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Maximum Likelihood Inference of 3D Structure from Image Sequences

Summary: Maximum Likelihood Inference
of 3D Structure from Image Sequences
Pedro M. Q. Aguiar and Jos´e M. F. Moura
Carnegie Mellon University
Abstract. The paper presents a new approach to recovering the 3D
rigid shape of rigid objects from a 2D image sequence. The method has
two distinguishing features: it exploits the rigidity of the object over the
sequence of images, rather than over a pair of images; and, it estimates
the 3D structure directly from the image intensity values, avoiding the
common intermediate step of first estimating the motion induced on the
image plane. The approach constructs the maximum likelihood (ML)
estimate of all the shape and motion unknowns. We do not attempt the
minimization of the ML energy function with respect to the entire set
of unknown parameters. Rather, we start by computing the 3D motion
parameters by using a robust factorization appraoch. Then, we refine the
estimate of the object shape along the image sequence, by minimizing the
ML-based energy function by a continuation-type method. Experimental
results illustrate the performance of the method.
1 Introduction


Source: Aguiar, Pedro M. Q. - Institute for Systems and Robotics (Lisbon)


Collections: Engineering