Robust Camera Calibration using Inaccurate Targets

Abstract

Accurate intrinsic camera calibration is essential to any computer vision task that
\ninvolves image based measurements. Given its crucial role with respect to precision, a
\nlarge number of approaches have been proposed over the last decades. Despite this rich
\nliterature, steady advancements in imaging hardware regularly push forward the need
\nfor even more accurate techniques. Some authors suggest generalizations of the camera
\nmodel itself, others propose novel designs for calibration targets or different optimization
\nschemas. In this paper we take a completely different route by directly addressing one
\nof the most overlooked problems in practical calibration scenarios. Specifically, we drop
\nthe assumption that the target is known with enough precision and we adjust it in an
\niterative way as part of the whole process. This is in fact the case with the typical target
\nused in most of the calibration literature, which is usually printed on paper and stitched
\non a flat surface. In the experimental section we show that even with such a cheaply
\ncrafted target it is possible to obtain a very accurate camera calibration that outperforms
\nthose obtained with well-known standard techniques.