Bridging the Domain Gap in Satellite Pose Estimation: A Self-Training Approach Based on Geometrical Constraints

Unsupervised domain adaptation in satellite posed estimation aimed at alleviating the annotation cost for training deep models has been gaining attention. To this end, we propose a self-training framework based on the domain-agnostic geometrical constraints. Specifically, we train a neural network to predict the 2-D keypoints of a satellite and then use perspective-n-point (PnP) to estimate the pose. The poses of target samples are regarded as latent variables to formulate the task as a minimization problem. Furthermore, we leverage fine-grained segmentation to tackle the information loss issue caused by abstracting the satellite as sparse keypoints. Finally, we iteratively solve the minimization problem in two steps: pseudolabel generation and network training. Experimental results show that our method adapts well to the target domain. Moreover, our method won the first place on the sunlamp task of the second international Satellite Pose Estimation Competition.