# LuvHarris: A Practical Corner Detector for Event-cameras

arXiv: https://arxiv.org/abs/2105.11443 · HTML: https://mlsanity.com/paper/2105.11443/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 24 May 2021.

## Builds on (most cited first, 7 of 7)

- [The Event-Camera Dataset and Simulator: Event-based Data for Pose Estimation, Visual Odometry, and SLAM](https://mlsanity.com/paper/1610.08336/) (2016)
- [Events-to-Video: Bringing Modern Computer Vision to Event Cameras](https://mlsanity.com/paper/1904.08298/) (2019)
- [Ultimate SLAM? Combining Events, Images, and IMU for Robust Visual SLAM in HDR and High Speed Scenarios](https://mlsanity.com/paper/1709.06310/) (2017)
- [Speed Invariant Time Surface for Learning to Detect Corner Points with Event-Based Cameras](https://mlsanity.com/paper/1903.11332/) (2019)
- [FA-Harris: A Fast and Asynchronous Corner Detector for Event Cameras](https://mlsanity.com/paper/1906.10925/) (2019)
- [Asynchronous Spatial Image Convolutions for Event Cameras](https://mlsanity.com/paper/1812.00438/) (2018)
- [Independent Motion Detection with Event-driven Cameras](https://mlsanity.com/paper/1706.08713/) (2017)

## Built on by (most cited first, 20 of 20)

- [Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarks](https://mlsanity.com/paper/2302.08890/) (2023)
- [EventPoint: Self-Supervised Interest Point Detection and Description for Event-based Camera](https://mlsanity.com/paper/2109.00210/) (2021)
- [Stereo Event-based, 6-DOF Pose Tracking for Uncooperative Spacecraft](https://mlsanity.com/paper/2503.12732/) (2025)
- [Event-based Simultaneous Localization and Mapping: A Comprehensive Survey](https://mlsanity.com/paper/2304.09793/) (2023)
- [ESVO2: Direct Visual-Inertial Odometry with Stereo Event Cameras](https://mlsanity.com/paper/2410.09374/) (2024)
- [Table tennis ball spin estimation with an event camera](https://mlsanity.com/paper/2404.09870/) (2024)
- [IMU-Aided Event-based Stereo Visual Odometry](https://mlsanity.com/paper/2405.04071/) (2024)
- [Long-Lived Accurate Keypoints in Event Streams](https://mlsanity.com/paper/2209.10385/) (2022)
- [SuperEvent: Cross-Modal Learning of Event-based Keypoint Detection for SLAM](https://mlsanity.com/paper/2504.00139/) (2025)
- [PUCK: Parallel Surface and Convolution-kernel Tracking for Event-Based Cameras](https://mlsanity.com/paper/2205.07657/) (2022)
- [Fast Feature Field (F3): A Predictive Representation of Events](https://mlsanity.com/paper/2509.25146/) (2025)
- [Continuous-Time Gaussian Process Motion-Compensation for Event-vision Pattern Tracking with Distance Fields](https://mlsanity.com/paper/2303.02672/) (2023)
- [Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling](https://mlsanity.com/paper/2505.21187/) (2025)
- [Lattice-allocated Real-time Line Segment Feature Detection and Tracking Using Only an Event-based Camera](https://mlsanity.com/paper/2510.06829/) (2025)
- [ANMS: Asynchronous Non-Maximum Suppression in Event Stream](https://mlsanity.com/paper/2303.10575/) (2023)
- [SuperEIO: Self-Supervised Event Feature Learning for Event Inertial Odometry](https://mlsanity.com/paper/2503.22963/) (2025)
- [RoEL: Robust Event-based 3D Line Reconstruction](https://mlsanity.com/paper/2602.18258/) (2026)
- [A Geometric Framework for Absolute Pose and Velocity Estimation with Event Cameras](https://mlsanity.com/paper/2606.09139/) (2026)
- [Static in Frames, Dynamic in Events: Rethinking Features in Event Cameras as Motion Cues](https://mlsanity.com/paper/2608.11075/) (2026)
- [An Event Preserving Velocity Invariant Representation for Event Cameras](https://mlsanity.com/paper/2609.19973/) (2026)

## Abstract (arXiv)

There have been a number of corner detection methods proposed for event cameras in the last years, since event-driven computer vision has become more accessible. Current state-of-the-art have either unsatisfactory accuracy or real-time performance when considered for practical use, for example when a camera is randomly moved in an unconstrained environment. In this paper, we present yet another method to perform corner detection, dubbed look-up event-Harris (luvHarris), that employs the Harris algorithm for high accuracy but manages an improved event throughput. Our method has two major contributions, 1. a novel "threshold ordinal event-surface" that removes certain tuning parameters and is well suited for Harris operations, and 2. an implementation of the Harris algorithm such that the computational load per event is minimised and computational heavy convolutions are performed only "as-fast-as-possible", i.e. only as computational resources are available. The result is a practical, real-time, and robust corner detector that runs more than 2.6x the speed of current state-of-the-art; a necessity when using high-resolution event-camera in real-time. We explain the considerations taken for the approach, compare the algorithm to current state-of-the-art in terms of computational performance and detection accuracy, and discuss the validity of the proposed approach for event cameras.