In Defense of the Eight-Point Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Camera Self-Calibration: Theory and Experiments
ECCV '92 Proceedings of the Second European Conference on Computer Vision
A Rational Function Lens Distortion Model for General Cameras
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Fundamental Matrix for Cameras with Radial Distortion
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Multi-View Geometry of 1D Radial Cameras and its Application to Omnidirectional Camera Calibration
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Parameter-Free Radial Distortion Correction with Center of Distortion Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A matter of notation: Several uses of the Kronecker product in 3D computer vision
Pattern Recognition Letters
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Automatic Generator of Minimal Problem Solvers
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Fast and robust numerical solutions to minimal problems for cameras with radial distortion
Computer Vision and Image Understanding
Estimation of omnidirectional camera model from epipolar geometry
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
| Hi-index | 0.00 |
The radial undistortion model proposed by Fitzgibbon and the radial fundamental matrix were early steps to extend classical epipolar geometry to distorted cameras. Later minimal solvers have been proposed to find relative pose and radial distortion, given point correspondences between images. However, a big drawback of all these approaches is that they require the distortion center to be exactly known. In this paper we show how the distortion center can be absorbed into a new radial fundamental matrix. This new formulation is much more practical in reality as it allows also digital zoom, cropped images and camera-lens systems where the distortion center does not exactly coincide with the image center. In particular we start from the setting where only one of the two images contains radial distortion, analyze the structure of the particular radial fundamental matrix and show that the technique also generalizes to other linear multi-view relationships like trifocal tensor and homography. For the new radial fundamental matrix we propose different estimation algorithms from 9,10 and 11 points. We show how to extract the epipoles and prove the practical applicability on several epipolar geometry image pairs with strong distortion that - to the best of our knowledge - no other existing algorithm can handle properly.