Self-Calibration and Euclidean Reconstruction Using Motions of a Stereo Rig

Authors:
Radu Horaud;Gabriella Csurka
Affiliations:
-;-
Venue:
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Year:
1998

Citing 0
Cited 9

Algebraic and Geometric Tools to Compute Projective and Permutation Invariants

IEEE Transactions on Pattern Analysis and Machine Intelligence
Stratified Self-Calibration with the Modulus Constraint

IEEE Transactions on Pattern Analysis and Machine Intelligence
Aligning Non-Overlapping Sequences

International Journal of Computer Vision - Marr Prize Special Issue
Binocular Self-Alignment and Calibration from Planar Scenes

ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Stereo Autocalibration from One Plane

ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Projective Translations and Affine Stereo Calibration

CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Automated Alignment of Robotic Pan-Tilt Camera Units Using Vision

International Journal of Computer Vision
Self-calibration of a stereo rig using monocular epipolar geometries

Pattern Recognition
Evaluation of a novel calibration technique for optically tracked oblique laparoscopes

MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper describes a method to upgrade projective reconstruction to affine and to metric reconstructions using rigid gener almotions of a stereo rig. We make clear the algebraic relationships between projective reconstruction, the plane at infinity (affine reconstruction), camera calibration, and metric reconstruction. We show that all the computations can be carried out using standard linear resolution methods and that these methods compare favorably with nonlinear optimization methods in the presence of Gaussian noise. We carry out a theoretical error analysis which quantify the relative importance of the accuracies of projective-to-affine conversion and affine-to-Euclidean conversion. Experiments with real data are consistent with the theoretical error analysis and with a sensitivity analysis performed with simulated data.