Robust Factorization Methods Using a Gaussian/Uniform Mixture Model
International Journal of Computer Vision
Successively alternate least square for low-rank matrix factorization with bounded missing data
Computer Vision and Image Understanding
A minimal solution for camera calibration using independent pairwise correspondences
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
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In this paper we address the problem of recovering structure and motion from a large number of intrinsically calibrated perspective cameras. We describe a method that combines (1) weak-perspective reconstruction in the presence of noisy and missing data and (2) an algorithm that updates weak-perspective reconstruction to perspective reconstruction by incrementally estimating the projective depths. The method also solves for the reversal ambiguity associated with affine factorization techniques. The method has been successfully applied to the problem of calibrating the external parameters (position and orientation) of several multiple-camera setups. Results obtained with synthetic and experimental data compare favourably with results obtained with nonlinear minimization such as bundle adjustment.