Euclidean structure and motion from uncalibrated image sequences under perspective projection

The paper addresses the problem of factorization based structure and motion recovery under perspective projection. Two new algorithms are proposed to improve the performance of perspective factorization. First, we propose to initialize the projective depths via a projective structure reconstructed from two views with large camera movement, then optimize the depths iteratively by minimizing reprojection residues. The algorithm is more accurate and converges quickly. Second, we propose a self-calibration method based on Kruppa constraints to deal with more general camera model. The Euclidean structure is then recovered from factorization of the normalized tracking matrix. Extensive experiments on synthetic data and real sequences show improvements of the proposed method.