Shape and motion from image streams under orthography: a factorization method
International Journal of Computer Vision
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International Journal of Computer Vision - Special issue: image understanding research at the University of Maryland
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
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International Journal of Computer Vision
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple view geometry in computer vision
Multiple view geometry in computer vision
Factorization Methods for Projective Structure and Motion
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Recursive Factorization Method for the Paraperspective Model Based on the Perspective Projection
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
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ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
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CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 1 - Volume 01
A Direct Method for 3D Factorization of Nonrigid Motion Observed in 2D
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Non-rigid metric reconstruction from perspective cameras
Image and Vision Computing
Reconstruction of non-rigid 3D shapes from stereo-motion
Pattern Recognition Letters
Deformation weight constraint and 3D reconstruction of nonrigid objects
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
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The paper focuses on the problem of structure and motion of nonrigid object from image sequence under perspective projection. Many previous methods on this problem utilize the extension technique of SVD factorization based on rank constraint to the tracking matrix, where the 3D shape of nonrigid object is expressed as a weighted combination of a set of shape bases. All these solutions are based on the assumption of Affine camera model. This assumption will become invalid and cause large reconstruction errors when the object is close to the camera. In this paper, we propose two algorithms, namely the linear recursive estimation and the nonlinear optimization, to extend these methods to general perspective camera model. Both algorithms are based on the shape and motion of weak perspective projection. The former one updates the solutions from weak perspective to perspective projection by refining the scalars corresponding to the projective depths recursively. The latter one is based on nonlinear optimization by minimizing the perspective reprojection residuals. Extensive experiments on simulated data and real image sequences are performed to validate the effectiveness of our new algorithms and noticeable improvements over the previous solutions are observed.