# One-Step Event-Driven High-Speed Autofocus

arXiv: https://arxiv.org/abs/2503.01214 · HTML: https://mlsanity.com/paper/2503.01214/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 21 references that are in the map.
- First posted on arXiv 3 Mar 2025.

## Builds on (most cited first, 5 of 5)

- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Deep Depth from Focus with Differential Focus Volume](https://mlsanity.com/paper/2112.01712/) (2021)
- [Temporal-Mapping Photography for Event Cameras](https://mlsanity.com/paper/2403.06443/) (2024)
- [Autofocus for Event Cameras](https://mlsanity.com/paper/2203.12321/) (2022)
- [Improving Fast Auto-Focus with Event Polarity](https://mlsanity.com/paper/2303.08611/) (2023)

## Built on by (most cited first, 1 of 1)

- [Event Camera Guided Visual Media Restoration & 3D Reconstruction: A Survey](https://mlsanity.com/paper/2509.09971/) (2025)

## Abstract (arXiv)

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.