  We propose an approach to estimate the 6DOF pose of a satellite, relative to
a canonical pose, from a single image. Such a problem is crucial in many space
proximity operations, such as docking, debris removal, and inter-spacecraft
communications. Our approach combines machine learning and geometric
optimisation, by predicting the coordinates of a set of landmarks in the input
image, associating the landmarks to their corresponding 3D points on an a
priori reconstructed 3D model, then solving for the object pose using
non-linear optimisation. Our approach is not only novel for this specific pose
estimation task, which helps to further open up a relatively new domain for
machine learning and computer vision, but it also demonstrates superior
accuracy and won the first place in the recent Kelvins Pose Estimation
Challenge organised by the European Space Agency (ESA).
