弥合合成-真实差距：用于鲁棒航天器6-DoF位姿估计的监督域自适应
Bridging the Synthetic-Real Gap: Supervised Domain Adaptation for Robust Spacecraft 6-DoF Pose Estimation
AI总结：
针对航天器位姿估计中合成到真实的域差距问题，提出首个基于LIRR范式的监督域自适应框架，联合优化域不变表示与任务特定风险，在SPEED+基准上仅用5%标记目标数据即超越oracle基线，实现高效鲁棒的位姿估计。
摘要：
航天器位姿估计（SPE）是交会、对接和在轨服务等自主空间操作的基础能力。结合目标检测、关键点回归和Perspective-n-Point（PnP）求解器的混合流水线近期在合成数据集上取得了优异结果，但由于持续存在的合成到真实域差距，其在真实或实验室生成的图像上性能急剧下降。现有的无监督域自适应方法旨在缓解此问题，但在有少量标记目标样本可用时往往表现不佳。在本工作中，我们提出了首个为SPE关键点回归量身定制的监督域自适应（SDA）框架。基于学习不变表示与风险（LIRR）范式，我们的方法利用标记合成数据和有限的标记真实数据，联合优化域不变表示和任务特定风险，从而减少域偏移下的泛化误差。在SPEED+基准上的大量实验表明，我们的方法持续优于仅源域、微调和oracle基线。值得注意的是，仅使用5%的标记目标数据，我们的方法就能匹配或超越在更大量标记数据上训练的oracle性能。该框架轻量级、与骨干网络无关且计算高效，为在真实世界空间环境中实现鲁棒且可部署的航天器位姿估计提供了实用途径。
英文摘要：
Spacecraft Pose Estimation (SPE) is a fundamental capability for autonomous space operations such as rendezvous, docking, and in-orbit servicing. Hybrid pipelines that combine object detection, keypoint regression, and Perspective-n-Point (PnP) solvers have recently achieved strong results on synthetic datasets, yet their performance deteriorates sharply on real or lab-generated imagery due to the persistent synthetic-to-real domain gap. Existing unsupervised domain adaptation approaches aim to mitigate this issue but often underperform when a modest number of labeled target samples are available. In this work, we propose the first Supervised Domain Adaptation (SDA) framework tailored for SPE keypoint regression. Building on the Learning Invariant Representation and Risk (LIRR) paradigm, our method jointly optimizes domain-invariant representations and task-specific risk using both labeled synthetic and limited labeled real data, thereby reducing generalization error under domain shift. Extensive experiments on the SPEED+ benchmark demonstrate that our approach consistently outperforms source-only, fine-tuning, and oracle baselines. Notably, with only 5% labeled target data, our method matches or surpasses oracle performance trained on larger fractions of labeled data. The framework is lightweight, backbone-agnostic, and computationally efficient, offering a practical pathway toward robust and deployable spacecraft pose estimation in real-world space environments.
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