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arXiv · 2503.01214

One-Step Event-Driven High-Speed Autofocus

Yuhan Bao·Shaohua Gao·Wenyong Li·Kaiwei Wang
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Abstract

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.

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Yuhan Bao, Shaohua Gao, Wenyong Li, Kaiwei Wang. 2025-03-03. One-Step Event-Driven High-Speed Autofocus. https://arxiv.org/abs/2503.01214

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