Most camera lens systems are designed in isolation, separately from downstream computer vision methods. Recently, joint optimization approaches that design lenses alongside other components of the image acquisition and processing pipeline -- notably, downstream neural networks -- have achieved improved imaging quality or better performance on vision tasks. However, these existing methods optimize only a subset of lens parameters and cannot optimize glass materials given their categorical nature. In this work, we develop a differentiable spherical lens simulation model that accurately captures geometrical aberrations. We propose an optimization strategy to address the challenges of lens design -- notorious for non-convex loss function landscapes and many manufacturing constraints -- that are exacerbated in joint optimization tasks. Specifically, we introduce quantized continuous glass variables to facilitate the optimization and selection of glass materials in an end-to-end design context, and couple this with carefully designed constraints to support manufacturability. In automotive object detection, we show improved detection performance over existing designs even when simplifying designs to two- or three-element lenses, despite significantly degrading the image quality. Code and optical designs will be made publicly available.

翻译：大多数照相镜头系统是孤立设计的,与下游计算机视觉方法分开。最近,设计镜头与图像获取和处理管道的其他组成部分 -- -- 特别是下游神经网络 -- -- 一道设计镜头的联合优化方法,提高了图像质量或改进了视觉任务的性能。然而,这些现有方法只优化了一组镜头参数,不能优化玻璃材料的绝对性质。在这项工作中,我们开发了一种可区分的球透镜模拟模型,精确地捕捉几何偏差。我们提出了一个优化战略,以应对镜头设计的挑战 -- -- 以非电离子损耗功能景观和许多制造限制而闻名 -- -- 在联合优化任务中更加严重。具体地说,我们引入了连续的玻璃变数,以促进玻璃材料在端到端设计环境中的优化和选择,并结合了精心设计的制约,以支持制造可操作性。在汽车物体探测中,我们发现现有设计,即使将设计简化为2或3分元素的镜眼镜时,也提高了探测性,尽管大大降低了图像质量。代码和光学设计将被公开。