## 1 Introduction  
Non-cooperative space object pose estimation is an urgent problem to be solved in the space field; it has very important application value in relative navigation, rendezvous and docking, active debris removal (ADR) on-orbit servicing (OOS), etc. [1, 2, 3, 4, 5, 6]. For the special environment of on-orbit work, the pose estimation [7, 8] algorithm based on a low-quality, low-power monocular sensor provides a feasible scheme for space application, and it has received extensive attention from scientific research institutions and researchers. Some institutions [9, 10, 11, 12] have carried out relevant studies and semi-physical simulation experiments on the pose estimation of non-cooperative space objects by using monocular vision cameras. Compared with monocular sensors, light detection and ranging (LIDAR) and depth cameras have a smaller scope, larger size, and higher power consumption, and they are more constrained by complex space environments. Therefore, the data obtained using the monocular sensor are more consistent with the pose estimation of the non-cooperative space objects. ESA and Stanford University held the Satellite Pose Estimation Challenge competition in 2019 (SPEC2019), using a monocular sensor to photograph scale models like the Tango satellite to create the Spacecraft Pose Estimation Dataset (SPEED) [13], which was collected through semi-physical simulation experiments and the simulated space environment. The results indicate a new direction for non-cooperative space object pose estimation based on monocular vision. The positional relationship between the sensor and the satellite to be measured is shown in Figure 1.  
Considering the specific mission scenario, the task of pose determination poses theoretical and technical challenges. It necessitates the discovery of optimal algorithmic solutions and sensor architectures. For non-cooperative space object pose estimation, common measurement methods include monocular sensors [14], binocular cameras [15], time-of-flight (TOF) cameras [16], LIDAR [17] sensors, laser range sensors [18], etc. Different sensors can be chosen based on the type of object, spatial environment, and satellite performance. Due to varying data output from different sensors, different processing methods can be employed. Considering factors such as on-board environment and cost, a low-power, lightweight, and cost-effective monocular sensor is more suitable. Similarly, monocular sensors are used in other pose estimation tasks, such as 2D hand pose estimation [19], camera pose estimation [20], head pose estimation [21], etc. Over the past few decades, vision-based non-cooperative space object pose estimation has relied on manually designed features [2; 22; 23] that are described using feature descriptors and detected using feature detectors. These features are then detected in a 2D image, and their corresponding 3D counterparts are used to determine the relative attitude. These features include keypoints, corners, edges, etc. However, feature-based methods have defects in their generalization ability, robustness, and identification efficiency when ranging from the complex spatial environment to tens of thousands of non-cooperative space object species. With the rapid development of computing power and algorithms, the use of deep learning technology to extract features from tremendous data has been gradually applied to the field of non-cooperative space objects detection, and its powerful feature extraction as well as generalization ability can effectively solve the disadvantages of traditional methods. Deep learning is widely used in satellite pose estimation [24; 25], and it can be divided into a direct method and an indirect method. The direct method of estimating satellite pose information directly through the model has the advantages of simple process and fast estimating speed. The indirect method, however, is to deduce satellite pose information  
Figure 1: Diagram of the relationship between the sensor and the satellite to be measured.