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Automatic complex building reconstruction from LIDAR based on hierarchical structure analysis

Since manual surface reconstruction is very costly and time consuming, the development of automatic algorithm is of great importance. In this paper a fully automated technique based on hierarchical structure analysis of the building to extract urban building models from LIDAR data is presented. In the processing of reconstruction, the existing automatic algorithm can solve some simple building reconstructions, such as flat roof, gabled roof. As to complex buildings, many researchers use external information or manual interaction for help because of the complexity of the reconstruction and the uncertainty of the building models especially in urban areas. The contour has the characteristics of closed loop, not intersect and deterministic topological relationship, which can be used to extract building ROI (region of interesting). A contours tree is constructed, the topological relationships between the different contours which extracted by TIN from the LIDAR data are established, then the relationships among each hierarchical model can be determined by the analysis of the topological relationship among contour clusters and a component tree corresponding to the building can be constructed by tracing the contours tree. The accurate edges of hierarchical model can be gained by the "polarized cornerity index"-based polygonal approximation of the contour. Especially, a 3D model recognition based on 2D shape recognition is employed. According to the characteristics of the contours, the category of the primitive parts can be classified. We assemble the hierarchical models by using the topological relationships among layers, then, the complete model of the building can be obtained. Experimental results show that the proposed algorithm is suitable for automatically producing building models including most complex buildings from LIDAR data in urban areas.