首次实现光学设计中容差感知的端到端优化，提升实际制造稳定性。
Tolerance-Aware Deep Optics
- 融合物理模型与数据驱动，联合优化光学结构与制造容差
- 实测与仿真均验证系统在偏差下的性能保持能力
- 适合关注光学制造鲁棒性的研究人员和工业设计者
深度光学通过协同设计光学元件与深度学习算法，展现出巨大潜力。然而，现有研究普遍忽略制造与装配容差的分析与优化，导致设计与实际器件间存在显著性能差距。为此，本文提出首个端到端的容差感知优化框架，将多种容差类型纳入深度光学设计流程。方法结合物理信息建模与数据驱动训练，通过考虑并补偿制造与装配中的结构偏差，提升光学系统设计性能。我们在计算成像应用中验证该方法，在模拟与真实实验中均取得良好效果。通过定性与定量分析，进一步证明所提方案能有效增强光学系统及视觉算法对容差的鲁棒性。代码与附加可视化结果见 openimaginglab.github.io/LensTolerance。
原文摘要 · Abstract (English)
Deep optics has emerged as a promising approach by co-designing optical elements with deep learning algorithms. However, current research typically overlooks the analysis and optimization of manufacturing and assembly tolerances. This oversight creates a significant performance gap between designed and fabricated optical systems. To address this challenge, we present the first end-to-end tolerance-aware optimization framework that incorporates multiple tolerance types into the deep optics design pipeline. Our method combines physics-informed modelling with data-driven training to enhance optical design by accounting for and compensating for structural deviations in manufacturing and assembly. We validate our approach through computational imaging applications, demonstrating results in both simulations and real-world experiments. We further examine how our proposed solution improves the robustness of optical systems and vision algorithms against tolerances through qualitative and quantitative analyses. Code and additional visual results are available at openimaginglab.github.io/LensTolerance.
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