Autofocus for Event Cameras

TL;DR

This paper introduces a novel autofocus framework for event cameras, utilizing an event-specific focus measure and search strategy, validated on a new dataset, outperforming existing methods in challenging conditions.

Contribution

The paper presents the first effective autofocus method tailored for event cameras, combining event rate measurement with a robust search algorithm, and provides a new dataset for evaluation.

Findings

Our method achieves higher accuracy than existing approaches.

The autofocus process is more efficient and reliable in challenging scenes.

Experimental results demonstrate significant improvements in real-world scenarios.

Abstract

Focus control (FC) is crucial for cameras to capture sharp images in challenging real-world scenarios. The autofocus (AF) facilitates the FC by automatically adjusting the focus settings. However, due to the lack of effective AF methods for the recently introduced event cameras, their FC still relies on naive AF like manual focus adjustments, leading to poor adaptation in challenging real-world conditions. In particular, the inherent differences between event and frame data in terms of sensing modality, noise, temporal resolutions, etc., bring many challenges in designing an effective AF method for event cameras. To address these challenges, we develop a novel event-based autofocus framework consisting of an event-specific focus measure called event rate (ER) and a robust search strategy called event-based golden search (EGS). To verify the performance of our method, we have collected an event-based autofocus dataset (EAD) containing well-synchronized frames, events, and focal positions in a wide variety of challenging scenes with severe lighting and motion conditions. The experiments on this dataset and additional real-world scenarios demonstrated the superiority of our method over state-of-the-art approaches in terms of efficiency and accuracy.