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.

翻译：估计非合作航天器的姿态是一个重要的计算机视觉问题，旨在推动在轨自主视觉系统的部署，其应用涵盖在轨服务到空间碎片清除等领域。随着计算机视觉领域的发展趋势，越来越多的研究致力于利用深度学习方法来解决这一问题。尽管研究阶段取得了令人鼓舞的成果，但阻碍此类方法应用于实际任务的主要挑战仍然存在。特别是，计算密集型算法的部署问题尚未得到充分研究，而使用合成图像训练与真实图像测试之间的性能下降问题仍需缓解。本综述的首要目的是全面描述当前基于深度学习的航天器姿态估计方法，其次旨在明确有效部署基于深度学习的航天器姿态估计解决方案以支持可靠自主视觉应用所面临的局限。为此，本文首先按照两种方法对现有算法进行总结：混合模块化流水线和直接端到端回归方法。算法对比不仅涉及姿态精度，还从潜在部署角度关注网络架构和模型规模。随后，讨论了当前用于训练和测试这些方法的单目航天器姿态估计数据集。本文还探讨了数据生成方法（模拟器与测试平台）、合成图像与实验室/空间采集图像之间的域差异及性能下降问题，并提出了潜在解决方案。最后，本文通过与其它计算机视觉应用的类比，提出了该领域尚未解决的研究问题与未来发展方向。