Robust 3D reconstruction of building surfaces from point clouds based on structural and closed constraints

Abstract The reconstruction of buildings using inhomogeneous and unstructured point clouds is a challenging task for photogrammetry and computer vision research communities. A new approach for 3D building surface modeling, based on closed constraints, is proposed. First, a region growth algorithm is applied to fit the input point clouds by a set of candidate planes. Then, additional candidate planes are generated from the initial planes according to a rigid transformation followed by expanding the original primitive set to the candidate model set through generation rules. Furthermore, an energy function is employed to combine the data fitting errors with the structural constraints at the model selection stage. Finally, the 3D building surface model is generated from the candidate set through energy minimization. More precisely speaking, we adopt the surface optimization scheme that enforces the 3D polygonal surfaces of the building to be consistent with a priori geometric structures. Our approach was assessed using multi-source datasets with different densities, noise levels covering diverse and complex structures. The experimental results demonstrated that the proposed approach achieves better accuracy and robustness than those of several state-of-the-art methods.