Least-Squares Fitting of Two 3-D Point Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Linear and Incremental Acquisition of Invariant Shape Models From Image Sequences
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
3D Reconstruction by Fitting Low-Rank Matrices with Missing Data
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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
Weak-perspective structure from motion for strongly contaminated data
Pattern Recognition Letters
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We address the problem of moving object reconstruction. Several methods have been published in the past 20 years including stereo reconstruction as well as multi-view factorization methods. In general, reconstruction algorithms estimate the 3D structure of the object and the camera parameters in a non-optimal way and then a nonlinear optimization method refines the estimated camera parameters and 3D object coordinates. In this paper, an adjustment method is proposed which is the fast version of the well-known down-hill alternation method. The novelty which yields the high speed of the algorithm is that the steps of the alternation give optimal solution to the subproblems by closed-form formulas. The proposed algorithm is discussed here and it is compared to the widely used bundle adjustment method.