Cameras were originally designed using physics-based heuristics to capture aesthetic images. In recent years, there has been a transformation in camera design from being purely physics-driven to increasingly data-driven and task-specific. In this paper, we present a framework to understand the building blocks of this nascent field of end-to-end design of camera hardware and algorithms. As part of this framework, we show how methods that exploit both physics and data have become prevalent in imaging and computer vision, underscoring a key trend that will continue to dominate the future of task-specific camera design. Finally, we share current barriers to progress in end-to-end design, and hypothesize how these barriers can be overcome.

翻译：相机最初的设计是使用基于物理的超自然学来捕捉美学图像。 近年来,相机设计发生了转变,从纯粹的物理学驱动转变为日益由数据驱动和任务特定。 在本文中,我们提出了一个框架来理解这个新建的相机硬件和算法端到端设计新领域的各个构件。作为这一框架的一部分,我们展示了物理学和数据利用方法在成像和计算机视觉中如何变得普遍,凸显出将继续主导特定任务相机设计未来的关键趋势。 最后,我们分享了当前在终端到端设计上取得进展的障碍,并假设如何克服这些障碍。