# EF-Calib: Spatiotemporal Calibration of Event- and Frame-Based Cameras Using Continuous-Time Trajectories

arXiv: https://arxiv.org/abs/2405.17278 · HTML: https://mlsanity.com/paper/2405.17278/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 31 references that are in the map.
- First posted on arXiv 27 May 2024.

## Builds on (most cited first, 18 of 18)

- [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)
- [Ultimate SLAM? Combining Events, Images, and IMU for Robust Visual SLAM in HDR and High Speed Scenarios](https://mlsanity.com/paper/1709.06310/) (2017)
- [Continuous-Time Visual-Inertial Odometry for Event Cameras](https://mlsanity.com/paper/1702.07389/) (2017)
- [How to Calibrate Your Event Camera](https://mlsanity.com/paper/2105.12362/) (2021)
- [VECtor: A Versatile Event-Centric Benchmark for Multi-Sensor SLAM](https://mlsanity.com/paper/2207.01404/) (2022)
- [RGB-Event Fusion for Moving Object Detection in Autonomous Driving](https://mlsanity.com/paper/2209.08323/) (2022)
- [Efficient Derivative Computation for Cumulative B-Splines on Lie Groups](https://mlsanity.com/paper/1911.08860/) (2019)
- [PL-EVIO: Robust Monocular Event-based Visual Inertial Odometry with Point and Line Features](https://mlsanity.com/paper/2209.12160/) (2022)
- [EvHandPose: Event-based 3D Hand Pose Estimation with Sparse Supervision](https://mlsanity.com/paper/2303.02862/) (2023)
- [Dynamic Event Camera Calibration](https://mlsanity.com/paper/2107.06749/) (2021)
- [TUMTraf Event: Calibration and Fusion Resulting in a Dataset for Roadside Event-Based and RGB Cameras](https://mlsanity.com/paper/2401.08474/) (2024)
- [ECMD: An Event-Centric Multisensory Driving Dataset for SLAM](https://mlsanity.com/paper/2311.02327/) (2023)
- [E-Calib: A Fast, Robust and Accurate Calibration Toolbox for Event Cameras](https://mlsanity.com/paper/2306.09078/) (2023)
- [Object-centric Cross-modal Feature Distillation for Event-based Object Detection](https://mlsanity.com/paper/2311.05494/) (2023)
- [Trajectory Representation and Landmark Projection for Continuous-Time Structure from Motion](https://mlsanity.com/paper/1805.02543/) (2018)
- [Event Camera-based Visual Odometry for Dynamic Motion Tracking of a Legged Robot Using Adaptive Time Surface](https://mlsanity.com/paper/2305.08962/) (2023)
- [Continuous-Time Spatiotemporal Calibration of a Rolling Shutter Camera-IMU System](https://mlsanity.com/paper/2108.07200/) (2021)

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

- [LET-US: Long Event-Text Understanding of Scenes](https://mlsanity.com/paper/2508.07401/) (2025)
- [eKalibr: Dynamic Intrinsic Calibration for Event Cameras From First Principles of Events](https://mlsanity.com/paper/2501.05688/) (2025)
- [Temporal and Rotational Calibration for Event-Centric Multi-Sensor Systems](https://mlsanity.com/paper/2508.12564/) (2025)
- [LiREC-Net: A Target-Free and Learning-Based Network for LiDAR, RGB, and Event Calibration](https://mlsanity.com/paper/2602.21754/) (2026)
- [Simplified Cross-Modal Calibration for Heterogeneous Event-RGB Stereo Systems](https://mlsanity.com/paper/2608.22965/) (2026)

## Abstract (arXiv)

Event camera, a bio-inspired asynchronous triggered camera, offers promising prospects for fusion with frame-based cameras owing to its low latency and high dynamic range. However, calibrating stereo vision systems that incorporate both event and frame-based cameras remains a significant challenge. In this letter, we present EF-Calib, a spatiotemporal calibration framework for event- and frame-based cameras using continuous-time trajectories. A novel calibration pattern applicable to both camera types and the corresponding event recognition algorithm is proposed. Leveraging the asynchronous nature of events, a derivable piece-wise B-spline to represent camera pose continuously is introduced, enabling calibration for intrinsic parameters, extrinsic parameters, and time offset, with analytical Jacobians provided. Various experiments are carried out to evaluate the calibration performance of EF-Calib, including calibration experiments for intrinsic parameters, extrinsic parameters, and time offset. Experimental results show that EF-Calib achieves the most accurate intrinsic parameters compared to current SOTA, the close accuracy of the extrinsic parameters compared to the frame-based results, and accurate time offset estimation. EF-Calib provides a convenient and accurate toolbox for calibrating the system that fuses events and frames. The code of this paper will also be open-sourced at: https://github.com/wsakobe/EF-Calib.