Robust regression and outlier detection
Robust regression and outlier detection
Performance of optical flow techniques
International Journal of Computer Vision
In Defense of the Eight-Point Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Hierarchical Estimation and Segmentation of Dense Motion Fields
International Journal of Computer Vision
Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods
International Journal of Computer Vision
On the Spatial Statistics of Optical Flow
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Coarse to over-fine optical flow estimation
Pattern Recognition
Over-Parameterized Variational Optical Flow
International Journal of Computer Vision
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
Joint estimation of motion, structure and geometry from stereo sequences
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
A Database and Evaluation Methodology for Optical Flow
International Journal of Computer Vision
Simultaneous Camera Pose and Correspondence Estimation with Motion Coherence
International Journal of Computer Vision
Dense versus Sparse Approaches for Estimating the Fundamental Matrix
International Journal of Computer Vision
Over-Parameterized optical flow using a stereoscopic constraint
SSVM'11 Proceedings of the Third international conference on Scale Space and Variational Methods in Computer Vision
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Traditional estimation methods for the fundamental matrix rely on a sparse set of point correspondences that have been established by matching salient image features between two images. Recovering the fundamental matrix from dense correspondences has not been extensively researched until now. In this paper we propose a new variational model that recovers the fundamental matrix from a pair of uncalibrated stereo images, and simultaneously estimates an optical flow field that is consistent with the corresponding epipolar geometry. The model extends the highly accurate optical flow technique of Brox et al.(2004) by taking the epipolar constraint into account. In experiments we demonstrate that our approach is able to produce excellent estimates for the fundamental matrix and that the optical flow computation is on par with the best techniques to date.