Tolerance-Aware Deep Optics
This work addresses the problem of manufacturing and assembly tolerances in deep optics for researchers and engineers, representing an incremental improvement by integrating tolerance analysis into existing co-design pipelines.
The paper tackles the performance gap between designed and fabricated optical systems in deep optics by introducing an end-to-end tolerance-aware optimization framework that incorporates manufacturing and assembly tolerances, demonstrating improved robustness in computational imaging applications through simulations and real-world experiments.
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.