Camera calibration is a first and fundamental step in various computer vision applications. Despite being an active field of research, Zhang's method remains widely used for camera calibration due to its implementation in popular toolboxes. However, this method initially assumes a pinhole model with oversimplified distortion models. In this work, we propose a novel approach that involves a pre-processing step to remove distortions from images by means of Gaussian processes. Our method does not need to assume any distortion model and can be applied to severely warped images, even in the case of multiple distortion sources, e.g., a fisheye image of a curved mirror reflection. The Gaussian processes capture all distortions and camera imperfections, resulting in virtual images as though taken by an ideal pinhole camera with square pixels. Furthermore, this ideal GP-camera only needs one image of a square grid calibration pattern. This model allows for a serious upgrade of many algorithms and applications that are designed in a pure projective geometry setting but with a performance that is very sensitive to nonlinear lens distortions. We demonstrate the effectiveness of our method by simplifying Zhang's calibration method, reducing the number of parameters and getting rid of the distortion parameters and iterative optimization. We validate by means of synthetic data and real world images. The contributions of this work include the construction of a virtual ideal pinhole camera using Gaussian processes, a simplified calibration method and lens distortion removal.

翻译：相机标定是各种计算机视觉应用中最基础和关键的步骤。尽管该领域研究活跃，张氏方法因其在流行工具包中的实现而仍被广泛用于相机标定。然而，该方法最初假设针孔模型并采用过度简化的畸变模型。本文提出一种新颖方法，通过高斯过程作为预处理步骤来消除图像畸变。我们的方法无需假设任何畸变模型，可应用于严重变形的图像，甚至能处理多个畸变源的情况（例如曲面镜反射的鱼眼图像）。高斯过程捕捉所有畸变和相机缺陷，生成仿佛由理想针孔相机（像素为正方形）拍摄的虚拟图像。此外，这种理想的GP相机只需一张正方形网格标定板图像。该模型能显著升级许多基于纯射影几何设计但对非线性镜头畸变敏感的算法和应用。我们通过简化张氏标定方法验证了本方法的有效性：减少参数数量、消除畸变参数和迭代优化。我们利用合成数据和真实图像进行验证。本文的贡献包括：利用高斯过程构建虚拟理想针孔相机、简化标定方法以及去除镜头畸变。