One-Step Event-Driven High-Speed Autofocus
This addresses the challenge of focus hunting in event-driven autofocus for high-speed applications, representing a novel method rather than an incremental improvement.
The paper tackles the problem of high-speed autofocus in extreme scenes by introducing the Event Laplacian Product (ELP) focus detection function, which redefines focus search as a detection task, resulting in up to two-thirds reduction in focusing time and 24 times lower error on the DAVIS346 dataset.
High-speed autofocus in extreme scenes remains a significant challenge. Traditional methods rely on repeated sampling around the focus position, resulting in ``focus hunting''. Event-driven methods have advanced focusing speed and improved performance in low-light conditions; however, current approaches still require at least one lengthy round of ``focus hunting'', involving the collection of a complete focus stack. We introduce the Event Laplacian Product (ELP) focus detection function, which combines event data with grayscale Laplacian information, redefining focus search as a detection task. This innovation enables the first one-step event-driven autofocus, cutting focusing time by up to two-thirds and reducing focusing error by 24 times on the DAVIS346 dataset and 22 times on the EVK4 dataset. Additionally, we present an autofocus pipeline tailored for event-only cameras, achieving accurate results across a range of challenging motion and lighting conditions. All datasets and code will be made publicly available.