Building Roof Reconstruction Based on Residue Anomaly Analysis and Shape Descriptors from Lidar and Optical Data

Abstract

Recently, combined use of lidar and optical data has been extensively employed to achieve 3D modeling of buildings. In the first step of this research, lidar point cloud is clustered into roof's major and minor planes. A FAST algorithm is used to extract roof corners from a 2D optical data. Afterwards, residue of the anomalies is obtained through subtraction of main planes' DSM from DSM of the lidar point cloud. Using a Canny operator, edges of the residue's anomalies image are extracted and then superimposed on the optical image to find appropriate corners. In the second step, shape descriptors related to all possible polygons formed by the corners are computed and compared against the ones, which are obtained from the extracted edges. Then, the polygon with the most similar shape descriptor would be chosen as the optimal polygon representing the roof structure of interest. In third step, planes are intersected and initial 3D roof model is reconstructed. It is added to 3D optimized roof model formed by 2D optimal polygon and minor planes which results in secondary 3D reconstructed roof model. Finally, a wire frame model is generated by vertexes. The results indicate efficiency of the residue of the anomalies and shape descriptors for buildings' roof modeling.