标题： 通过领域自适应实现无标签数据的真实镜头主动对准 显示英文标题

标题： Towards Real-world Lens Active Alignment with Unlabeled Data via Domain Adaptation

摘要： 主动对准（AA）是高精度光学系统大规模自动化装配的关键技术。 与耗时的逐模型设备校准相比，基于光学模拟构建的数字孪生流程在生成大规模标记数据方面具有显著优势。 然而，复杂的成像条件会在仿真和真实图像之间产生领域差距，限制了仿真训练模型的泛化能力。 为了解决这个问题，我们提出通过添加少量在随机错位位置捕获的未标记真实世界图像来增强仿真基线，从领域自适应的角度减轻差距。 我们引入了领域自适应主动对准（DA3），它利用自回归领域转换生成器和基于对抗的特征对齐策略，通过自监督学习提取真实领域信息。 这使得能够提取领域不变的图像退化特征，以促进稳健的错位预测。 在两种透镜类型的实验表明，DA3比纯粹的仿真流程提高了46%的准确性。 显著的是，它接近于在3个透镜样本上收集的精确标记的真实世界数据所实现的性能，同时将设备端数据收集时间减少了98.7%。 结果表明，领域自适应有效地赋予仿真训练模型强大的真实世界性能，验证了数字孪生流程作为显著提高大规模光学装配效率的实际解决方案。 显示英文摘要

摘要： Active Alignment (AA) is a key technology for the large-scale automated assembly of high-precision optical systems. Compared with labor-intensive per-model on-device calibration, a digital-twin pipeline built on optical simulation offers a substantial advantage in generating large-scale labeled data. However, complex imaging conditions induce a domain gap between simulation and real-world images, limiting the generalization of simulation-trained models. To address this, we propose augmenting a simulation baseline with minimal unlabeled real-world images captured at random misalignment positions, mitigating the gap from a domain adaptation perspective. We introduce Domain Adaptive Active Alignment (DA3), which utilizes an autoregressive domain transformation generator and an adversarial-based feature alignment strategy to distill real-world domain information via self-supervised learning. This enables the extraction of domain-invariant image degradation features to facilitate robust misalignment prediction. Experiments on two lens types reveal that DA3 improves accuracy by 46% over a purely simulation pipeline. Notably, it approaches the performance achieved with precisely labeled real-world data collected on 3 lens samples, while reducing on-device data collection time by 98.7%. The results demonstrate that domain adaptation effectively endows simulation-trained models with robust real-world performance, validating the digital-twin pipeline as a practical solution to significantly enhance the efficiency of large-scale optical assembly.