A Model-Selection Framework for Multibody Structure-and-Motion of Image Sequences
International Journal of Computer Vision
Is Dense Optic Flow Useful to Compute the Fundamental Matrix?
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
Modeling and Recognition of Landmark Image Collections Using Iconic Scene Graphs
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Flow separation for fast and robust stereo odometry
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Motion estimation by decoupling rotation and translation in catadioptric vision
Computer Vision and Image Understanding
Robust Algebraic Segmentation of Mixed Rigid-Body and Planar Motions from Two Views
International Journal of Computer Vision
Simultaneous plane extraction and 2D homography estimation using local feature transformations
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
Pose estimation from multiple cameras based on Sylvester's equation
Computer Vision and Image Understanding
Landmark image classification using 3D point clouds
Proceedings of the international conference on Multimedia
Degeneracy from twisted cubic under two views
Journal of Computer Science and Technology
Modeling and Recognition of Landmark Image Collections Using Iconic Scene Graphs
International Journal of Computer Vision
Relative pose estimation for planetary entry descent landing
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume part II
Dense versus Sparse Approaches for Estimating the Fundamental Matrix
International Journal of Computer Vision
Twisted cubic: degeneracy degree and relationship with general degeneracy
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
Recovering epipolar geometry from images of smooth surfaces
Pattern Recognition and Image Analysis
Efficient and robust model fitting with unknown noise scale
Image and Vision Computing
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The computation of relations from a number of potential matches is a major task in computer vision. Often RANSAC is employed for the robust computation of relations such as the fundamental matrix. For (quasi-)degenerate data however, it often fails to compute the correct relation. The computed relation is always consistent with the data but RANSAC does not verify that it is unique. The paper proposes a framework that estimates the correct relation with the same robustness as RANSAC even for (quasi-)degenerate data. The approach is based on a hierarchical RANSAC over the number of constraints provided by the data. In contrast to all previously presented algorithms for (quasi-)degenerate data our technique does not require problem specific tests or models to deal with degenerate configurations. Accordingly it can be applied for the estimation of any relation on any data and is not limited to a special type of relation as previous approaches. The results are equivalent to the results achieved by state of the art approaches that employ knowledge about degeneracies.