Motion from point matches: multiple of solutions
International Journal of Computer Vision
Reconstruction from Calibrated Cameras—A New Proof of the Kruppa-Demazure Theorem
Journal of Mathematical Imaging and Vision
The Geometry of Multiple Images: The Laws That Govern The Formation of Images of A Scene and Some of Their Applications
Algebraic Geometry and Computer Vision: Polynomial Systems, Real andComplex Roots
Journal of Mathematical Imaging and Vision
Estimation of Relative Camera Positions for Uncalibrated Cameras
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Globally Convergent Autocalibration Using Interval Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Minimal Solution for Relative Pose with Unknown Focal Length
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Globally Optimal Estimates for Geometric Reconstruction Problems
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Focal length calibration from two views: method and analysis of singular cases
Computer Vision and Image Understanding
Estimation of the epipole using optical flow at antipodal points
Computer Vision and Image Understanding
The six point algorithm revisited
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume part II
Robust focal length estimation by voting in multi-view scene reconstruction
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
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This paper presents a simple and practical solution to the 6-point 2-view focal-length estimation problem. Based on the hidden-variable technique we have derived a 15th degree polynomial in the unknown focal-length. During this course, a simple and constructive algorithm is established. To make use of multiple redundant measurements and then select the best solution, we suggest a kernel-voting scheme. The algorithm has been tested on both synthetic data and real images. Satisfactory results are obtained for both cases. For reference purpose we include our Matlab implementation in the paper, which is quite concise, consisting of 20 lines of code only. The result of this paper will make a small but useful module in many computer vision systems.