# E-Calib: A Fast, Robust and Accurate Calibration Toolbox for Event Cameras

arXiv: https://arxiv.org/abs/2306.09078 · HTML: https://mlsanity.com/paper/2306.09078/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 53 references that are in the map.
- First posted on arXiv 15 Jun 2023.

## Builds on (most cited first, 12 of 12)

- [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)
- [Recurrent Vision Transformers for Object Detection with Event Cameras](https://mlsanity.com/paper/2212.05598/) (2022)
- [How to Calibrate Your Event Camera](https://mlsanity.com/paper/2105.12362/) (2021)
- [Real-Time Intensity-Image Reconstruction for Event Cameras Using Manifold Regularisation](https://mlsanity.com/paper/1607.06283/) (2016)
- [Exploring Event Camera-based Odometry for Planetary Robots](https://mlsanity.com/paper/2204.05880/) (2022)
- [Neuromorphic Camera Denoising using Graph Neural Network-driven Transformers](https://mlsanity.com/paper/2112.09685/) (2021)
- [Autonomous Quadrotor Flight despite Rotor Failure with Onboard Vision Sensors: Frames vs. Events](https://mlsanity.com/paper/2102.13406/) (2021)
- [Dynamic Event Camera Calibration](https://mlsanity.com/paper/2107.06749/) (2021)
- [Neuromorphic Eye-in-Hand Visual Servoing](https://mlsanity.com/paper/2004.07398/) (2020)
- [A Neuromorphic Vision-Based Measurement for Robust Relative Localization in Future Space Exploration Missions](https://mlsanity.com/paper/2206.11541/) (2022)
- [Neuromorphic Vision Based Control for the Precise Positioning of Robotic Drilling Systems](https://mlsanity.com/paper/2201.01626/) (2022)

## Built on by (most cited first, 9 of 9)

- [eWand: A calibration framework for wide baseline frame-based and event-based camera systems](https://mlsanity.com/paper/2309.12685/) (2023)
- [EF-Calib: Spatiotemporal Calibration of Event- and Frame-Based Cameras Using Continuous-Time Trajectories](https://mlsanity.com/paper/2405.17278/) (2024)
- [eKalibr: Dynamic Intrinsic Calibration for Event Cameras From First Principles of Events](https://mlsanity.com/paper/2501.05688/) (2025)
- [High-precision visual navigation device calibration method based on collimator](https://mlsanity.com/paper/2502.18012/) (2025)
- [AsynEIO: Asynchronous Monocular Event-Inertial Odometry Using Gaussian Process Regression](https://mlsanity.com/paper/2411.12175/) (2024)
- [Event-Based Method for High-Speed 3D Deformation Measurement under Extreme Illumination Conditions](https://mlsanity.com/paper/2603.28159/) (2026)
- [Temporal and Rotational Calibration for Event-Centric Multi-Sensor Systems](https://mlsanity.com/paper/2508.12564/) (2025)
- [Filter-Based Reconstruction of Images from Events](https://mlsanity.com/paper/2510.20071/) (2025)
- [Simplified Cross-Modal Calibration for Heterogeneous Event-RGB Stereo Systems](https://mlsanity.com/paper/2608.22965/) (2026)

## Abstract (arXiv)

Event cameras triggered a paradigm shift in the computer vision community delineated by their asynchronous nature, low latency, and high dynamic range. Calibration of event cameras is always essential to account for the sensor intrinsic parameters and for 3D perception. However, conventional image-based calibration techniques are not applicable due to the asynchronous, binary output of the sensor. The current standard for calibrating event cameras relies on either blinking patterns or event-based image reconstruction algorithms. These approaches are difficult to deploy in factory settings and are affected by noise and artifacts degrading the calibration performance. To bridge these limitations, we present E-Calib, a novel, fast, robust, and accurate calibration toolbox for event cameras utilizing the asymmetric circle grid, for its robustness to out-of-focus scenes. The proposed method is tested in a variety of rigorous experiments for different event camera models, on circle grids with different geometric properties, and under challenging illumination conditions. The results show that our approach outperforms the state-of-the-art in detection success rate, reprojection error, and estimation accuracy of extrinsic parameters.