Alignment of 3D Building Models with Satellite Images Using Extended Chamfer Matching

Large scale alignment of 3D building models and satellite images has many applications ranging from realistic 3D city modeling to urban planning. In this paper, we address this problem by matching the 2D projection of the building roofs and detected edges of satellite images. To better handle noise and occlusions in alignment, the proposed approach seeks an optimal matching location using an extended Chamfer matching algorithm. In addition the proposed approach attempt to optimize the alignment within large region using a global constraint. We show that the proposed approach can estimate the alignment of matching parts and produce robust result under occlusion. We test the proposed algorithm on two different datasets that covers the downtown areas of San Francisco and Chicago. The results show that the proposed algorithm significantly improves the registration accuracy while maintaining consistent performance.