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Computational imaging systems algorithmically post-process acquisition images either to reveal physical quantities of interest or to increase image quality, e.g., deblurring. Designing a computational imaging system requires co-design of optics and algorithms, and recently Deep Lens systems have been proposed in which both components are end-to-end designed using data-driven end-to-end training. However, progress on this exciting concept has so far been hampered by the lack of differentiable forward simulations for complex optical design spaces. Here, we introduce <bold>dO</bold> (DiffOptics) to provide derivative insights into the design pipeline to chain variable parameters and their gradients to an error metric through differential ray tracing. However, straightforward back-propagation of many millions of rays requires unaffordable device memory, and is not resolved by prior works. <bold>dO</bold> alleviates this issue using two customized memory-efficient techniques: differentiable ray-surface intersection and adjoint back-propagation. Broad application examples demonstrate the versatility and flexibility of <bold>dO</bold>, including classical lens designs in asphere, double-Gauss, and freeform, reverse engineering for metrology, and joint designs of optics-network in computational imaging applications. We believe <bold>dO</bold> enables a radically new approach to computational imaging system designs and relevant research domains.

dO: A Differentiable Engine for Deep Lens Design of Computational Imaging Systems