robust structure and motion factorization of nonrigid objects

Structure from motion is an import theme in computer vision. Although great progress has been made both in theory and applications, most of the algorithms only work for static scenes and rigid objects. In recent years, structure and motion recovery of nonrigid objects and dynamic scenes has received a lot of attention. In this paper, the state-of-the-art techniques for structure and motion factorization of nonrigid objects are reviewed and discussed. First, an introduction of the structure from motion problem is presented, followed by a general formulation of nonrigid structure from motion. Then, an augmented affined factorization framework, by using homogeneous representation, is presented to solve the registration issue in the presence of outlying and missing data. Third, based on the observation that the reprojection residuals of outliers are significantly larger than those of inliers, a robust factorization strategy with outlier rejection is proposed by means of the reprojection residuals, followed by some comparative experimental evaluations. Finally, some future research topics in nonrigid structure from motion are discussed.