3-D structure recovery from 2-D observations

In this paper we present a novel method for simultaneously determining three dimensional motion and structure of a non-rigid object from its uncalibrated two dimensional data with Gaussian or non-Gaussian distributions. A non-rigid motion can be treated as a combination of a rigid component and a non-rigid deformation. To reduce the high dimensionality of the deformable structure or shape, we estimate the probability distribution function of the structure through random sampling, integrating an established probabilistic model. The fitting between the observations and the estimated 3-D structure is evaluated using the pooled variance estimator. Applications of the proposed method to both synthetic and real image sequences show promising results.

[1]  Edmond Boyer,et al.  Camera calibration and 3D reconstruction from single images using parallelepipeds , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[2]  Aaron Hertzmann,et al.  Learning Non-Rigid 3D Shape from 2D Motion , 2003, NIPS.

[3]  Zhiwei Zhu,et al.  A Real-Time Human Stress Monitoring System Using Dynamic Bayesian Network , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[4]  Henning Biermann,et al.  Recovering non-rigid 3D shape from image streams , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[5]  T. Kanade,et al.  Non-rigid shape and motion recovery: degenerate deformations , 2004, CVPR 2004.

[6]  E. Vonesh,et al.  Efficient inference for random-coefficient growth curve models with unbalanced data. , 1987, Biometrics.

[7]  Takeo Kanade,et al.  Shape and motion from image streams under orthography: a factorization method , 1992, International Journal of Computer Vision.

[8]  Reinhard Koch,et al.  Self-Calibration and Metric Reconstruction Inspite of Varying and Unknown Intrinsic Camera Parameters , 1999, International Journal of Computer Vision.

[9]  Geoffrey E. Hinton,et al.  The EM algorithm for mixtures of factor analyzers , 1996 .

[10]  Adrien Bartoli,et al.  A Batch Algorithm for Implicit Non-rigid Shape and Motion Recovery , 2006, WDV.

[11]  Alessio Del Bue,et al.  Non-Rigid Metric Shape and Motion Recovery from Uncalibrated Images Using Priors , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[12]  Michael Isard,et al.  CONDENSATION—Conditional Density Propagation for Visual Tracking , 1998, International Journal of Computer Vision.

[13]  Lorenzo Torresani,et al.  Tracking and modeling non-rigid objects with rank constraints , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.