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Humans can orient themselves in their 3D environments using simple 2D maps.Differently, algorithms for visual localization mostly rely on complex 3D pointclouds that are expensive to build, store, and maintain over time. We bridgethis gap by introducing OrienterNet, the first deep neural network that canlocalize an image with sub-meter accuracy using the same 2D semantic maps thathumans use. OrienterNet estimates the location and orientation of a query imageby matching a neural Bird's-Eye View with open and globally available maps fromOpenStreetMap, enabling anyone to localize anywhere such maps are available.OrienterNet is supervised only by camera poses but learns to perform semanticmatching with a wide range of map elements in an end-to-end manner. To enablethis, we introduce a large crowd-sourced dataset of images captured across 12cities from the diverse viewpoints of cars, bikes, and pedestrians. OrienterNetgeneralizes to new datasets and pushes the state of the art in both roboticsand AR scenarios. The code and trained model will be released publicly.

OrienterNet: Visual Localization in 2D Public Maps with Neural Matching