Radial Distortion from Epipolar Constraint for Rectilinear Cameras

Abstract

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 added. Descriptor matched scale-invariant feature (SIFT) point pairs that contain false matches can readily be given to our algorithm, EPOS (EpiPOlar-based Solver), as input. The processing is automated to the point where EPOS solves the distortion whether its type is barrel or pincushion or reports if there is no need for correction.

Experimental Study of Radial Distortion Compensation for Camera Submerged Underwater Using Open SaltWaterDistortion Data Set

“… Calibration algorithms based on active vision, calculating the parameters of RD correction from a series of scene images; in this case, information about camera movement is known during calibration [ 23 ]. Self-calibration algorithms that calculate the RD correction parameters by checking the correctness of the epipolar constraint for a series of images of the same scene taken from different angles [ 24 ]. Self-calibration algorithms that calculate camera parameters from a single-shot image [ 25 , 26 ].…”

“…Self-calibration algorithms that calculate the RD correction parameters by checking the correctness of the epipolar constraint for a series of images of the same scene taken from different angles [ 24 ].…”

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

“…Due to a mistake during the production process, the J. Imaging Editorial Office and the authors wish to make this correction to the paper written by Lehtola et al [1]. Extra % symbols were accidentally added.…”