Journal
2022 IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL PHOTOGRAPHY (ICCP)
Volume -, Issue -, Pages -Publisher
IEEE
DOI: 10.1109/ICCP54855.2022.9887681
Keywords
Camera Design; Computational Imaging; Perception; Computer Vision; Machine Learning
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This paper presents a framework to understand the building blocks of the emerging field of end-to-end design of camera hardware and algorithms, highlighting the transformation from physics-driven to data-driven and task-specific camera design. It emphasizes the prevalence of methods that combine both physics and data in imaging and computer vision.
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.
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