  Deep models trained using synthetic data require domain adaptation to bridge
the gap between the simulation and target environments. State-of-the-art domain
adaptation methods often demand sufficient amounts of (unlabelled) data from
the target domain. However, this need is difficult to fulfil when the target
domain is an extreme environment, such as space. In this paper, our target
problem is close proximity satellite pose estimation, where it is costly to
obtain images of satellites from actual rendezvous missions. We demonstrate
that event sensing offers a promising solution to generalise from the
simulation to the target domain under stark illumination differences. Our main
contribution is an event-based satellite pose estimation technique, trained
purely on synthetic event data with basic data augmentation to improve
robustness against practical (noisy) event sensors. Underpinning our method is
a novel dataset with carefully calibrated ground truth, comprising of real
event data obtained by emulating satellite rendezvous scenarios in the lab
under drastic lighting conditions. Results on the dataset showed that our
event-based satellite pose estimation method, trained only on synthetic data
without adaptation, could generalise to the target domain effectively.
