# An Asynchronous Intensity Representation for Framed and Event Video Sources

arXiv: https://arxiv.org/abs/2301.08783 · HTML: https://mlsanity.com/paper/2301.08783/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 53 references that are in the map.
- First posted on arXiv 20 Jan 2023.

## Builds on (most cited first, 21 of 21)

- [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)
- [EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras](https://mlsanity.com/paper/1802.06898/) (2018)
- [End-to-End Learning of Representations for Asynchronous Event-Based Data](https://mlsanity.com/paper/1904.08245/) (2019)
- [The Event-Camera Dataset and Simulator: Event-based Data for Pose Estimation, Visual Odometry, and SLAM](https://mlsanity.com/paper/1610.08336/) (2016)
- [Event-based Vision meets Deep Learning on Steering Prediction for Self-driving Cars](https://mlsanity.com/paper/1804.01310/) (2018)
- [Events-to-Video: Bringing Modern Computer Vision to Event Cameras](https://mlsanity.com/paper/1904.08298/) (2019)
- [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)
- [YouTube UGC Dataset for Video Compression Research](https://mlsanity.com/paper/1904.06457/) (2019)
- [Time-Ordered Recent Event (TORE) Volumes for Event Cameras](https://mlsanity.com/paper/2103.06108/) (2021)
- [Continuous-time Intensity Estimation Using Event Cameras](https://mlsanity.com/paper/1811.00386/) (2018)
- [Event Enhanced High-Quality Image Recovery](https://mlsanity.com/paper/2007.08336/) (2020)
- [DDD20 End-to-End Event Camera Driving Dataset: Fusing Frames and Events with Deep Learning for Improved Steering Prediction](https://mlsanity.com/paper/2005.08605/) (2020)
- [Real-Time Intensity-Image Reconstruction for Event Cameras Using Manifold Regularisation](https://mlsanity.com/paper/1607.06283/) (2016)
- [Event Probability Mask (EPM) and Event Denoising Convolutional Neural Network (EDnCNN) for Neuromorphic Cameras](https://mlsanity.com/paper/2003.08282/) (2020)
- [ViViD++: Vision for Visibility Dataset](https://mlsanity.com/paper/2204.06183/) (2022)
- [An Asynchronous Kalman Filter for Hybrid Event Cameras](https://mlsanity.com/paper/2012.05590/) (2020)
- [E-CIR: Event-Enhanced Continuous Intensity Recovery](https://mlsanity.com/paper/2203.01935/) (2022)
- [Feedback control of event cameras](https://mlsanity.com/paper/2105.00409/) (2021)
- [A Joint Intensity-Neuromorphic Event Imaging System for Resource Constrained Devices](https://mlsanity.com/paper/2105.14164/) (2021)

## Built on by (most cited first, 5 of 5)

- [EventMamba: Enhancing Spatio-Temporal Locality with State Space Models for Event-Based Video Reconstruction](https://mlsanity.com/paper/2503.19721/) (2025)
- [Accelerated Event-Based Feature Detection and Compression for Surveillance Video Systems](https://mlsanity.com/paper/2312.08213/) (2023)
- [An Open Software Suite for Event-Based Video](https://mlsanity.com/paper/2401.17151/) (2024)
- [adder-viz: Real-Time Visualization Software for Transcoding Event Video](https://mlsanity.com/paper/2508.14996/) (2025)
- [Scalable Event-Based Video Streaming for Machines with MoQ](https://mlsanity.com/paper/2508.15003/) (2025)

## Abstract (arXiv)

Neuromorphic "event" cameras, designed to mimic the human vision system with asynchronous sensing, unlock a new realm of high-speed and high dynamic range applications. However, researchers often either revert to a framed representation of event data for applications, or build bespoke applications for a particular camera's event data type. To usher in the next era of video systems, accommodate new event camera designs, and explore the benefits to asynchronous video in classical applications, we argue that there is a need for an asynchronous, source-agnostic video representation. In this paper, we introduce a novel, asynchronous intensity representation for both framed and non-framed data sources. We show that our representation can increase intensity precision and greatly reduce the number of samples per pixel compared to grid-based representations. With framed sources, we demonstrate that by permitting a small amount of loss through the temporal averaging of similar pixel values, we can reduce our representational sample rate by more than half, while incurring a drop in VMAF quality score of only 4.5. We also demonstrate lower latency than the state-of-the-art method for fusing and transcoding framed and event camera data to an intensity representation, while maintaining 2000× the temporal resolution. We argue that our method provides the computational efficiency and temporal granularity necessary to build real-time intensity-based applications for event cameras.