  Spacecraft pose estimation is a key task to enable space missions in which
two spacecrafts must navigate around each other. Current state-of-the-art
algorithms for pose estimation employ data-driven techniques. However, there is
an absence of real training data for spacecraft imaged in space conditions due
to the costs and difficulties associated with the space environment. This has
motivated the introduction of 3D data simulators, solving the issue of data
availability but introducing a large gap between the training (source) and test
(target) domains. We explore a method that incorporates 3D structure into the
spacecraft pose estimation pipeline to provide robustness to intensity domain
shift and we present an algorithm for unsupervised domain adaptation with
robust pseudo-labelling. Our solution has ranked second in the two categories
of the 2021 Pose Estimation Challenge organised by the European Space Agency
and the Stanford University, achieving the lowest average error over the two
categories.
