A FRAMEWORK TO EXTRACT STRUCTURAL ELEMENTS OF CONSTRUCTION SITE FROM LASER SCANNING
This paper proposes a framework to automatic extract structural elements of reinforced concrete buildings from laser scanning data, which can be used in dimensional quality control and surface defect identification. The framework deploys both spatial information of a point cloud and contextual knowledge of building structures to extract the structural elements in a sequential order: floors and ceilings, walls, columns and beams. The method starts to extract a subset data containing candidate points of the structural elements and segmentation methods and filtered based contextual knowledge subsequently apply to obtain the final points of the elements. In this framework, a combination between kernel density estimation and a cell-patch-based region growing are to extract the floors, ceilings and walls, while the points of the columns and beams are achieved through a voxel-based region growing. 23.5 million data points of one story of the building is used to test a performance of the proposed framework. Results showed all structural components are successfully extracted. Moreover, completeness, correctness, and quality indicated through point-based performance report larger than 96.0%, 96.9% and 93.0%, respectively while overlap rates of the floors, ceilings and walls are no less than 95.3%. Interestingly, an executing time of the proposed method is about 7.7seconds per a million point.