# Asynchronous Spatial Image Convolutions for Event Cameras

arXiv: https://arxiv.org/abs/1812.00438 · HTML: https://mlsanity.com/paper/1812.00438/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2018.

## Built on by (most cited first, 18 of 18)

- [DSEC: A Stereo Event Camera Dataset for Driving Scenarios](https://mlsanity.com/paper/2103.06011/) (2021)
- [Event-based Video Reconstruction via Potential-assisted Spiking Neural Network](https://mlsanity.com/paper/2201.10943/) (2022)
- [LuvHarris: A Practical Corner Detector for Event-cameras](https://mlsanity.com/paper/2105.11443/) (2021)
- [Event-based Simultaneous Localization and Mapping: A Comprehensive Survey](https://mlsanity.com/paper/2304.09793/) (2023)
- [Autofocus for Event Cameras](https://mlsanity.com/paper/2203.12321/) (2022)
- [E2VIDiff: Perceptual Events-to-Video Reconstruction using Diffusion Priors](https://mlsanity.com/paper/2407.08231/) (2024)
- [Towards End-to-End Neuromorphic Event-based 3D Object Reconstruction Without Physical Priors](https://mlsanity.com/paper/2501.00741/) (2025)
- [An Asynchronous Linear Filter Architecture for Hybrid Event-Frame Cameras](https://mlsanity.com/paper/2309.01159/) (2023)
- [Automotive Object Detection via Learning Sparse Events by Spiking Neurons](https://mlsanity.com/paper/2307.12900/) (2023)
- [Ev-Edge: Efficient Execution of Event-based Vision Algorithms on Commodity Edge Platforms](https://mlsanity.com/paper/2403.15717/) (2024)
- [Hardware, Algorithms, and Applications of the Neuromorphic Vision Sensor: a Review](https://mlsanity.com/paper/2504.08588/) (2025)
- [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)
- [Recovering Continuous Scene Dynamics from A Single Blurry Image with Events](https://mlsanity.com/paper/2304.02695/) (2023)
- [FEDORA: Flying Event Dataset fOr Reactive behAvior](https://mlsanity.com/paper/2305.14392/) (2023)
- [High-speed and High-quality Vision Reconstruction of Spike Camera with Spike Stability Theorem](https://mlsanity.com/paper/2412.11639/) (2024)
- [ANMS: Asynchronous Non-Maximum Suppression in Event Stream](https://mlsanity.com/paper/2303.10575/) (2023)
- [Filter-Based Reconstruction of Images from Events](https://mlsanity.com/paper/2510.20071/) (2025)

## Abstract (arXiv)

Spatial convolution is arguably the most fundamental of 2D image processing operations. Conventional spatial image convolution can only be applied to a conventional image, that is, an array of pixel values (or similar image representation) that are associated with a single instant in time. Event cameras have serial, asynchronous output with no natural notion of an image frame, and each event arrives with a different timestamp. In this paper, we propose a method to compute the convolution of a linear spatial kernel with the output of an event camera. The approach operates on the event stream output of the camera directly without synthesising pseudo-image frames as is common in the literature. The key idea is the introduction of an internal state that directly encodes the convolved image information, which is updated asynchronously as each event arrives from the camera. The state can be read-off as-often-as and whenever required for use in higher level vision algorithms for real-time robotic systems. We demonstrate the application of our method to corner detection, providing an implementation of a Harris corner-response "state" that can be used in real-time for feature detection and tracking on robotic systems.