Shape and motion from image streams under orthography: a factorization method
International Journal of Computer Vision
Affine analysis of image sequences
Affine analysis of image sequences
A Paraperspective Factorization Method for Shape and Motion Recovery
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Ambiguities in Structure From Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Sequential Factorization Method for Recovering Shape and Motion From Image Streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
Linear fitting with missing data for structure-from-motion
Computer Vision and Image Understanding
Error characterization of the factorization method
Computer Vision and Image Understanding
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
A Factorization Method for Affine Structure from Line Correspondences
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
A Unified Factorization Algorithm for Points, Line Segments and Planes with Uncertainty Models
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Rank 1 Weighted Factorization for 3D Structure Recovery: Algorithms and Performance Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Three-dimensional modeling from two-dimensional video
IEEE Transactions on Image Processing
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This paper studies the geometrical recovery of an incomplete observation matrix for converting existing 2D video sequences to 3D content. In situations when converting previously recorded monoscopic video to 3D, several entries of the observation matrix have not been observed and other entries have been perturbed by the influence of noise. In such cases, there is no simple solution for SVD factorization for shape from motion. In this paper, a new recovery algorithm is proposed for recovering missing feature points, by minimizing the influence of noise, using iteratively geometrical correlations between a 2D observation matrix and 3D shape. Results in practical situations demonstrated with synthetic and real video sequences verify the efficiency and flexibility of the proposed method.