  Estimating the pose of an uncooperative spacecraft is an important computer
vision problem for enabling the deployment of automatic vision-based systems in
orbit, with applications ranging from on-orbit servicing to space debris
removal. Following the general trend in computer vision, more and more works
have been focusing on leveraging Deep Learning (DL) methods to address this
problem. However and despite promising research-stage results, major challenges
preventing the use of such methods in real-life missions still stand in the
way. In particular, the deployment of such computation-intensive algorithms is
still under-investigated, while the performance drop when training on synthetic
and testing on real images remains to mitigate. The primary goal of this survey
is to describe the current DL-based methods for spacecraft pose estimation in a
comprehensive manner. The secondary goal is to help define the limitations
towards the effective deployment of DL-based spacecraft pose estimation
solutions for reliable autonomous vision-based applications. To this end, the
survey first summarises the existing algorithms according to two approaches:
hybrid modular pipelines and direct end-to-end regression methods. A comparison
of algorithms is presented not only in terms of pose accuracy but also with a
focus on network architectures and models' sizes keeping potential deployment
in mind. Then, current monocular spacecraft pose estimation datasets used to
train and test these methods are discussed. The data generation methods:
simulators and testbeds, the domain gap and the performance drop between
synthetically generated and lab/space collected images and the potential
solutions are also discussed. Finally, the paper presents open research
questions and future directions in the field, drawing parallels with other
computer vision applications.
