A framework is presented for estimating the pose of a camera based on images extracted from a single omnidirectional image of an urban scene, given a 2D map with building outlines with no 3D geometric information nor appearance data. The framework attempts to identify vertical corner edges of buildings in the query image, which we term VCLH, as well as the neighboring plane normals, through vanishing point analysis. A bottom-up process further groups VCLH into elemental planes and subsequently into 3D structural fragments modulo a similarity transformation. A geometric hashing lookup allows us to rapidly establish multiple candidate correspondences between the structural fragments and the 2D map building contours. A voting-based camera pose estimation method is then employed to recover the correspondences admitting a camera pose solution with high consensus. In a dataset that is even challenging for humans, the system returned a top-30 ranking for correct matches out of 3600 camera pose hypotheses (0.83% selectivity) for 50.9% of queries.