Shape and motion from image streams under orthography: a factorization method
International Journal of Computer Vision
Projective Reconstruction and Invariants from Multiple Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Invariants of 6 points from 3 uncalibrated images
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International Journal of Computer Vision
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International Journal of Computer Vision
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International Journal of Computer Vision
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ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume II - Volume II
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ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume II - Volume II
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ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
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CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Factorization Methods for Projective Structure and Motion
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
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VSR '95 Proceedings of the IEEE Workshop on Representation of Visual Scenes
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ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
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ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Ambiguity in Reconstruction From Images of Six Points
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
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ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
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On Camera Calibration with Linear Programming and Loop Constraint Linearization
International Journal of Computer Vision
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The paper has two main contributions: The first is a set of methods for computing structure and motion for m ≥ 3 views of 6 points. It is shown that a geometric image error can be minimized over all views by a simple three parameter numerical optimization. Then, that an algebraic image error can be minimized over all views by computing the solution to a cubic in one variable. Finally, a minor point, is that this "quasi-linear" linear solution enables a more concise algorithm, than any given previously, for the reconstruction of 6 points in 3 views. The second contribution is an m view n ≥ 6 point robust reconstruction algorithm which uses the 6 point method as a search engine. This extends the successful RANSAC based algorithms for 2-views and 3-views to m views. The algorithm can cope with missing data and mismatched data and may be used as an efficient initializer for bundle adjustment. The new algorithms are evaluated on synthetic and real image sequences, and compared to optimal estimation results (bundle adjustment).