# An Asynchronous Kalman Filter for Hybrid Event Cameras

arXiv: https://arxiv.org/abs/2012.05590 · HTML: https://mlsanity.com/paper/2012.05590/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2020.

## Built on by (most cited first, 26 of 26)

- [Time Lens++: Event-based Frame Interpolation with Parametric Non-linear Flow and Multi-scale Fusion](https://mlsanity.com/paper/2203.17191/) (2022)
- [EventNeRF: Neural Radiance Fields from a Single Colour Event Camera](https://mlsanity.com/paper/2206.11896/) (2022)
- [Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarks](https://mlsanity.com/paper/2302.08890/) (2023)
- [Recent Event Camera Innovations: A Survey](https://mlsanity.com/paper/2408.13627/) (2024)
- [Multi-Bracket High Dynamic Range Imaging with Event Cameras](https://mlsanity.com/paper/2203.06622/) (2022)
- [EventAid: Benchmarking Event-aided Image/Video Enhancement Algorithms with Real-captured Hybrid Dataset](https://mlsanity.com/paper/2312.08220/) (2023)
- [Stereo Hybrid Event-Frame (SHEF) Cameras for 3D Perception](https://mlsanity.com/paper/2110.04988/) (2021)
- [Event-based Simultaneous Localization and Mapping: A Comprehensive Survey](https://mlsanity.com/paper/2304.09793/) (2023)
- [Asynchronous Blob Tracker for Event Cameras](https://mlsanity.com/paper/2307.10593/) (2023)
- [A Linear Comb Filter for Event Flicker Removal](https://mlsanity.com/paper/2205.08090/) (2022)
- [Event-guided Multi-patch Network with Self-supervision for Non-uniform Motion Deblurring](https://mlsanity.com/paper/2302.07689/) (2023)
- [Noise2Image: Noise-Enabled Static Scene Recovery for Event Cameras](https://mlsanity.com/paper/2404.01298/) (2024)
- [Ultra-High-Frequency Harmony: mmWave Radar and Event Camera Orchestrate Accurate Drone Landing](https://mlsanity.com/paper/2502.14992/) (2025)
- [An Asynchronous Linear Filter Architecture for Hybrid Event-Frame Cameras](https://mlsanity.com/paper/2309.01159/) (2023)
- [An Asynchronous Intensity Representation for Framed and Event Video Sources](https://mlsanity.com/paper/2301.08783/) (2023)
- [E-VFIA : Event-Based Video Frame Interpolation with Attention](https://mlsanity.com/paper/2209.09359/) (2022)
- [BlinkTrack: Feature Tracking over 80 FPS via Events and Images](https://mlsanity.com/paper/2409.17981/) (2024)
- [Seeing Behind Dynamic Occlusions with Event Cameras](https://mlsanity.com/paper/2307.15829/) (2023)
- [Neuromorphic Synergy for Video Binarization](https://mlsanity.com/paper/2402.12644/) (2024)
- [Event Cameras Meet SPADs for High-Speed, Low-Bandwidth Imaging](https://mlsanity.com/paper/2404.11511/) (2024)
- [Recovering Continuous Scene Dynamics from A Single Blurry Image with Events](https://mlsanity.com/paper/2304.02695/) (2023)
- [MAEVI: Motion Aware Event-Based Video Frame Interpolation](https://mlsanity.com/paper/2303.02025/) (2023)
- [Seeing through Light and Darkness: Sensor-Physics Grounded Deblurring HDR NeRF from Single-Exposure Images and Events](https://mlsanity.com/paper/2601.15475/) (2026)
- [High-Speed Full-Color HDR Imaging via Unwrapping Modulo-Encoded Spike Streams](https://mlsanity.com/paper/2604.14632/) (2026)
- [High Dynamic Range Video Reconstruction from Single-Exposure Raw Sequences](https://mlsanity.com/paper/2609.27274/) (2026)
- [Asynchronous Tracking, Optical Communication and 3D Motion Capture using Event-based Sensors](https://mlsanity.com/paper/2610.04342/) (2026)

## Abstract (arXiv)

Event cameras are ideally suited to capture HDR visual information without blur but perform poorly on static or slowly changing scenes. Conversely, conventional image sensors measure absolute intensity of slowly changing scenes effectively but do poorly on high dynamic range or quickly changing scenes. In this paper, we present an event-based video reconstruction pipeline for High Dynamic Range (HDR) scenarios. The proposed algorithm includes a frame augmentation pre-processing step that deblurs and temporally interpolates frame data using events. The augmented frame and event data are then fused using a novel asynchronous Kalman filter under a unifying uncertainty model for both sensors. Our experimental results are evaluated on both publicly available datasets with challenging lighting conditions and fast motions and our new dataset with HDR reference. The proposed algorithm outperforms state-of-the-art methods in both absolute intensity error (48% reduction) and image similarity indexes (average 11% improvement).