International Journal of Computer Vision - 1998 Marr Prize
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Autocalibration and the absolute quadric
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
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
Uncalibrated Perspective Reconstruction of Deformable Structures
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
A Closed-Form Solution to Non-Rigid Shape and Motion Recovery
International Journal of Computer Vision
Implicit Non-Rigid Structure-from-Motion with Priors
Journal of Mathematical Imaging and Vision
Nonrigid Structure-from-Motion: Estimating Shape and Motion with Hierarchical Priors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Perspective Nonrigid Shape and Motion Recovery
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
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Rigid structure-from-motion (SfM) usually consists of two steps: First, a projective reconstruction is computed which is then upgraded to Euclidean structure and motion in a subsequent step. Reliable algorithms exist for both problems. In the case of non-rigid SfM, on the other hand, especially the Euclidean upgrading has turned out to be difficult. A few algorithms have been proposed for upgrading an affine reconstruction, and are able to obtain successful 3D-reconstructions. For upgrading a non-rigid projective reconstruction, however, either simple sequences are used, or no 3D-reconstructions are shown at all. In this article, an algorithm is proposed for estimating the self-calibration of a projectively reconstructed non-rigid scene. In contrast to other algorithms, neither prior knowledge of the non-rigid deformations is required, nor a subsequent step to align different motion bases. An evaluation with synthetic data reveals that the proposed algorithm is robust to noise and it is able to accurately estimate the 3D-reconstructions and the intrinsic calibration. Finally, reconstructions of a challenging real image with strong non-rigid deformation are presented.