可微透镜：复合透镜在玻璃表面和材料上的搜索，用于目标检测
The Differentiable Lens: Compound Lens Search over Glass Surfaces and Materials for Object Detection
摘要：
大多数摄像机镜头系统是独立设计的，与下游的计算机视觉方法分开。最近，设计镜头以及图像采集和处理管道的其他组件（特别是下游神经网络）的联合优化方法已经实现了成像质量的提高或视觉任务的更好性能。然而，这些现有的方法仅优化透镜参数的子集，并且鉴于其分类性质，不能优化玻璃材料。在这项工作中，我们开发了一个微分球面透镜的模拟模型，准确地捕捉几何像差。我们提出了一种优化策略，以解决镜头设计的挑战-臭名昭著的非凸损失函数景观和许多制造限制-这是加剧了联合优化任务。具体而言，我们引入量化的连续玻璃变量，以促进在端到端设计环境中优化和选择玻璃材料，并将其与精心设计的约束条件相结合，以支持可制造性。在汽车目标检测中，我们报告了比现有设计更好的检测性能，即使在将设计简化为两个或三个元件的镜头时，尽管会显著降低图像质量。
英文摘要：
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 report improved detection performance over existing designs even when simplifying designs to two- or three-element lenses, despite significantly degrading the image quality.
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