Shape and motion from image streams under orthography: a factorization method
International Journal of Computer Vision
Self-calibration of an affine camera from multiple views
International Journal of Computer Vision
Euclidean Shape and Motion from Multiple Perspective Views by Affine Iterations
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Paraperspective Factorization Method for Shape and Motion Recovery
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
Factorization Methods for Projective Structure and Motion
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
A Closed-Form Solution to Non-Rigid Shape and Motion Recovery
International Journal of Computer Vision
Iterative Extensions of the Sturm/Triggs Algorithm: Convergence and Nonconvergence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rotation constrained power factorization for structure from motion of nonrigid objects
Pattern Recognition Letters
Multiframe Motion Segmentation with Missing Data Using PowerFactorization and GPCA
International Journal of Computer Vision
Nonrigid Structure-from-Motion: Estimating Shape and Motion with Hierarchical Priors
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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The paper focuses on the problem of structure and motion recovery from a monocular image sequence under quasi-perspective projection model. Previous study on this problem adopts singular value decomposition (SVD) to the tracking matrix with rank constraint. The method is time consuming and does not work for incomplete data. In this paper, we propose to adopt power factorization to the problem. The proposed algorithm overcomes the limitations of previous SVD-based counterpart. It is easy to implement and can deal with missing data in the tracking matrix. The algorithm can also be applied to nonrigid factorization. Extensive tests on synthetic and real images validate the proposed method and show its improvements over existing solutions.