Shape and motion from image streams under orthography: a factorization method
International Journal of Computer Vision
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
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
Recursive Structure and Motion from Image Sequences using Shape and Depth Spaces
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)
Damped Newton Algorithms for Matrix Factorization with Missing Data
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Iterative Extensions of the Sturm/Triggs Algorithm: Convergence and Nonconvergence
IEEE Transactions on Pattern Analysis and Machine Intelligence
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We show that SIESTA, the simplest iterative extension of the Sturm/Triggs algorithm, descends an error function. However, we prove that SIESTA does not converge to usable results. The iterative extension of Mahamud et al. has similar problems, and experiments with “balanced” iterations show that they can fail to converge. We present CIESTA, an algorithm which avoids these problems. It is identical to SIESTA except for one extra, simple stage of computation. We prove that CIESTA descends an error and approaches fixed points. Under weak assumptions, it converges. The CIESTA error can be minimized using a standard descent method such as Gauss–Newton, combining quadratic convergence with the advantage of minimizing in the projective depths.