipses from the points projected onto an intersecting cut plane. The proposed methods were compared to established state-of-the-art methods, using simulated and real-world datasets, through the design of four sets of original experiments. The experiments include (i) comparisons of robust ellipse fitting; (ii) sensitivity analysis of the ellipse validation criteria; (iii) comparison of non-overlapping ellipse detection; and (iv) detection of pipes from terrestrial laser scanner point clouds. It was found that the proposed robust ellipse detection was superior to four reliable robust methods, including the popular least median of squares, in both simulated and real-world datasets. The proposed process for detecting non-overlapping ellipses achieved F-measure of 99.3% on real images, compared to 42.4%, 65.6%, and 59.2%, obtained using the methods of Fornaciari, Patraucean, and Panagiotakis, respectively. The proposed cylinder extraction method identified all detectable mechanical pipes in two real-world point clouds collected in laboratory and industrial construction site conditions. The results of this investigation show promise for the application of the proposed methods for automatic extraction of circular targets from images and pipes from point clouds.
Reza Maalek; Derek D. Lichti. Robust detection of non-overlapping ellipses from points with applications to circular target extraction in images and cylinder detection in point clouds. ISPRS Journal of Photogrammetry and Remote Sensing 2021, 176, 83 -108.
AMA StyleReza Maalek, Derek D. Lichti. Robust detection of non-overlapping ellipses from points with applications to circular target extraction in images and cylinder detection in point clouds. ISPRS Journal of Photogrammetry and Remote Sensing. 2021; 176 ():83-108.
Chicago/Turabian StyleReza Maalek; Derek D. Lichti. 2021. "Robust detection of non-overlapping ellipses from points with applications to circular target extraction in images and cylinder detection in point clouds." ISPRS Journal of Photogrammetry and Remote Sensing 176, no. : 83-108.
This manuscript presents a new framework towards automated digital documentation and progress reporting of mechanical pipes in building construction projects, using smartphones. New methods were proposed to optimize video frame rate to achieve a desired image overlap; define metric scale for 3D reconstruction; extract pipes from point clouds; and classify pipes according to their planned bill of quantity radii. The effectiveness of the proposed methods in both laboratory (six pipes) and construction site (58 pipes) conditions was evaluated. It was observed that the proposed metric scale definition achieved sub-millimeter pipe radius estimation accuracy. Both laboratory and field experiments revealed that increasing the defined image overlap improved point cloud quality, pipe classification quality, and pipe radius/length estimation. Overall, it was found possible to achieve pipe classification F-measure, radius estimation accuracy, and length estimation percent error of 96.4%, 5.4 mm, and 5.0%, respectively, on construction sites using at least 95% image overlap.
Reza Maalek; Derek D. Lichti; Shahrokh Maalek. Towards automatic digital documentation and progress reporting of mechanical construction pipes using smartphones. Automation in Construction 2021, 127, 103735 .
AMA StyleReza Maalek, Derek D. Lichti, Shahrokh Maalek. Towards automatic digital documentation and progress reporting of mechanical construction pipes using smartphones. Automation in Construction. 2021; 127 ():103735.
Chicago/Turabian StyleReza Maalek; Derek D. Lichti; Shahrokh Maalek. 2021. "Towards automatic digital documentation and progress reporting of mechanical construction pipes using smartphones." Automation in Construction 127, no. : 103735.
This manuscript presents a new method for fitting ellipses to two-dimensional data using the confocal hyperbola approximation to the geometric distance of points to ellipses. The proposed method was evaluated and compared to established methods on simulated and real-world datasets. First, it was revealed that the confocal hyperbola distance considerably outperforms other distance approximations such as algebraic and Sampson. Next, the proposed ellipse fitting method was compared with five reliable and established methods proposed by Halir, Taubin, Kanatani, Ahn and Szpak. The performance of each method as a function of rotation, aspect ratio, noise, and arc-length were examined. It was observed that the proposed ellipse fitting method achieved almost identical results (and in some cases better) than the gold standard geometric method of Ahn and outperformed the remaining methods in all simulation experiments. Finally, the proposed method outperformed the considered ellipse fitting methods in estimating the geometric parameters of cylindrical mechanical pipes from point clouds. The results of the experiments show that the confocal hyperbola is an excellent approximation to the true geometric distance and produces reliable and accurate ellipse fitting in practical settings.
Reza Maalek; Derek D. Lichti. New confocal hyperbola-based ellipse fitting with applications to estimating parameters of mechanical pipes from point clouds. Pattern Recognition 2021, 116, 107948 .
AMA StyleReza Maalek, Derek D. Lichti. New confocal hyperbola-based ellipse fitting with applications to estimating parameters of mechanical pipes from point clouds. Pattern Recognition. 2021; 116 ():107948.
Chicago/Turabian StyleReza Maalek; Derek D. Lichti. 2021. "New confocal hyperbola-based ellipse fitting with applications to estimating parameters of mechanical pipes from point clouds." Pattern Recognition 116, no. : 107948.
This study presented established methods, along with new algorithmic developments, to automate point cloud processing in support of the Field Information Modeling (FIM)™ framework. More specifically, given a multi-dimensional (n-D) designed information model, and the point cloud’s spatial uncertainty, the problem of automatic assignment of point clouds to their corresponding model elements was considered. The methods addressed two classes of field conditions, namely (i) negligible construction errors and (ii) the existence of construction errors. Emphasis was given to defining the assumptions, potentials, and limitations of each method in practical settings. Considering the shortcomings of current frameworks, three generic algorithms were designed to address the point-cloud-to-model assignment. The algorithms include new developments for (i) point cloud vs. model comparison (negligible construction errors), (ii) robust point neighborhood definition, and (iii) Monte-Carlo-based point-cloud-to-model surface hypothesis testing (existence of construction errors). The effectiveness of the new methods was demonstrated in real-world point clouds, acquired from construction projects, with promising results. For the overall problem of point-cloud-to-model assignment, the proposed point cloud vs. model and point-cloud-to-model hypothesis testing methods achieved F-measures of 99.3% and 98.4%, respectively, on real-world datasets.
Reza Maalek. Field Information Modeling (FIM)™: Best Practices Using Point Clouds. Remote Sensing 2021, 13, 967 .
AMA StyleReza Maalek. Field Information Modeling (FIM)™: Best Practices Using Point Clouds. Remote Sensing. 2021; 13 (5):967.
Chicago/Turabian StyleReza Maalek. 2021. "Field Information Modeling (FIM)™: Best Practices Using Point Clouds." Remote Sensing 13, no. 5: 967.
This study presents established methods, along with new algorithmic developments, to automate the point cloud processing within the Field Information Modeling (FIM)™ framework. More specifically, given an n-D designed information model, and the point cloud’s spatial uncertainty, the problem of automatic assignment of the point clouds to their corresponding elements within the designed model is considered. The methods addressed two classes of field conditions, namely, (i) negligible construction errors; and (ii) existence of construction errors. The emphasis was given to describing and defining the assumptions in each method and shed light on some of their potentials and limitations in practical settings. Considering the shortcomings of current point cloud processing frameworks, three new and generic algorithms were developed to help solve the point cloud to model assignment in field conditions with both negligible, and existence (or speculation) of construction errors. The effectiveness of the new methods was demonstrated in real-world point clouds, acquired from construction projects, with promising results.
Reza Maalek. Field Information Modeling (FIM)™: Best Practices using Point Clouds. 2021, 1 .
AMA StyleReza Maalek. Field Information Modeling (FIM)™: Best Practices using Point Clouds. . 2021; ():1.
Chicago/Turabian StyleReza Maalek. 2021. "Field Information Modeling (FIM)™: Best Practices using Point Clouds." , no. : 1.
This manuscript provides a robust framework for the extraction of common structural components, such as columns, from terrestrial laser scanning point clouds acquired at regular rectangular concrete construction projects. The proposed framework utilizes geometric primitive as well as relationship-based reasoning between objects to semantically label point clouds. The framework then compares the extracted objects to the planned building information model (BIM) to automatically identify the as-built schedule and dimensional discrepancies. A novel method was also developed to remove redundant points of a newly acquired scan to detect changes between consecutive scans independent of the planned BIM. Five sets of point cloud data were acquired from the same construction site at different time intervals to assess the effectiveness of the proposed framework. In all datasets, the framework successfully extracted 132 out of 133 columns and achieved an accuracy of 98.79% for removing redundant surfaces. The framework successfully determined the progress of concrete work at each epoch in both activity and project levels through earned value analysis. It was also shown that the dimensions of 127 out of the 132 columns and all the slabs complied with those in the planned BIM.
Reza Maalek; Derek D. Lichti; Janaka Y. Ruwanpura. Automatic Recognition of Common Structural Elements from Point Clouds for Automated Progress Monitoring and Dimensional Quality Control in Reinforced Concrete Construction. Remote Sensing 2019, 11, 1102 .
AMA StyleReza Maalek, Derek D. Lichti, Janaka Y. Ruwanpura. Automatic Recognition of Common Structural Elements from Point Clouds for Automated Progress Monitoring and Dimensional Quality Control in Reinforced Concrete Construction. Remote Sensing. 2019; 11 (9):1102.
Chicago/Turabian StyleReza Maalek; Derek D. Lichti; Janaka Y. Ruwanpura. 2019. "Automatic Recognition of Common Structural Elements from Point Clouds for Automated Progress Monitoring and Dimensional Quality Control in Reinforced Concrete Construction." Remote Sensing 11, no. 9: 1102.
The application of terrestrial laser scanners for fabrication verification of the components of pre-fabricated modules (such as pipes and flanges) is growing markedly in the oil and gas industry. However, there remains strong reliance on impractical and error-prone manual or semi-automated methods to extract semantic information from the acquired point clouds. This manuscript presents a generic and robust framework for automatic extraction of pipe and flange pairs in pre-fabricated modules using the geometric primitives of the point cloud. It has been tested on two point cloud datasets with different data quality and density acquired from different sites. Our method was able to extract all 49 pipes and flanges correctly and improved the accuracy of the estimated centers and normal vectors by 171% and 145%, respectively, when compared to results from commercially-available verification software. The experiments' results show great promise for generic applicability of the proposed system for fabrication verification purposes.
Reza Maalek; Derek D. Lichti; Ryan Walker; Adam Bhavnani; Janaka Y. Ruwanpura. Extraction of pipes and flanges from point clouds for automated verification of pre-fabricated modules in oil and gas refinery projects. Automation in Construction 2019, 103, 150 -167.
AMA StyleReza Maalek, Derek D. Lichti, Ryan Walker, Adam Bhavnani, Janaka Y. Ruwanpura. Extraction of pipes and flanges from point clouds for automated verification of pre-fabricated modules in oil and gas refinery projects. Automation in Construction. 2019; 103 ():150-167.
Chicago/Turabian StyleReza Maalek; Derek D. Lichti; Ryan Walker; Adam Bhavnani; Janaka Y. Ruwanpura. 2019. "Extraction of pipes and flanges from point clouds for automated verification of pre-fabricated modules in oil and gas refinery projects." Automation in Construction 103, no. : 150-167.
Automated segmentation of planar and linear features of point clouds acquired from construction sites is essential for the automatic extraction of building construction elements such as columns, beams and slabs. However, many planar and linear segmentation methods use scene-dependent similarity thresholds that may not provide generalizable solutions for all environments. In addition, outliers exist in construction site point clouds due to data artefacts caused by moving objects, occlusions and dust. To address these concerns, a novel method for robust classification and segmentation of planar and linear features is proposed. First, coplanar and collinear points are classified through a robust principal components analysis procedure. The classified points are then grouped using a new robust clustering method, the robust complete linkage method. A robust method is also proposed to extract the points of flat-slab floors and/or ceilings independent of the aforementioned stages to improve computational efficiency. The applicability of the proposed method is evaluated in eight datasets acquired from a complex laboratory environment and two construction sites at the University of Calgary. The precision, recall, and accuracy of the segmentation at both construction sites were 96.8%, 97.7% and 95%, respectively. These results demonstrate the suitability of the proposed method for robust segmentation of planar and linear features of contaminated datasets, such as those collected from construction sites.
Reza Maalek; Derek D Lichti; Janaka Y Ruwanpura. Robust Segmentation of Planar and Linear Features of Terrestrial Laser Scanner Point Clouds Acquired from Construction Sites. Sensors 2018, 18, 819 .
AMA StyleReza Maalek, Derek D Lichti, Janaka Y Ruwanpura. Robust Segmentation of Planar and Linear Features of Terrestrial Laser Scanner Point Clouds Acquired from Construction Sites. Sensors. 2018; 18 (3):819.
Chicago/Turabian StyleReza Maalek; Derek D Lichti; Janaka Y Ruwanpura. 2018. "Robust Segmentation of Planar and Linear Features of Terrestrial Laser Scanner Point Clouds Acquired from Construction Sites." Sensors 18, no. 3: 819.
Reza Maalek; Farnaz Sadeghpour. Accuracy assessment of ultra-wide band technology in locating dynamic resources in indoor scenarios. Automation in Construction 2016, 63, 12 -26.
AMA StyleReza Maalek, Farnaz Sadeghpour. Accuracy assessment of ultra-wide band technology in locating dynamic resources in indoor scenarios. Automation in Construction. 2016; 63 ():12-26.
Chicago/Turabian StyleReza Maalek; Farnaz Sadeghpour. 2016. "Accuracy assessment of ultra-wide band technology in locating dynamic resources in indoor scenarios." Automation in Construction 63, no. : 12-26.
Efficient onsite data acquisition of a construction project enables the comparison of the actual state of the project to the as-plan state so that potential delays can be identified early within the project life cycle. Traditionally, onsite data are collected manually, a time consuming, costly and error-prone task, and therefore not justifiable in modern construction management. To overcome the challenges corresponding to such manual approaches, the application of automated progress monitoring of construction sites has attracted the attention of researchers. To enable an effective application, it is necessary to evaluate the reliability of the available technologies in collecting onsite data. In this paper, a qualitative evaluation of the applicability of the state-of-the-art automated progress monitoring technologies, namely camera, LiDAR, and 3D range imaging, has been carried out. A set of experiments has been carried out to compare the time of data collection for each technology. LiDAR provides the most accurate 3D estimates. The time of data collection of the Leica HDS6100 laser scanner is shown to be seven times faster than that of the DSLR camera in an indoor construction site simulated laboratory. However, the cost of LiDAR devices is the major economical drawback of the technology.
Reza Maalek; Janaka Ruwanpura; Kamal Ranaweera. Evaluation of the State-of-the-Art Automated Construction Progress Monitoring and Control Systems. Construction Research Congress 2014 2014, 1023 -1032.
AMA StyleReza Maalek, Janaka Ruwanpura, Kamal Ranaweera. Evaluation of the State-of-the-Art Automated Construction Progress Monitoring and Control Systems. Construction Research Congress 2014. 2014; ():1023-1032.
Chicago/Turabian StyleReza Maalek; Janaka Ruwanpura; Kamal Ranaweera. 2014. "Evaluation of the State-of-the-Art Automated Construction Progress Monitoring and Control Systems." Construction Research Congress 2014 , no. : 1023-1032.
Automating the progress monitoring and control process is of great interest to industry practitioners to help improve the limitations associated with the current manual data collection and analysis practices. Two remote sensing technologies, namely, Light Detection and Ranging (LiDAR) and digital camera, are widely used to acquire 3D point clouds as a means of measuring the “scope of the work performed” of structural elements. However, to assign the collected 3D point clouds to their corresponding structural element, current object-based recognition models use the as-planned 4D model, which may not be reliable in cases where the locations of the as-built structure differ from those of the planned, and/or the planned 4D model is not available with sufficient detail. Here, a novel method is proposed to eliminate the dependency on the as-planned data by automatically generating the 3D/4D as-built model through a robust Principal Component Analysis-based (PCA) segmentation algorithm. The proposed system is also independent of the technology used to capture the 3D point clouds. To evaluate the reliability of the proposed automated as-built model generation procedure, two sets of LiDAR data from the "Mechanics of Materials" laboratory and the "Graduate Student Hall of Residence" construction site at the University of Calgary were collected. A novel method of automated registration of the as-built model to the planned model coordinate system is also proposed through which the compliance of the planned vs. actual dimensions of corresponding structural elements are examined. The results of the two experiments demonstrate the applicability of the proposed methods for the automatic generation of the 3D/4D as-built model and the dimension compliance control of structural elements.
Reza Maalek; Derek Lichti; Janaka Ruwanpura. Development of an automated 3D/4D as-built model generation system for construction progress monitoring and quality control. 2021, 1 .
AMA StyleReza Maalek, Derek Lichti, Janaka Ruwanpura. Development of an automated 3D/4D as-built model generation system for construction progress monitoring and quality control. . 2021; ():1.
Chicago/Turabian StyleReza Maalek; Derek Lichti; Janaka Ruwanpura. 2021. "Development of an automated 3D/4D as-built model generation system for construction progress monitoring and quality control." , no. : 1.