One-Step Event-Driven High-Speed Autofocus
Yuhan Bao, Shaohua Gao, Wenyong Li, Kaiwei Wang
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.
BibTeX
@InProceedings{Bao_2025_CVPR,
author = {Bao, Yuhan and Gao, Shaohua and Li, Wenyong and Wang, Kaiwei},
title = {One-Step Event-Driven High-Speed Autofocus},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {6222-6230}
}