Completing point clouds using structural constraints for large-scale points absence in 3D building reconstruction

The completion of point cloud directly affects the accuracy of primitive parameter extraction and 3D reconstruction. Due to the limitations of sensor scanning, point cloud are often incomplete due to occlusions during the data collection. To solve the problem of incomplete building point clouds and to support geometric detail LoD2 reconstruction, a point cloud completion method with structural constraint is proposed in this paper. First, based on the idea of cloth simulation, regularity and planarity of the building were used to impose structural constraints on the cloth nodes. Combined with the rigidity of the building, the force rules of nodes under the distribution of the building structure are formulated. Then, the sinking and rebounding stages of the node were simulated to recover the position of point cloud in the area of interest. Finally, the missing surface in each façade direction was completed by attitude adjustment. The solution of a multi-layer structural façade was realized by the selection of parameters during the sinking and rebounding stage. The feasibility and accuracy of the proposed point cloud completion method have been verified through experiments. It is demonstrated that the proposed method can improve the integrity of a building point cloud by achieving more than 90% coverage. Compared with existing methods, the proposed method can deal with the problem of large-scale structure missing from buildings with fewer limitations. The patched points do not rely on the geometric primitives' extraction and are based on the distribution of existing points without redundant observations, so the patched points adapt better to the existing building points. The proposed method has enhanced the utilization of point clouds and improved the detail of LoD2 reconstruction. It provides a new feasible method for point cloud completion. The code of the method is available at https://github.com/bufanzhao/point-cloud-completion.git.