用事件数据一步完成高速对焦，大幅减少对焦时间与误差。
One-Step Event-Driven High-Speed Autofocus
- 结合事件数据与拉普拉斯灰度信息，将对焦转为检测任务。
- 在DAVIS346上对焦误差降低24倍，时间缩短三分之二。
- 专为事件相机设计，适用于复杂光照与运动场景。
极端场景下的高速自动对焦仍是重大挑战。传统方法依赖对焦点附近的反复采样，导致“对焦搜索”；事件驱动方法虽提升了速度并改善了低光性能，但现有方法仍需至少一轮完整的对焦堆栈采集。本文提出事件拉普拉斯乘积（ELP）对焦检测函数，融合事件数据与灰度拉普拉斯信息，将对焦搜索重构为检测任务。该创新实现了首个一步式事件驱动对焦，在DAVIS346数据集上对焦时间减少三分之二，对焦误差降低24倍；在EVK4数据集上误差降低22倍。同时，提出适配纯事件相机的对焦流水线，在多种复杂运动与光照条件下均实现高精度对焦。所有数据集与代码将公开。
原文摘要 · Abstract (English)
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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