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.