Shape recovery from an unorganized image sequence

We propose a method for recovering a 3D object from an unorganized image sequence, in which the order of the images and the corresponding points among the images are unknown, using a random sampling and voting process. Least squares methods such that the factorization method and the 8-point algorithm are not directly applicable to an unorganized image sequence, because the corresponding points are a priori unknown. The proposed method repeatedly generates relevant shape parameters from randomly sampled data as a series of hypotheses, and finally produces the solutions supported by a large number of the hypotheses. The method is demonstrated on synthetic and real data.