Construction progress estimation to ensure high productivity and quality is an essential component of the daily construction cycle. However, using three-dimensional (3D) laser-scanned point clouds for the purpose of measuring deviations between as-built structures and as-planned building information models (BIMs) remains cumbersome due to difficulties in data registration, segmentation, annotation, and modeling in large-scale point clouds. This research proposes the use of a data-driven deep learning framework to automatically detect and classify building elements from a laser-scanned point cloud scene. The point cloud is first converted into a graph representation, in which vertices represent points and edges represent connections between points within a fixed distance. An edge-based classifier is used to discard edges connecting points from different objects and to form connected components from points in the same object. Next, a point-based object classifier is used to determine the type of building component based on the segmented points and augmented with context from surrounding points. Finally, each detected object is matched with a corresponding BIM entity based on the nearest neighbor in the feature space.