Structure and motion factorization under quasi-perspective projection with missing data in tracking matrix

The paper is focused on the problem of structure and motion factorization from uncalibrated image sequences. Based on our early study on quasi-perspective projection, we give an analysis on the imaging errors of different projection models and propose to adopt power factorization algorithm to deal with missing data problem. The main contribution lies in two aspects. First, we carry out an error analysis of the quasi-perspective projection and prove that it is more accurate than affine model under small camera movements. Second, we propose to utilize power factorization to factorize the tracking matrix. Compared with SVD-based method, the algorithm can work with incomplete tracking data, and it is computationally cheaper than other methods. The proposed method is evaluated on synthetic and real image sequences and better results are observed.