Research on the incomplete point cloud data repairing of the large-scale scene buildings

Abstract

Both airborne and mobile LiDAR are suitable for the fast information acquisition of large-scale urban buildings, which respectively express the rooftop and the facade information of buildings. The fusion of airborne and mobile LiDAR can provide a relatively complete building point cloud, which is beneficial to constructing precise building models. Due to the different data acquisition methods, airborne LiDAR can get the relatively complete point cloud data of the building rooftop, while the building facade point cloud from mobile LiDAR is often incomplete or even missing because of the tree occlusion or the limitation of operating conditions, which to some extent limits the application of the fused data. To solve the problems above, a method of building point cloud similarity matching based on airborne LiDAR and mobile LiDAR data fusion is proposed. Finally a test result is provided to illustrate the proposed algorithm.