Automated construction progress monitoring using as-planned building information modeling (BIM) and as-built point cloud data integration has substantial potential and could lead to the fast-tracking of construction work and identifying discrepancies. Laser scanning is becoming mainstream for conducting construction surveys due to the accuracy of the data obtained and the speed of the process; however, construction progress monitoring techniques are still limited because of the complexity of the methods, incompleteness of the scanned areas, or the obstructions by temporary objects in construction sites. The novel method proposed within this study enables the extracting of BIM data, calculating the plane equation of the faces, and performing a point-to-plane distance estimation, which successfully overcomes some limitations reported in previous studies, including automated object detection in an occluded environment. Six datasets consisting of point clouds collected by static and mobile laser scanning techniques including the corresponding BIM models were analyzed. In all the analyzed cases, the proposed method automatically detected whether the construction of an object was completed or not in the as-built point cloud compared to the provided as-planned BIM model.