  Humans can build a mental map of a geographical area to find their way and
recognize places. The basic task we consider is geo-localization - finding the
pose (position & orientation) of a camera in a large 3D scene from a single
image. We aim to experimentally explore the role of geometry in
geo-localization in a convolutional neural network (CNN) solution. We do so by
ignoring the often available texture of the scene. We therefore deliberately
avoid using texture or rich geometric details and use images projected from a
simple 3D model of a city, which we term lean images. Lean images contain
solely information that relates to the geometry of the area viewed (edges,
faces, or relative depth). We find that the network is capable of estimating
the camera pose from the lean images, and it does so not by memorization but by
some measure of geometric learning of the geographical area. The main
contributions of this paper are: (i) providing insight into the role of
geometry in the CNN learning process; and (ii) demonstrating the power of CNNs
for recovering camera pose using lean images.
