Automated Model-Based Finding of 3D Objects in Cluttered Construction Point Cloud Models

Finding construction components in cluttered point clouds is a critical pre-processing task that requires intensive and manual operations. Accurate isolation of an object from point clouds is a key for further processing steps such as positive identification, scan-to-building information modeling (BIM), and robotic manipulation. Manual isolaton is tedious, time consuming, and disconnected from the automated tasks involved in the process. This article adapts and examines a method for finding objects within 3D point clouds robustly, quickly, and automatically. A local feature on a pair of points is employed for representing 3D shapes. The method has three steps: (1) offline model library generation, (2) online searching and matching, and (3) match refinement and isolation. Experimental tests are carried out for finding industrial (curvilinear) and structural (rectilinear) elements. The method is verified under various circumstances in order to measure its performance toward addressing the major challenges involved in 3D object finding. Results show that the method is sufficiently quick and robust to be integrated with automated process control frameworks.