An airborne rooftop point cloud extraction method based on RANSAC plane detection for seed points
Airborne LiDAR technology has become an effective means to quickly acquire three-dimensional(3D)digital models of urban buildings,and the extraction of rooftop point clouds is the key to the reconstruction of 3D digital models of buildings.To solve this problem,an airborne rooftop point cloud extraction method based on random sampling consensus algorithm(RANSAC)plane detection for seed points was proposed.Firstly,the ground point was separated from the point cloud by perigee separation,retaining the point cloud of buildings and a small amount of canopy point cloud.Secondly,the RANSAC plane detection was used to select seed points,which were almost all rooftop points,and the proportion of non-rooftop points was very low.Finally,the seed point was taken as the initial growth point,and the normal angle between the seed point and its neighborhood point and the distance difference in the Z direction were used as the clustering features to extract the rooftop point.Experimental results show that the proposed method achieves good results in rooftop point extraction on different data sets.Point cloud density has a certain influence on the extraction results of rooftop points,and higher point cloud density is conducive to the extraction of rooftop points with obvious geometric features.In addition,the seed points selected by this method have high accuracy,and the influence of non-rooftop points is very limited.In summary,this method can effectively extract rooftop points in airborne point clouds and provide important data support for its applications in the 3D reconstruction of buildings and urban planning.
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