“Measurement Matrix Interpolation Based on Projective Reconstruction for Factorization Method”

The factorization method by Tomasi and Kanade simultaneously recovers camera motion and object shape from an image sequence. This method is robust because the solution is linear by assuming the orthographic camera model. However, the only feature points that are tracked throughout the image sequence can be reconstructed, it is difficult to recover whole object shape by the factorization method. In this paper, we propose a new method to interpolate feature tracking so that even the loci of unseen feature points can be used as inputs of the factorization for object shape reconstruction. In this method, we employ projective reconstruction to interpolate un-tracked feature points. All loci of all detected feature points throughout the input image sequence provide correct reconstructed shape of the object via the factorization. The results of reconstruction are evaluated by the experiment using synthetic images and real images.