  Being capable of estimating the pose of uncooperative objects in space has
been proposed as a key asset for enabling safe close-proximity operations such
as space rendezvous, in-orbit servicing and active debris removal. Usual
approaches for pose estimation involve classical computer vision-based
solutions or the application of Deep Learning (DL) techniques. This work
explores a novel DL-based methodology, using Convolutional Neural Networks
(CNNs), for estimating the pose of uncooperative spacecrafts. Contrary to other
approaches, the proposed CNN directly regresses poses without needing any prior
3D information. Moreover, bounding boxes of the spacecraft in the image are
predicted in a simple, yet efficient manner. The performed experiments show how
this work competes with the state-of-the-art in uncooperative spacecraft pose
estimation, including works which require 3D information as well as works which
predict bounding boxes through sophisticated CNNs.
