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In this paper, based on the assumption that the object boundaries (e.g.,buildings) from the over-view data should coincide with footprints offa\c{c}ade 3D points generated from street-view photogrammetric images, we aimto address this problem by proposing a fully automated geo-registration methodfor cross-view data, which utilizes semantically segmented object boundaries asview-invariant features under a global optimization framework throughgraph-matching: taking the over-view point clouds generated fromstereo/multi-stereo satellite images and the street-view point clouds generatedfrom monocular video images as the inputs, the proposed method models segmentsof buildings as nodes of graphs, both detected from the satellite-based andstreet-view based point clouds, thus to form the registration as agraph-matching problem to allow non-rigid matches; to enable a robust solutionand fully utilize the topological relations between these segments, we proposeto address the graph-matching problem on its conjugate graph solved through abelief-propagation algorithm. The matched nodes will be subject to a furtheroptimization to allow precise-registration, followed by a constrained bundleadjustment on the street-view image to keep 2D29 3D consistencies, which yieldswell-registered street-view images and point clouds to the satellite pointclouds.

A Graph-Matching Approach for Cross-view Registration of Over-view 2 and Street-view based Point Clouds