Shape and motion from image streams under orthography: a factorization method
International Journal of Computer Vision
A Flexible New Technique for Camera Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple view geometry in computer vision
Multiple view geometry in computer vision
Fast and Accurate Algorithms for Projective Multi-Image Structure from Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
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)
Fast and Accurate Self-Calibration
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
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
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Perspective Nonrigid Shape and Motion Recovery
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Robust Factorization Methods Using a Gaussian/Uniform Mixture Model
International Journal of Computer Vision
Cayley Transformation and Numerical Stability of Calibration Equation
International Journal of Computer Vision
International Journal of Computer Vision
Perspective 3-D euclidean reconstruction with varying camera parameters
IEEE Transactions on Circuits and Systems for Video Technology
The quasi-perspective model: Geometric properties and 3D reconstruction
Pattern Recognition
Non-rigid metric reconstruction from perspective cameras
Image and Vision Computing
Quasi-perspective structure factorization with missing data
CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 2
Euclidean structure recovery from motion in perspective image sequences via Hankel rank minimization
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Element-wise factorization for N-View projective reconstruction
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Two-View geometry and reconstruction under quasi-perspective projection
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
General and nested wiberg minimization: L2 and maximum likelihood
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VII
Application of heterogenous motion models towards structure recovery from motion
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
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We give the first complete theoretical convergence analysis for the iterative extensions of the Sturm/Triggs algorithm. We show that the simplest extension, SIESTA, converges to nonsense results. Another proposed extension has similar problems, and experiments with “balanced” iterations show that they can fail to converge or become unstable. We present CIESTA, an algorithm which avoids these problems. It is identical to SIESTA except for one simple extra computation. Under weak assumptions, we prove that CIESTA iteratively decreases an error and approaches fixed points. With one more assumption, we prove it converges uniquely. Our results imply that CIESTA gives a reliable way of initializing other algorithms such as bundle adjustment. A descent method such as Gauss–Newton can be used to minimize the CIESTA error, combining quadratic convergence with the advantage of minimizing in the projective depths. Experiments show that CIESTA performs better than other iterations.