Camera calibration is a critical process in 3D vision, im pacting applications in autonomous driving, robotics, ar chitecture, and so on. This paper focuses on enhancing feature extraction for chessboard corner detection, a key step in calibration. We analyze existing methods, high lighting their limitations and propose a novel sub-pixel refinement approach based on symmetry, which signifi cantly improves accuracy for visible light cameras. Un like prior symmetry based method that assume a contin uous physical pattern, our approach accounts for abrupt changes in visible light camera images and defocus ef fects. We introduce a simplified objective function that reduces computation time and mitigates overfitting risks. Furthermore, we derive an explicit expression for the pixel value of a blurred edge, providing insights into the relationship between pixel value and center intensity. Our method demonstrates superior performance, achiev ing substantial accuracy improvements over existing tech niques, particularly in the context of visible light cam era calibration. Our code is available from https: //github.com/spdfghi/Accurate-Checkerboard Corner-Detection-under-Defoucs.git.

翻译：相机标定是三维视觉中的关键过程，对自动驾驶、机器人技术、建筑等领域的应用至关重要。本文重点改进棋盘格角点检测中的特征提取，这是标定的关键步骤。我们分析了现有方法，指出其局限性，并提出了一种基于对称性的新型亚像素细化方法，显著提高了可见光相机的检测精度。与先前假设物理图案连续性的对称性方法不同，我们的方法考虑了可见光相机图像中的突变和散焦效应。我们引入了一个简化的目标函数，减少了计算时间并降低了过拟合风险。此外，我们推导出了模糊边缘像素值的显式表达式，揭示了像素值与中心强度之间的关系。我们的方法展现出卓越的性能，相比现有技术实现了显著的精度提升，特别是在可见光相机标定场景中。我们的代码可从 https://github.com/spdfghi/Accurate-Checkerboard-Corner-Detection-under-Defoucs.git 获取。