Tan Lei, Wang Yaonan, Yu Hongshan, Zhu Jiang
College of Electrical and Information Engineering, Hunan University, Yuelushan 410082, China.
National Engineering Laboratory for Robot Visual Perception and Control Technology, Hunan University, Yuelushan 410082, China.
Sensors (Basel). 2017 Mar 27;17(4):685. doi: 10.3390/s17040685.
Camera calibration plays a critical role in 3D computer vision tasks. The most commonly used calibration method utilizes a planar checkerboard and can be done nearly fully automatically. However, it requires the user to move either the camera or the checkerboard during the capture step. This manual operation is time consuming and makes the calibration results unstable. In order to solve the above problems caused by manual operation, this paper presents a full-automatic camera calibration method using a virtual pattern instead of a physical one. The virtual pattern is actively transformed and displayed on a screen so that the control points of the pattern can be uniformly observed in the camera view. The proposed method estimates the camera parameters from point correspondences between 2D image points and the virtual pattern. The camera and the screen are fixed during the whole process; therefore, the proposed method does not require any manual operations. Performance of the proposed method is evaluated through experiments on both synthetic and real data. Experimental results show that the proposed method can achieve stable results and its accuracy is comparable to the standard method by Zhang.
相机校准在三维计算机视觉任务中起着关键作用。最常用的校准方法使用平面棋盘格，并且几乎可以完全自动完成。然而，它要求用户在拍摄步骤中移动相机或棋盘格。这种手动操作既耗时又会使校准结果不稳定。为了解决由手动操作引起的上述问题，本文提出了一种使用虚拟图案而非物理图案的全自动相机校准方法。虚拟图案会被主动变换并显示在屏幕上，以便在相机视图中能够均匀地观察到图案的控制点。所提出的方法根据二维图像点与虚拟图案之间的点对应关系来估计相机参数。在整个过程中相机和屏幕都是固定的；因此，所提出的方法不需要任何手动操作。通过对合成数据和真实数据进行实验来评估所提出方法的性能。实验结果表明，所提出的方法能够获得稳定的结果，并且其精度与张的标准方法相当。
