  We propose a new method for estimating the relative pose between two images,
where we jointly learn keypoint detection, description extraction, matching and
robust pose estimation. While our architecture follows the traditional pipeline
for pose estimation from geometric computer vision, all steps are learnt in an
end-to-end fashion, including feature matching. We demonstrate our method for
the task of visual localization of a query image within a database of images
with known pose. Pairwise pose estimation has many practical applications for
robotic mapping, navigation, and AR. For example, the display of persistent AR
objects in the scene relies on a precise camera localization to make the
digital models appear anchored to the physical environment. We train our
pipeline end-to-end specifically for the problem of visual localization. We
evaluate our proposed approach on localization accuracy, robustness and runtime
speed. Our method achieves state of the art localization accuracy on the 7
Scenes dataset.
