# Event Camera Calibration of Per-pixel Biased Contrast Threshold

arXiv: https://arxiv.org/abs/2012.09378 · HTML: https://mlsanity.com/paper/2012.09378/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2020.

## Built on by (most cited first, 23 of 23)

- [Event-Based Fusion for Motion Deblurring with Cross-modal Attention](https://mlsanity.com/paper/2112.00167/) (2021)
- [Stereo Hybrid Event-Frame (SHEF) Cameras for 3D Perception](https://mlsanity.com/paper/2110.04988/) (2021)
- [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)
- [Frequency Cam: Imaging Periodic Signals in Real-Time](https://mlsanity.com/paper/2211.00198/) (2022)
- [Control and Evaluation of Event Cameras Output Sharpness via Bias](https://mlsanity.com/paper/2210.13929/) (2022)
- [Temporal-Mapping Photography for Event Cameras](https://mlsanity.com/paper/2403.06443/) (2024)
- [Event-based Asynchronous HDR Imaging by Temporal Incident Light Modulation](https://mlsanity.com/paper/2403.09392/) (2024)
- [EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision](https://mlsanity.com/paper/2412.07080/) (2024)
- [Noise2Image: Noise-Enabled Static Scene Recovery for Event Cameras](https://mlsanity.com/paper/2404.01298/) (2024)
- [An Asynchronous Linear Filter Architecture for Hybrid Event-Frame Cameras](https://mlsanity.com/paper/2309.01159/) (2023)
- [Improving Fast Auto-Focus with Event Polarity](https://mlsanity.com/paper/2303.08611/) (2023)
- [A PyTorch-Enabled Tool for Synthetic Event Camera Data Generation and Algorithm Development](https://mlsanity.com/paper/2503.09754/) (2025)
- [Neuromorphic Synergy for Video Binarization](https://mlsanity.com/paper/2402.12644/) (2024)
- [Classification of Primitive Manufacturing Tasks from Filtered Event Data](https://mlsanity.com/paper/2303.09558/) (2023)
- [Optical Linear Systems Framework for Event Sensing and Computational Neuromorphic Imaging](https://mlsanity.com/paper/2601.13498/) (2026)
- [Filter-Based Reconstruction of Images from Events](https://mlsanity.com/paper/2510.20071/) (2025)
- [Locally Adaptive Decay Surfaces for High-Speed Face and Landmark Detection with Event Cameras](https://mlsanity.com/paper/2602.23101/) (2026)
- [Noise2Params: Unification and Parameter Determination from Noise via a Probabilistic Event Camera Model](https://mlsanity.com/paper/2605.16317/) (2026)
- [See Silhouettes in Motion with Neuromorphic Vision](https://mlsanity.com/paper/2605.17984/) (2026)
- [Modelling dynamic systems transfer functions from events in computational neuromorphic imaging](https://mlsanity.com/paper/2609.29863/) (2026)
- [Vmem-varphi: Low-Compute Out-of-Distribution Detection in Spiking Neural Networks from Membrane-Potential Statistics](https://mlsanity.com/paper/2610.00350/) (2026)
- [Asynchronous Tracking, Optical Communication and 3D Motion Capture using Event-based Sensors](https://mlsanity.com/paper/2610.04342/) (2026)

## Abstract (arXiv)

Event cameras output asynchronous events to represent intensity changes with a high temporal resolution, even under extreme lighting conditions. Currently, most of the existing works use a single contrast threshold to estimate the intensity change of all pixels. However, complex circuit bias and manufacturing imperfections cause biased pixels and mismatch contrast threshold among pixels, which may lead to undesirable outputs. In this paper, we propose a new event camera model and two calibration approaches which cover event-only cameras and hybrid image-event cameras. When intensity images are simultaneously provided along with events, we also propose an efficient online method to calibrate event cameras that adapts to time-varying event rates. We demonstrate the advantages of our proposed methods compared to the state-of-the-art on several different event camera datasets.