Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Euclidean structure from uncalibrated images
BMVC 94 Proceedings of the conference on British machine vision (vol. 2)
Artificial Intelligence - Special volume on computer vision
Performance characterization of fundamental matrix estimation under image degradation
Machine Vision and Applications - Special issue on performance evaluation
Self-Calibration of a Moving Camera from PointCorrespondences and Fundamental Matrices
International Journal of Computer Vision
Determining the Epipolar Geometry and its Uncertainty: A Review
International Journal of Computer Vision
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
The Geometry of Multiple Images: The Laws That Govern The Formation of Images of A Scene and Some of Their Applications
Motion and Structure from Image Sequences
Motion and Structure from Image Sequences
Closed-Form Solutions for the Euclidean Calibration of a Stereo Rig
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Critical Motion Sequences for Monocular Self-Calibration and Uncalibrated Euclidean Reconstruction
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
From projective to Euclidean reconstruction
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Projective Translations and Affine Stereo Calibration
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Metric calibration of a stereo rig
VSR '95 Proceedings of the IEEE Workshop on Representation of Visual Scenes
A Practical Self-Calibration Method of Rotating and Zooming Cameras
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Self-Calibration of a Stereo Rig in a Planar Scene by Data Combination
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Self-Calibration and Euclidean Reconstruction Using Motions of a Stereo Rig
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Bayesian self-calibration of a moving camera
Computer Vision and Image Understanding
The impact of radial distortion on the self-calibration of rotating cameras
Computer Vision and Image Understanding
Numerical Methods for Unconstrained Optimization and Nonlinear Equations (Classics in Applied Mathematics, 16)
Initialization method for self-calibration using 2-views
Pattern Recognition
Linear auto-calibration for ground plane motion
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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This paper addresses the problem of self-calibration from one unknown motion of an uncalibrated stereo rig. Unlike the existing methods for stereo rig self-calibration, which have been focused on applying the autocalibration paradigm using both motion and stereo correspondences, our method does not require the recovery of stereo correspondences. Our method combines purely algebraic constraints with implicit geometric constraints. Assuming that the rotational part of the stereo geometry has two unknown degrees of freedom (i.e., the third dof is roughly known), and that the principle point of each camera is known, we first show that the computation of the intrinsic and extrinsic parameters of the stereo rig can be recovered from the motion correspondences only, i.e., the monocular fundamental matrices. We then provide an initialization procedure for the proposed non-linear method. We provide an extensive performance study for the method in the presence of image noise. In addition, we study some of the aspects related to the 3D motion that govern the accuracy of the proposed self-calibration method. Experiments conducted on synthetic and real data/images demonstrate the effectiveness and efficiency of the proposed method.