用强化学习自动对准镜头，省去人工设计与昂贵测量。
Active Alignments of Lens Systems with Reinforcement Learning
- 直接在传感器像素空间训练，无需专家对齐经验。
- 比现有方法快3倍以上，对制造误差更鲁棒。
- 开源仿真平台支持真实光学系统模拟，适合工业落地。
镜头系统与成像器的相对对准是相机制造中的关键挑战。尽管理想条件下可数学计算最优对准，但实际制造公差常导致该方法不可行。测量这些公差成本高甚至无法实现，忽略则会导致对准不佳。本文提出一种仅在传感器输出像素空间中训练的强化学习（RL）方法，无需设计专家对齐规则。通过大规模基准测试，证明该方法在速度、精度和鲁棒性上均优于其他方法。我们进一步推出 relign——一个基于物理渲染的真实感开源仿真平台，可建模非确定性制造公差及机器人对准噪声，并兼容主流机器学习框架，便于实验与开发。本工作展示了强化学习在制造环境中提升光学对准效率的潜力，同时减少人工干预。
原文摘要 · Abstract (English)
Aligning a lens system relative to an imager is a critical challenge in camera manufacturing. While optimal alignment can be mathematically computed under ideal conditions, real-world deviations caused by manufacturing tolerances often render this approach impractical. Measuring these tolerances can be costly or even infeasible, and neglecting them may result in suboptimal alignments. We propose a reinforcement learning (RL) approach that learns exclusively in the pixel space of the sensor output, eliminating the need to develop expert-designed alignment concepts. We conduct an extensive benchmark study and show that our approach surpasses other methods in speed, precision, and robustness. We further introduce relign, a realistic, freely explorable, open-source simulation utilizing physically based rendering that models optical systems with non-deterministic manufacturing tolerances and noise in robotic alignment movement. It provides an interface to popular machine learning frameworks, enabling seamless experimentation and development. Our work highlights the potential of RL in a manufacturing environment to enhance efficiency of optical alignments while minimizing the need for manual intervention.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。