Shape and motion from image streams under orthography: a factorization method
International Journal of Computer Vision
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple view geometry in computer vision
Multiple view geometry in computer vision
A Factorization Based Algorithm for Multi-Image Projective Structure and Motion
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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ICCV '99 Proceedings of the International Workshop on Vision Algorithms: Theory and Practice
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Proceedings of the ACM 2nd international workshop on Video surveillance & sensor networks
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Reconstructing camera projection matrices from multiple pairwise overlapping views
Computer Vision and Image Understanding
Affine Approximation for Direct Batch Recovery of Euclidian Structure and Motion from Sparse Data
International Journal of Computer Vision
A convenient multicamera self-calibration for virtual environments
Presence: Teleoperators and Virtual Environments
Optimization Algorithms on Subspaces: Revisiting Missing Data Problem in Low-Rank Matrix
International Journal of Computer Vision
Reconstructing camera projection matrices from multiple pairwise overlapping views
Computer Vision and Image Understanding
Consistent multi-view reconstruction from epipolar geometries with outliers
SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
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CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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This paper proposes a method for recovery of projective shape and motion from multiple images by factorization of a matrix containing the images of all scene points. Compared to previous methods, this method can handle perspective views and occlusions jointly. The projective depths of image points are estimated by the method of Sturm & Triggs [11] using epipolar geometry. Occlusions are solved by the extension of the method by Jacobs [8] for filling of missing data. This extension can exploit the geometry of perspective camera so that both points with known and unknown projective depths are used. Many ways of combining the two methods exist, and therefore several of them have been examined and the one with the best results is presented. The new method gives accurate results in practical situations, as demonstrated here with a series of experiments on laboratory and outdoor image sets. It becomes clear that the method is particularly suited for wide base-line multiple view stereo.