  Computer vision tasks are crucial for aerospace missions as they help
spacecraft to understand and interpret the space environment, such as
estimating position and orientation, reconstructing 3D models, and recognizing
objects, which have been extensively studied to successfully carry out the
missions. However, traditional methods like Kalman Filtering, Structure from
Motion, and Multi-View Stereo are not robust enough to handle harsh conditions,
leading to unreliable results. In recent years, deep learning (DL)-based
perception technologies have shown great potential and outperformed traditional
methods, especially in terms of their robustness to changing environments. To
further advance DL-based aerospace perception, various frameworks, datasets,
and strategies have been proposed, indicating significant potential for future
applications. In this survey, we aim to explore the promising techniques used
in perception tasks and emphasize the importance of DL-based aerospace
perception. We begin by providing an overview of aerospace perception,
including classical space programs developed in recent years, commonly used
sensors, and traditional perception methods. Subsequently, we delve into three
fundamental perception tasks in aerospace missions: pose estimation, 3D
reconstruction, and recognition, as they are basic and crucial for subsequent
decision-making and control. Finally, we discuss the limitations and
possibilities in current research and provide an outlook on future
developments, including the challenges of working with limited datasets, the
need for improved algorithms, and the potential benefits of multi-source
information fusion.
