论文标题
在卫星姿势估计中桥接域间隙：一种基于几何约束的自我训练方法
Bridging the Domain Gap in Satellite Pose Estimation: a Self-Training Approach based on Geometrical Constraints
论文作者
论文摘要
最近，卫星姿势估计中无监督的域的适应性引起了人们的关注，旨在减轻训练深层模型的注释成本。为此，我们提出了一个基于域 - 不可吻合的几何约束的自我训练框架。具体而言，我们训练神经网络以预测卫星的2D关键点，然后使用PNP估计姿势。目标样本的姿势被认为是将任务作为最小化问题提出任务的潜在变量。此外，我们利用细粒细分来解决通过将卫星作为稀疏关键的卫星引起的信息损失问题。最后，我们以两个步骤迭代解决最小化问题：伪标签生成和网络培训。实验结果表明，我们的方法很好地适应了目标域。此外，我们的方法赢得了第二次国际卫星姿势估算竞赛的SunLamp任务的第一名。
Recently, unsupervised domain adaptation in satellite pose estimation has gained increasing attention, aiming at alleviating the annotation cost for training deep models. 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 2D keypoints of a satellite and then use 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: pseudo-label generation and network training. Experimental results show that our method adapts well to the target domain. Moreover, our method won the 1st place on the sunlamp task of the second international Satellite Pose Estimation Competition.
