Shahrokh Maalek

Reza Maalek, Shahrokh Maalek

Advancements in optical metrology has enabled documentation of dense 3D point clouds of cultural heritage sites. For large scale and continuous digital documentation, processing of dense 3D point clouds becomes computationally cumbersome, and often requires additional hardware for data management, increasing the time cost, and complexity of projects. To this end, this manuscript presents an original approach to generate fast and reliable semantic digital models of heritage hemispherical domes using only two images. New closed formulations were derived to establish the relationships between spheres and their projected ellipses onto images, which fostered the development of a new automatic framework for as-built generation of spheres. The effectiveness of the proposed method was evaluated under both laboratory and real-world datasets. The results revealed that the proposed method achieved as-built modeling accuracy of around 6mm, while improving the computation time by a factor of 7, when compared to established point cloud processing methods.

Reza Maalek, Derek Lichti, Shahrokh Maalek

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%, 5.4mm, and 5.0%, respectively, on construction sites using at least 95% image overlap.