用深度学习仅凭光学测量诊断多镜头系统偏移，精度达0.031mm。
Deep Learning for Optical Misalignment Diagnostics in Multi-Lens Imaging Systems
- 用光线追迹点图预测5自由度误差，实现高精度逆向设计。
- 在6镜头系统中横向平移误差均值仅0.031mm，倾斜误差0.011°。
- 结合物理仿真生成灰度图像，可推广至双镜头与六镜头系统。
在快速发展的光学工程领域，多镜头成像系统的精确对准至关重要却极具挑战性，微小的偏移即可能导致性能显著下降。传统对准方法依赖专用设备，过程耗时，亟需自动化、可扩展的解决方案。本文提出两种互补的基于深度学习的逆向设计方法，仅通过光学测量即可诊断多元件镜头系统的偏移。首先，利用光线追迹点图预测一个六镜头摄影定焦镜头的五自由度（5-DOF）误差，横向平移平均绝对误差为0.031mm，倾斜误差为0.011°。其次，引入基于物理的仿真流程，使用灰度合成相机图像，使深度学习模型能够估计双镜头和六镜头多镜头系统中的四自由度（4-DOF）离心与倾斜误差。结果表明，该方法有望重塑精密成像领域的制造与质量控制流程。
原文摘要 · Abstract (English)
In the rapidly evolving field of optical engineering, precise alignment of multi-lens imaging systems is critical yet challenging, as even minor misalignments can significantly degrade performance. Traditional alignment methods rely on specialized equipment and are time-consuming processes, highlighting the need for automated and scalable solutions. We present two complementary deep learning-based inverse-design methods for diagnosing misalignments in multi-element lens systems using only optical measurements. First, we use ray-traced spot diagrams to predict five-degree-of-freedom (5-DOF) errors in a 6-lens photographic prime, achieving a mean absolute error of 0.031mm in lateral translation and 0.011$^\circ$ in tilt. We also introduce a physics-based simulation pipeline that utilizes grayscale synthetic camera images, enabling a deep learning model to estimate 4-DOF, decenter and tilt errors in both two- and six-lens multi-lens systems. These results show the potential to reshape manufacturing and quality control in precision imaging.
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