Line Processes for Highly Accurate Geometric Camera Calibration

The availability of highly accurate geometric camera calibration is an implicit assumption for many 3D computer vision algorithms. Single-camera applications like structure from motion or rigid multi-camera systems that use stereo matching algorithms depend on calibration accuracy. We present an approach that has proven to deliver accurate geometric information in a reliable, repeatable manner for many industrial applications. The major limitation in typical camera calibration methods is the printing accuracy of the used target. We address  this problem by modeling the calibration target uncertainty as a line process and incorporate a lifted cost function into a bundle adjustment formulation. The regularized target deformation is incorporated directly into the nonlinear least-squares estimation and is solved in a  non-iterative, principled framework.