Test-Time Adaptation for Keypoint-Based Spacecraft Pose Estimation Based on Predicted-View Synthesis

Due to the difficulty of replicating the real conditions during training, supervised algorithms for spacecraft pose estimation experience a drop in performance when trained on synthetic data and applied to real operational data. To address this issue, we propose a test-time adaptation approach that leverages the temporal redundancy between images acquired during close proximity operations. Our approach involves extracting features from sequential spacecraft images, estimating their poses, and then using this information to synthesize a reconstructed view. We establish a self-supervised learning objective by comparing the synthesized view with the actual one. During training, we supervise both pose estimation and image synthesis, while at test time, we optimize the self-supervised objective. In addition, we introduce a regularization loss to prevent solutions that are not consistent with the keypoint structure of the spacecraft.