Camera calibration by vision threads with pixel-resolved focus measurement

像照相机和投影仪这样的成像系统广泛用于二维和三维坐标测量或机器视觉。例如，这是在条纹投影和条纹反射技术中完成的，其中用相机观察投影或显示的图案。光学成像设备，例如CCD或CMOS相机，将来自物体空间的光线映射到图像空间中的2D点。为了建立正确的物像关系，必须确定映射函数（包括透镜畸变）。摄像机标定是视觉测量过程的基础，经典的摄像机标定方法是以针孔摄像机为模型。在此基础上的摄影测量标定得到了广泛的研究，但也越来越接近极限。然而，光学计量中的新研究任务，如高效的多相机或综合投影仪或监视器校准，需要更准确和灵活的程序。代替进一步调整针孔模型，通用相机校准或所谓的视觉射线校准的更复杂的方法描述了每个像素的独立射线。它可以更精确地补偿透镜畸变的影响，比其他技术更灵活，但仍需要深入研究才能充分发挥其潜力。随着视觉射线校准的好处，需要密集的数据采集和评估。典型的校准模式，如棋盘格是不够的了。相反，在显示器上使用相移技术以获得所需的空间数据密度。然而，对于校准，需要大量的时间来重建的确切位置和显示位置的对齐，直到视觉射线是从data.In这个建议中，我们提出了一种替代的优化方法，它使用视觉线程，而不是视觉射线。这些连接不同显示位置之间的像素的视点。如果它们变得“紧张”，则位置和对准被直接转移到期望的星座中。使用这种方法，优化应该比以前更快，更准确。此外，我们提出了一种方法来计算每个像素的焦距从相同的数据，这是在一方面用来支持视觉线程，并在另一方面为用户提供了一个方便的访问他们的系统的焦点信息。

Imaging systems like cameras and projectors are widely used for two-dimensional- and three-dimensional coordinate measurement or machine vision. For example, this is done in fringe-projection- and fringe-reflection techniques, where projected or displayed pattern are observed with cameras. The optical imaging device, for instance a CCD or CMOS camera, maps light rays from object space into 2D points in image space. The mapping function (including lens distortion) must be determined in order to build the correct object-image relation. This is done by camera calibration, which are therefore fundamental for visual measurement processes.The classic and common approach of camera calibration assumes the model of a pinhole camera. The photogrammetric calibration based on it has been researched widely and is reaching its limits more often. New research tasks in optical metrology like efficient multi camera- or comprehensive projector or monitor calibration, however, require more accurately and flexible procedures. Instead of further adapting the pinhole model, the more sophisticated approach of the generic camera calibration or so-called vision-ray-calibration describes independent rays for every pixel. It compensates the effects of lens distortions more precisely and is more flexible then other techniques, but still requires thorough investigation to achieve its full potential.With the benefits of the vision-ray-calibration comes the need for intense data acquisition and evaluation. The typical calibration pattern like checkerboards are not sufficient anymore. Instead the phase shifting technique is used on a display to get the required spatial data density. For the calibration it takes, however, a lot of time to reconstruct the exact position and alignment of the display positions, until the vision rays are determined from the data.In this proposal we present an alternative optimization method, which uses vision-threads instead of vision-rays. These connect the viewpoints of a pixel between the different display places. If they become "tensed", positions and alignment are transferred directly into the desired constellation. With this method an optimization should be faster and more accurate than before. Furthermore, we present a way to calculate the focal length for each pixel from the same data, which is at one hand used to support the vision-threads and at the other hand provide users an easy access to the focal information of their systems.