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.

翻译：事件相机以其异步特性、低延迟和高动态范围引发了计算机视觉领域的范式转变。对于事件相机而言，为获取传感器内参并进行三维感知，标定始终至关重要。然而，由于传感器输出为异步二进制信号，传统的基于图像的标定技术并不适用。当前标定事件相机的标准方法依赖于闪烁模式或基于事件图像重建算法。这些方法难以在工厂环境中部署，且易受噪声和伪影影响，从而导致标定性能下降。为弥补这些不足，我们提出了E-Calib——一种新颖、快速、鲁棒且精确的面向事件相机的标定工具箱，其利用非对称圆形网格对离焦场景具有鲁棒性的特点。该方法在不同事件相机模型、不同几何特性的圆形网格以及具有挑战性的光照条件下进行了多项严格实验测试。结果表明，我们的方法在检测成功率、重投影误差和外参估计精度方面均优于现有技术水平。