Linear fitting with missing data for structure-from-motion
Computer Vision and Image Understanding
Incremental Singular Value Decomposition of Uncertain Data with Missing Values
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Shape and Motion from Image Streams: a Factorization Method Parts 2,8,10 Full Report on the Orthographic Case
An Invitation to 3-D Vision: From Images to Geometric Models
An Invitation to 3-D Vision: From Images to Geometric Models
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
Recovering the missing components in a large noisy low-rank matrix: application to SFM
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Iterative Multiresolution Scheme for SFM with Missing Data
Journal of Mathematical Imaging and Vision
An iterative multiresolution scheme for SFM with missing data: Single and multiple object scenes
Image and Vision Computing
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Several factorization techniques have been proposed for tackling the Structure from Motion problem. Most of them provide a good solution, while the amount of missing and noisy data is within an acceptable ratio. Focussing on this problem, we propose to use an incremenal multiresolution scheme, with classical factorization techniques. Information recovered following a coarse-to-fine strategy is used for both, filling in the missing entries of the input matrix and denoising original data. An evaluation study, by using two different factorization techniques–the Alternation and the Damped Newton–is presented for both synthetic data and real video sequences.