从角点到基准标记：重新审视事件相机的棋盘格标定
From Corners to Fiducial Tags: Revisiting Checkerboard Calibration for Event Cameras
对做事件相机标定或多传感器标定的视觉研究者，这篇给出了不依赖灰度重建、直接从事件流检测棋盘格角点与 AprilTag 的流程：用边缘事件初始化角点，再向低事件密度区域迭代细化到亚像素，可直接用于事件-帧相机外参标定与遮挡场景下的标记检测。
原文依据 we leverage edge-driven event cues to initialize corner positions
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论传统的基于棋盘格的标准相机标定方法在应用于仿生事件相机时面临根本性限制。具体而言，这源于两个挑战：(i) 事件沿运动轨迹在不同时间戳上异步触发。若直接在图像平面上累积这些事件，会造成时序错位并产生模糊边缘。(ii) 事件相机上的棋盘格角点在其角点本身处表现出近乎零的事件发生率。这阻碍了可靠的角点定位，使标定变得困难。为解决这些问题，我们提出了一种新颖的标定框架，可直接从事件数据中检测棋盘格角点，而无需基于学习的灰度图像重建。我们首先从数学上分析了角点处事件缺失的现象。基于这一事实，我们利用边缘驱动的事件线索来初始化角点位置。利用棋盘格角点处近乎零的事件发生率，我们逐步将估计的角点向低事件密度区域细化，从而实现亚像素精度。此外，我们将角点检测扩展到 AprilTag 等基准标记，从而在部分可见或遮挡的情况下也能实现可靠检测。在自采集数据和公开数据上的评估表明，该方法能够实现可靠的棋盘格角点检测和稳定的相机标定。更多信息可通过以下链接获取：https://vision3d-lab.github.io/corner2tag/。
英文原文摘要 1462 字符
The conventional checkerboard-based calibration for standard cameras faces fundamental limitations when applied to bio-inspired event cameras. Specifically, this stems from two challenges: (i) Events are triggered asynchronously at different timestamps along motion trajectories. If we accumulate them directly on the image plane, it causes temporal misalignment and produces blurred edges. (ii) Checkerboard corners on event cameras show near-zero event occurrence at the corner itself. This hinders reliable corner localization and makes calibration difficult. To address these issues, we present a novel calibration framework that directly detects checkerboard corners from event data without learning-based grayscale image reconstruction. We first mathematically analyze the absence of events at corner points. Based on this fact, we then leverage edge-driven event cues to initialize corner positions. Using the near-zero event occurrence at checkerboard corners, we gradually refine the estimated corner toward low event-density regions, achieving sub-pixel accuracy. Furthermore, we extend the corner detection to fiducial markers such as AprilTag, resulting in reliable detection even under partial visibility or occlusion. Evaluations on self-collected and public data demonstrate reliable checkerboard corner detection and stable camera calibration. Additional information is available at the following link: https://vision3d-lab.github.io/corner2tag/.