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Provably-Convergent Iterative Methods for Projective Structure from Motion
S. Mahamud, M. Hebert, Y. Omori, and J. Ponce
IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2001.

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The estimation of the projective structure of a scene from image correspondences can be formulated as the minimization of the mean-squared distance between predicted and observed image points with respect to the projection matrices, the scene point positions, and their depths. Since these unknowns are not independent, constraints must be chosen to ensure that the optimization process is well posed. This paper examines three plausible choices, and shows that the first one leads to the Sturm-Triggs projective factorization algorithm, while the other two lead to new provably-convergent approaches. Experiments with synthetic and real data are used to compare the proposed techniques to the Sturm-Triggs algorithm and bundle adjustment.

S. Mahamud, M. Hebert, Y. Omori, and J. Ponce, "Provably-Convergent Iterative Methods for Projective Structure from Motion," IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2001.

@inproceedings{Mahamud_2001_4710,
   author = "Shyjan Mahamud and Martial Hebert and Y. Omori and J. Ponce",
   title = "Provably-Convergent Iterative Methods for Projective Structure from Motion",
   booktitle = "IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)",
   year = "2001"
}