On-Orbit 3D Reconstruction and Pose Measurement of Space Non-Cooperative Satellites Using Monocular Vision

Dense 3D reconstruction and accurate pose measurement of non-cooperative satellites are essential for on-orbit servicing missions. Multi-view stereo (MVS) is the preferred approach for high-precision dense reconstruction from monocular imagery, yet current methods struggle with the speed–accuracy trade-off and reconstruction degradation under complex on-orbit conditions, while keypoint-based pose measurement is further constrained by limited training data and heavy reliance on manual annotations. To address these challenges, we present a monocular vision-based method coupling dense 3D reconstruction with pose estimation. We develop MambaMVS, an end-to-end MVS network featuring a Mamba Cost Volume (MCV) Module for efficient cost volume regularization via the linear complexity of structured state space models, and an improved focused linear attention module for robust cross-view feature matching. The reconstructed point cloud provides 3D keypoint coordinates for pose measurement, where a few-shot scheme employing 3D Gaussian Splatting-based view augmentation and reprojection-driven self-labeling enables training with minimal manual annotations. During online inference, detected 2D keypoints are combined with offline-reconstructed 3D coordinates via the Perspective-n-Point algorithm for fast solving of the 6-DOF pose. Experiments on the Customized Space Target dataset demonstrate state-of-the-art reconstruction quality with 1.5× faster inference than current leading methods. Physical satellite model experiments achieve rotation ME within 0.2° and translation ME below 0.01 m, while robustness is validated across varying illumination, reflective surfaces, motion blur, and occlusion. The proposed reconstruction-guided measurement paradigm offers a practical pathway toward autonomous on-orbit perception and in situ structural assessment.