Radial Distortion from Epipolar Constraint for Rectilinear Cameras

Radial Distortion from Epipolar Constraint for Rectilinear Cameras

Lens distortion causes difficulties for 3D reconstruction, when uncalibrated image sets with weak geometry are used. We show that the largest part of lens distortion, known as the radial distortion, can be estimated along with the center of distortion from the epipolar constraint separately and before bundle adjustment without any calibration rig. The estimate converges as more image pairs are ...

Erratum: Ville V. Lehtola, et al. Radial Distortion from Epipolar Constraint for Rectilinear Cameras. J. Imaging 2017, 3, 8

Estimating Global Uncertainty in Epipolar Geometry for Vehicle-Mounted Cameras

We present a method for estimating the global uncertainty of epipolar geometry with applications to autonomous vehicle navigation. Such uncertainty information is necessary for making informed decisions regarding the confidence of a motion estimate, since we must otherwise accept the estimate without any knowledge of the probability that the estimate is in error. For example, we may wish to fus...

Uncertainty analysis of the DLT-Lines calibration algorithm for cameras with radial distortion

3D metric data of environmental structures is nowadays present in many information sources (maps, GIS) and can be easily acquired with modern depth sensing technology (RGBD, laser). This wealth of information can be readily used for single view calibration of 2D cameras with radial distortion, provided that image structures can be matched with the 3D data. In this paper we present an analysis o...

Cylindrical Rectication to Minimize Epipolar Distortion

We propose a new rectification method for aligning epipolar lines of a pair of stereo images taken under any camera geometry. It effectively remaps both images onto the surface of a cylinder instead of a plane, which is used in common rectification methods. For a large set of camera motions, remapping to a plane has the drawback of creating rectified images that are potentially infinitely large...