1Technical University of Denmark
2Dansk Drone Kompagni ApS
3University of Zaragoza
* denotes equal contribution
Place recognition and visual localization are particularly challenging in wide baseline configurations. In this paper, we present the Danish Airs and Grounds (DAG) dataset, a large collection of street-level and aerial images targeting such cases. Its main challenge lies in the extreme viewing-angle difference between query and reference images with consequent changes in illumination and perspective. We propose a map-to-image re-localization pipeline that first estimates a dense 3D reconstruction from the aerial images and then matches query street-level images to street-level renderings of the 3D model.
Street-level to aerial localization pipeline. We generate a dense and accurate multi-view 3D reconstruction from aerial images. Using this 3D model we can render a database of photo realistic images for real-image-to-render retrieval from actual street-view queries. Our experiments show that this is an effective pipeline for air-to-ground retrieval and localization.