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