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Visual localization is the problem of estimating the position and orientationfrom which a given image (or a sequence of images) is taken in a known scene.It is an important part of a wide range of computer vision and roboticsapplications, from self-driving cars to augmented/virtual reality systems.Visual localization techniques should work reliably and robustly under a widerange of conditions, including seasonal, weather, illumination and man-madechanges. Recent benchmarking efforts model this by providing images underdifferent conditions, and the community has made rapid progress on thesedatasets since their inception. However, they are limited to a few geographicalregions and often recorded with a single device. We propose a new benchmark forvisual localization in outdoor scenes, using crowd-sourced data to cover a widerange of geographical regions and camera devices with a focus on the failurecases of current algorithms. Experiments with state-of-the-art localizationapproaches show that our dataset is very challenging, with all evaluatedmethods failing on its hardest parts. As part of the dataset release, weprovide the tooling used to generate it, enabling efficient and effective 2Dcorrespondence annotation to obtain reference poses.

CrowdDriven: A New Challenging Dataset for Outdoor Visual Localization