An Affine Invariant Interest Point Detector
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Grouping with Asymmetric Affinities: A Game-Theoretic Perspective
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Modeling the World from Internet Photo Collections
International Journal of Computer Vision
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Multilevel spectral hypergraph partitioning with arbitrary vertex sizes
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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In this paper we introduce a robust matching technique that allows to operate a very accurate selection of corresponding feature points from multiple views. Robustness is achieved by enforcing global geometric consistency at an early stage of the matching process, without the need of ex-post verification through reprojection. Two forms of global consistency are proposed, but in both cases they are reduced to pairwise compatibilities making use of the size and orientation information provided by common feature descriptors. Then a game-theoretic approach is used to select a maximally consistent set of candidate matches, where highly compatible matches are enforced while incompatible correspondences are driven to extinction. The effectiveness of the approach in estimating camera parameters for bundle adjustment is assessed and compared with state-of-the-art techniques.