Improving progress monitoring by fusing point clouds, semantic data and computer vision

Automated construction-progress monitoring enables the required transparency for improved process control, and is thus being increasingly adopted by the construction industry. Many recent approaches use Scan-to/vs-BIM methods for capturing the as-built status of large construction sites. However, they often lack accuracy or are incomplete due to occluded elements and reconstruction inaccuracies. To overcome these limitations and exploit the rich project knowledge from the design phase, the authors propose taking advantage of the extensive geometric-semantic information provided by Building Information Models. In particular, valuable knowledge on the construction processes is inferred from BIM objects and their precedence relationships. SfM methods enable 3D building elements to be located and projected into the picture's 2D coordinate system. On this basis, the paper presents a machine-learning-based object-detection approach that supports progress monitoring by verifying element categories compared to the expected data from the digital model. The results show that, depending on the type of construction and the type of occlusions, the detection of built elements can rise by up to 50% compared to an SfM-based, purely geometric as-planned vs. as-built comparison.