Abstract
Modern day camera calibration in computer vision dates back 20 years to a milestone paper from Zhang, A flexible new technique for camera calibration, which provided instructions along with code to allow novice users to perform monocular camera calibration using only readily available components. His work excelled in terms of usability and robustness and is therefore one of the most cited contributions in the field. Despite this amazing work and the many contributions since then the surge of cameras in mobile devices, robots, and automobiles calls for ever new, more accurate, and more flexible calibration methods. In this keynote presentation, I would like to explore the many facets of camera calibration, from new approaches in deep learning, which are able to estimate the parameters of a camera model from a single image to highly accurate methods using thousands of parameters, and from single cameras to heterogeneous multi-camera networks. Finally, I will address one of the main challenges of calibration, which is assessing the calibration result itself.
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