# eWand: A calibration framework for wide baseline frame-based and event-based camera systems

arXiv: https://arxiv.org/abs/2309.12685 · HTML: https://mlsanity.com/paper/2309.12685/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 22 Sep 2023.

## Builds on (most cited first, 8 of 8)

- [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)
- [Event-aided Direct Sparse Odometry](https://mlsanity.com/paper/2204.07640/) (2022)
- [Semi-Dense 3D Reconstruction with a Stereo Event Camera](https://mlsanity.com/paper/1807.07429/) (2018)
- [How to Calibrate Your Event Camera](https://mlsanity.com/paper/2105.12362/) (2021)
- [Dynamic Event Camera Calibration](https://mlsanity.com/paper/2107.06749/) (2021)
- [Frequency Cam: Imaging Periodic Signals in Real-Time](https://mlsanity.com/paper/2211.00198/) (2022)
- [E-Calib: A Fast, Robust and Accurate Calibration Toolbox for Event Cameras](https://mlsanity.com/paper/2306.09078/) (2023)

## Built on by (most cited first, 6 of 6)

- [Detection of Fast-Moving Objects with Neuromorphic Hardware](https://mlsanity.com/paper/2403.10677/) (2024)
- [A multi-modal table tennis robot system](https://mlsanity.com/paper/2310.19062/) (2023)
- [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)
- [Multi-Camera Self-Calibration in Sports Motion Capture: Leveraging Human and Stick Poses](https://mlsanity.com/paper/2604.17567/) (2026)
- [Simplified Cross-Modal Calibration for Heterogeneous Event-RGB Stereo Systems](https://mlsanity.com/paper/2608.22965/) (2026)

## Abstract (arXiv)

Accurate calibration is crucial for using multiple cameras to triangulate the position of objects precisely. However, it is also a time-consuming process that needs to be repeated for every displacement of the cameras. The standard approach is to use a printed pattern with known geometry to estimate the intrinsic and extrinsic parameters of the cameras. The same idea can be applied to event-based cameras, though it requires extra work. By using frame reconstruction from events, a printed pattern can be detected. A blinking pattern can also be displayed on a screen. Then, the pattern can be directly detected from the events. Such calibration methods can provide accurate intrinsic calibration for both frame- and event-based cameras. However, using 2D patterns has several limitations for multi-camera extrinsic calibration, with cameras possessing highly different points of view and a wide baseline. The 2D pattern can only be detected from one direction and needs to be of significant size to compensate for its distance to the camera. This makes the extrinsic calibration time-consuming and cumbersome. To overcome these limitations, we propose eWand, a new method that uses blinking LEDs inside opaque spheres instead of a printed or displayed pattern. Our method provides a faster, easier-to-use extrinsic calibration approach that maintains high accuracy for both event- and frame-based cameras.