A High-Precision Extraction Method for Building Outlines Based on Point Cloud and Topological Reasoning
A contour extraction method based on oblique photography data was proposed to address the problem of extracting building outlines in large-scale real 3D models.Firstly,the building point cloud data was separated from the oblique 3D model by using regional growth and Euclidean clustering methods.Then,the RANSAC algorithm and normal constraint were used to achieve accurate elevation.Next,the missing surface was generated by topological reasoning.Finally,generate complete,closed,smooth,and consistent building outlines.The research results have showed that the building outlines extracted by this method have quite low errors on 20 randomly selected buildings with different sizes and styles.Specifically,the root mean square error (RMSE) of plane position deviation is 0.05 m,and the RMSE of geometric shape deviation (length and width) and contour areas are 0.06 m,0.07 m,and 1.33％,respectively.Besides,it is simple and efficient,and suitable for extracting building outlines from large-scale real 3D models.
contour line extractionoblique photography modelRANSAC algorithmmain baselinetopology reasoning
万方数据
维普
