发表机构
Center for Machine Learning Heilbronn University of Applied Sciences（海尔布隆应用科学大学机器学习中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对异构事件-RGB立体系统的标定瓶颈，提出无运动跨模态标定框架，简化流程且降低重投影误差，在机器人眼到手标定中具实用性。
AI 中文摘要
基于事件的相机与基于帧的相机之间的精确外参标定仍是异构立体系统的实际瓶颈。现有方法通常需要传感器或目标运动、精确同步，或计算成本高昂的事件到图像重建。我们提出一种简单的无运动跨模态标定框架，该框架使用显示在标准消费级显示器上的时间调制混合ChArUco标定板。通过在原始图案和部分混合版本之间交替切换，该目标能可靠触发事件，同时对基于帧的相机保持连续可见，避免空白帧并将同步约束降低为基于触发器的粗对准。我们将事件离散化为与RGB图像粗对准的帧，应用轻量级去噪，并执行基于ChArUco的内参与立体外参标定。大量实验评估了其对混合不透明度、显示器亮度、外部照明、视角和手持采集的鲁棒性。与最强的基于运动的基准方法（E2Calib + Kalibr）和无运动的基准方法（Plasberg等人的方法）相比，我们的方法分别将平均重投影误差降低了44%和6%，同时大幅简化了标定流程。最后，我们在机器人眼到手标定案例研究中展示了其实用性，即使在部分遮挡下也能呈现一致的变换和稳定的下游几何测量。代码可在该https URL公开获取。
英文摘要
Accurate extrinsic calibration between event-based and frame-based cameras remains a practical bottleneck for heterogeneous stereo systems. Existing approaches often require sensor or target motion, precise synchronization, or computationally expensive event-to-image reconstruction. We propose a simple, motion-free cross-modal calibration framework that uses a temporally modulated, blended ChArUco target presented on standard consumer displays. By alternating between the original pattern and a partially blended version, the target reliably triggers events while remaining continuously observable to a frame-based camera, avoiding blank frames and reducing synchronization constraints to a coarse, trigger-based alignment. We discretize events into frames coarsely aligned with the RGB images, apply lightweight denoising, and perform ChArUco-based intrinsic and stereo extrinsic calibration. Extensive experiments assess robustness to blending opacity, display brightness, external illumination, viewing angle, and handheld acquisition. Compared to the strongest motion-based reference (E2Calib + Kalibr) and a non-motion-based reference (Plasberg et al.), our approach reduces the mean reprojection error by $44\%$ and $6\%$, respectively, while substantially simplifying the calibration procedure. Finally, we demonstrate practical utility in a robotic eye-to-hand calibration case study, showing consistent transformations and stable downstream geometric measurements even under partial occlusions. Code is publicly available at https://github.com/nhessenthaler/simple-evrgb-cal.
CommentsAccepted to the 37th British Machine Vision Conference (BMVC) 2026
