The 3D perception of satellites, including both their shape and pose, is a key foundation for robotic on-orbit servicing. However, the demanding space environment-such as intense and dim illumination-presents significant challenges. Previous non-cooperative methods focus on specific geometric features like solar panel brackets or docking rings, overlooking the satellite's overall shape and increasing the risk of collisions during grasping. Additionally, satellites are often weakly textured, limiting the accuracy of 3D perception. To address these issues, we propose, for the first time, a 3D perceptionbased visual servo system of non-cooperative satellites. This system combines reconstruction and tracking to enhance shape perception and pose estimation accuracy in orbital conditions. Specifically, we employ an alternating iterative strategy to simultaneously reconstruct and track the satellite and introduce a novel constraint to fuse different cues under extreme conditions. Further, we develop a simulation environment platform, a dualarm microgravity grasping system, and an online monitoring module to enhance system capabilities for on-orbit servicing. Synthetic and real-world datasets from the simulation environment are also created for experimental validation. Results show that each module of our system achieves state-of-the-art performance.

Category-level pose tracking methods can continuously track the pose of objects without requiring any prior knowledge of the specific shape of the tracked instance. This makes them advantageous in augmented reality and virtual reality applications. The key challenge is how to train neural networks to accurately predict the poses of objects they have never seen before and exhibit strong generalization performance. We propose a novel category-level 6D pose tracking method Corr-Track, which is capable of accurately tracking objects belonging to the same category from depth video streams. Our approach utilizes direct soft correspondence constraints to train a neural network, which estimates bidirectional soft correspondences between sparsely sampled point clouds of objects in two frames. We first introduce a soft correspondence matrix for pose tracking tasks and establish effective constraints through direct spatial point-to-point correspondence representations in the sparse point cloud correspondence matrix. We propose the "point cloud expansion" strategy to address the "point cloud shrinkage" problem resulting from soft correspondences. This strategy ensures that the corresponding point cloud accurately reproduces the shape of the target point cloud, leading to precise pose tracking results. We evaluated our approach on the NOCS-REAL275 and Wild6D dataset and observed superior performance compared to previous methods. Additionally, we conducted cross-category experiments that further demonstrated its generalization capability.