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

• URL: http://arxiv.org/abs/2601.03718v2
• Date: Thu, 08 Jan 2026 02:11:05 GMT
• Title: Towards Real-world Lens Active Alignment with Unlabeled Data via Domain Adaptation
• Authors: Wenyong Li, Qi Jiang, Weijian Hu, Kailun Yang, Zhanjun Zhang, Wenjun Tian, Kaiwei Wang, Jian Bai,
• Abstract summary: Digital-twin pipeline built on optical simulation offers substantial advantage in generating large-scale labeled data.<n> domain adaptation effectively endows simulation-trained models with robust real-world performance.
• Score: 18.724828089521733
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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