# Autofocus for Event Cameras

arXiv: https://arxiv.org/abs/2203.12321 · HTML: https://mlsanity.com/paper/2203.12321/

## Key facts

- Cited by 7 papers in the map (data through 13 Oct 2026).
- Builds on 12 of its 45 references that are in the map.
- First posted on arXiv 23 Mar 2022.

## Builds on (most cited first, 12 of 12)

- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [High Speed and High Dynamic Range Video with an Event Camera](https://mlsanity.com/paper/1906.07165/) (2019)
- [The Event-Camera Dataset and Simulator: Event-based Data for Pose Estimation, Visual Odometry, and SLAM](https://mlsanity.com/paper/1610.08336/) (2016)
- [Learning to Detect Objects with a 1 Megapixel Event Camera](https://mlsanity.com/paper/2009.13436/) (2020)
- [Bringing a Blurry Frame Alive at High Frame-Rate with an Event Camera](https://mlsanity.com/paper/1811.10180/) (2018)
- [Asynchronous, Photometric Feature Tracking using Events and Frames](https://mlsanity.com/paper/1807.09713/) (2018)
- [Focus Is All You Need: Loss Functions For Event-based Vision](https://mlsanity.com/paper/1904.07235/) (2019)
- [Real-Time Intensity-Image Reconstruction for Event Cameras Using Manifold Regularisation](https://mlsanity.com/paper/1607.06283/) (2016)
- [Learning to Autofocus](https://mlsanity.com/paper/2004.12260/) (2020)
- [Inceptive Event Time-Surfaces for Object Classification Using Neuromorphic Cameras](https://mlsanity.com/paper/2002.11656/) (2020)
- [Asynchronous Spatial Image Convolutions for Event Cameras](https://mlsanity.com/paper/1812.00438/) (2018)
- [Intelligent Autofocus](https://mlsanity.com/paper/2002.12389/) (2020)

## Built on by (most cited first, 7 of 7)

- [E-MLB: Multilevel Benchmark for Event-Based Camera Denoising](https://mlsanity.com/paper/2303.11997/) (2023)
- [Improving Fast Auto-Focus with Event Polarity](https://mlsanity.com/paper/2303.08611/) (2023)
- [Neuromorphic Synergy for Video Binarization](https://mlsanity.com/paper/2402.12644/) (2024)
- [Autobiasing Event Cameras](https://mlsanity.com/paper/2411.00729/) (2024)
- [Event Camera Guided Visual Media Restoration & 3D Reconstruction: A Survey](https://mlsanity.com/paper/2509.09971/) (2025)
- [One-Step Event-Driven High-Speed Autofocus](https://mlsanity.com/paper/2503.01214/) (2025)
- [EROAM: Event-based Camera Rotational Odometry and Mapping in Real-time](https://mlsanity.com/paper/2411.11004/) (2024)

## Abstract (arXiv)

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.