Knowledge-based 3D reconstruction of bridge structures using UAV-based photogrammetric point cloud

Abstract

Due to the rapid developments of smart cities, 3D modeling of urban infrastructures has become essential for making decisions in urban management and planning applications. 3D modeling of the bridge structure, as one of the most important civil structures in transportations, includes many challenges due to geometrical and structural complexities. A knowledge-based approach based on a 2D projection method is proposed for automatic 3D reconstruction of the main bridge elements including the railing, body, piers, and abutment using dense point clouds of unmanned aerial vehicle images. The knowledge is the geometric relations between the bridge elements to model different types of bridge structures. In the first step, the input point cloud is segmented into the major elements using a fuzzy c-means clustering algorithm. Next, the 2D model of each element is generated using a 2D projection-based reconstruction technique. Then 3D models of elements are created by extruding 2D models into the 3D space. Finally, an integrated 3D model of the bridge structure is formed by merging individual 3D models in a CAD format. In order to evaluate the performance of the proposed modeling framework, the length differences of the elements are compared with a reference model. In addition, the geometric distances between the final models and the input point cloud are calculated. Accordingly, a median error of about 3 cm between the length of the elements in the reference and reconstructed models and 3.8 cm between the reconstructed models and corresponding point clouds highlight the effectiveness of the proposed method to generate 3D models of different bridges.