Robust regression and outlier detection
Robust regression and outlier detection
Sensitivity analysis in linear regression
Sensitivity analysis in linear regression
Motion and Structure From Two Perspective Views: Algorithms, Error Analysis, and Error Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Artificial Intelligence - Special volume on computer vision
In Defense of the Eight-Point Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Development and Comparison of Robust Methodsfor Estimating the Fundamental Matrix
International Journal of Computer Vision
The Geometry of Multiple Images: The Laws That Govern The Formation of Images of A Scene and Some of Their Applications
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
International Journal of Computer Vision
RANSAC for (Quasi-)Degenerate data (QDEGSAC)
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Correspondence-Free Determination of the Affine Fundamental Matrix
IEEE Transactions on Pattern Analysis and Machine Intelligence
Over-Parameterized Variational Optical Flow
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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Estimating the fundamental matrix from a pair of stereo images is one of the central problems in stereo vision. Typically, this estimation is based on a sparse set of point correspondences that has been obtained by a matching of characteristic image features. In this paper, however, we propose a completely different strategy: Motivated by the high precision of recent variational methods for computing the optic flow, we investigate the usefulness of their dense flow fields for recovering the fundamental matrix. To this end, we consider the state-of-the-art optic flow method of Brox et al. (ECCV 2004). Using non-robust and robust estimation techniques for determining the fundamental matrix, we compare the results computed from its dense flow fields to the ones estimated from a RANSAC method that is based on a sparse set of SIFT-matches. Scenarios for both converging and ortho-parallel camera settings are considered. In all cases, the computed results are significantly better than the ones obtained by the RANSAC method --- even without the explicit removal of outliers.