  In recent years, there has been a growing demand for improved autonomy for
in-orbit operations such as rendezvous, docking, and proximity maneuvers,
leading to increased interest in employing Deep Learning-based Spacecraft Pose
Estimation techniques. However, due to limited access to real target datasets,
algorithms are often trained using synthetic data and applied in the real
domain, resulting in a performance drop due to the domain gap. State-of-the-art
approaches employ Domain Adaptation techniques to mitigate this issue. In the
search for viable solutions, event sensing has been explored in the past and
shown to reduce the domain gap between simulations and real-world scenarios.
Event sensors have made significant advancements in hardware and software in
recent years. Moreover, the characteristics of the event sensor offer several
advantages in space applications compared to RGB sensors. To facilitate further
training and evaluation of DL-based models, we introduce a novel dataset,
SPADES, comprising real event data acquired in a controlled laboratory
environment and simulated event data using the same camera intrinsics.
Furthermore, we propose an effective data filtering method to improve the
quality of training data, thus enhancing model performance. Additionally, we
introduce an image-based event representation that outperforms existing
representations. A multifaceted baseline evaluation was conducted using
different event representations, event filtering strategies, and algorithmic
frameworks, and the results are summarized. The dataset will be made available
at http://cvi2.uni.lu/spades.
