Konrad Karner

Fusion of Feature- and Area-Based Information for Urban Buildings Modeling from Aerial Imagery

Accurate and realistic building models of urban environments are increasingly important for applications, like virtual tourism or city planning. Initiatives like Virtual Earth or Google Earth are aiming at offering virtual models of all major cities world wide. The prohibitively high costs of manual generation of such models explain the need for an automatic workflow. This paper proposes an algorithm for fully automatic building reconstruction from aerial images. Sparse line features delineating height discontinuities and dense depth data providing the roof surface are combined in an innovative manner with a global optimization algorithm based on Graph Cuts. The fusion process exploits the advantages of both information sources and thus yields superior reconstruction results compared to the indiviual sources. The nature of the algorithm also allows to elegantly generate image driven levels of detail of the geometry. The algorithm is applied to a number of real world data sets encompassing thousands of buildings. The results are analyzed in detail and extensively evaluated using ground truth data.