| description abstract | Photogrammetry has gained the interest of professionals and researchers for activities related to construction projects’ progress monitoring via attaining precise 3D point models. However, the precision of the generated models is directly linked with the precise scaling of the point cloud to ground truth dimensions (GTDs). Available scaling-up procedures for the close-range photogrammetry technique are complex, time consuming, and require human intervention, which adds the risk of error in the scaled-up model dimensions. Such a scenario creates hesitation among industry professionals toward implementing point cloud technologies. This paper devises an automated scaling-up methodology to overcome the said concerns by considering the construction progress monitoring theme. The intact process of automated scaling up of point cloud model to GTDs is controlled by two main parameters, that is, Python-based modules and designed ArUco-supported controlled markers. Remarkable outcomes are achieved with less than 1% scaled-up error compared with GTDs, which will improve the confidence of industry professionals toward point cloud technologies. Photogrammetry applications have been adopted in several domains and the optimum usage of attained models can be executed with 3D replicas having precise details of surface features and geometry. Therefore, to attain 3D point cloud models with ground truth dimensions (GTDs), or actual dimensions of the targeted object the practitioners mostly follow the markers/ground control points (GCPs) technique (minimum three GCPs/markers), manual scaling, or georeferencing data. However, the accuracy of traditional GCPs/markers’ technique and manual rescaling is dependent on the experience of the site staff/operator, and error chances may increase with the increasing number of GCPs/markers, whereas the georeferencing data-based technique is more technical and complex. Therefore, this paper developed an automated system for scaling up 3D point cloud models to GTDs with minimal human involvement. The system works with the help of specialized designed markers known as ArUco-supported controlled markers (ASCM). Only one ASCM marker is placed beside the targeted object for imaging; the devised system detects the marker in the images and rescales the developed point cloud model following the designed strategy. The system has high accuracy and can easily be implemented for scaling up close-range photogrammetry models in any domain. | |
