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. © 2023 American Society of Civil Engineers.