Robust fitting for multiple view geometry

Authors:
Olof Enqvist;Erik Ask;Fredrik Kahl;Kalle Åström
Affiliations:
Centre for Mathematical Sciences, Lund University, Sweden;Centre for Mathematical Sciences, Lund University, Sweden;Centre for Mathematical Sciences, Lund University, Sweden;Centre for Mathematical Sciences, Lund University, Sweden
Venue:
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Year:
2012

Citing 11
Cited 1

Polynomial-Time Geometric Matching for Object Recognition

International Journal of Computer Vision
Triangulation

Computer Vision and Image Understanding
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography

Communications of the ACM
Implementation techniques for geometric branch-and-bound matching methods

Computer Vision and Image Understanding
Removing Outliers Using The L\infty Norm

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Quasiconvex Optimization for Robust Geometric Reconstruction

IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal Randomized RANSAC

IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple-View Geometry Under the {$L_\infty$}-Norm

IEEE Transactions on Pattern Analysis and Machine Intelligence
Global Optimization through Rotation Space Search

International Journal of Computer Vision
Fast and Stable Polynomial Equation Solving and Its Application to Computer Vision

International Journal of Computer Vision
An adversarial optimization approach to efficient outlier removal

ICCV '11 Proceedings of the 2011 International Conference on Computer Vision

Numerically stable optimization of polynomial solvers for minimal problems

ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III

Quantified Score

Hi-index	0.00

Visualization

Abstract

How hard are geometric vision problems with outliers? We show that for most fitting problems, a solution that minimizes the number of outliers can be found with an algorithm that has polynomial time-complexity in the number of points (independent of the rate of outliers). Further, and perhaps more interestingly, other cost functions such as the truncated L2-norm can also be handled within the same framework with the same time complexity. We apply our framework to triangulation, relative pose problems and stitching, and give several other examples that fulfill the required conditions. Based on efficient polynomial equation solvers, it is experimentally demonstrated that these problems can be solved reliably, in particular for low-dimensional models. Comparisons to standard random sampling solvers are also given.