Department of Geomatics, National Cheng Kung University, No. 1, Daxue Road, East District, Tainan City 701, Taiwan.
Sensors (Basel). 2022 Dec 7;22(24):9602. doi: 10.3390/s22249602.
The precision modelling of intrinsic camera geometry is a common issue in the fields of photogrammetry (PH) and computer vision (CV). However, in both fields, intrinsic camera geometry has been modelled differently, which has led researchers to adopt different definitions of intrinsic camera parameters (ICPs), including focal length, principal point, radial distortion, decentring distortion, affinity and shear. These ICPs are indispensable for vision-based measurements. These differences can confuse researchers from one field when using ICPs obtained from a camera calibration software package developed in another field. This paper clarifies the ICP definitions used in each field and proposes an ICP transformation algorithm. The originality of this study lies in its use of least-squares adjustment, applying the image points involving ICPs defined in PH and CV image frames to convert a complete set of ICPs. This ICP transformation method is more rigorous than the simplified formulas used in conventional methods. Selecting suitable image points can increase the accuracy of the generated adjustment model. In addition, the proposed ICP transformation method enables users to apply mixed software in the fields of PH and CV. To validate the transformation algorithm, two cameras with different view angles were calibrated using typical camera calibration software packages applied in each field to obtain ICPs. Experimental results demonstrate that our proposed transformation algorithm can be used to convert ICPs derived from different software packages. Both the PH-to-CV and CV-to-PH transformation processes were executed using complete mathematical camera models. We also compared the rectified images and distortion plots generated using different ICPs. Furthermore, by comparing our method with the state of art method, we confirm the performance improvement of ICP conversions between PH and CV models.
内在相机几何的精密建模是摄影测量（PH）和计算机视觉（CV）领域的一个常见问题。然而，在这两个领域中，内在相机几何的建模方式都不同，这导致研究人员采用了不同的内在相机参数（ICPs）定义，包括焦距、主点、径向失真、偏心失真、仿射和剪切。这些 ICPs 对于基于视觉的测量是不可或缺的。这些差异可能会使来自一个领域的研究人员在使用另一个领域开发的相机校准软件包获得的 ICP 时感到困惑。本文澄清了每个领域中使用的 ICP 定义，并提出了一种 ICP 转换算法。本研究的原创性在于它使用最小二乘调整，将涉及 PH 和 CV 图像帧中定义的 ICP 的图像点应用于转换完整的 ICP 集。这种 ICP 转换方法比传统方法中使用的简化公式更严格。选择合适的图像点可以提高生成调整模型的准确性。此外，所提出的 ICP 转换方法使用户能够在 PH 和 CV 领域中应用混合软件。为了验证转换算法，使用每个领域中典型的相机校准软件包对两个具有不同视角的相机进行校准，以获得 ICP。实验结果表明，我们提出的转换算法可用于转换来自不同软件包的 ICP。PH 到 CV 和 CV 到 PH 的转换过程都是使用完整的数学相机模型执行的。我们还比较了使用不同 ICP 生成的校正图像和失真图。此外，通过将我们的方法与最先进的方法进行比较，我们确认了 PH 和 CV 模型之间的 ICP 转换性能的提高。
