It is well known that projective depth recovery and camera calibration are two essential and difficult steps in the problem of 3D Euclidean structure and motion recovery from video sequences. This chapter presents two new algorithms to improve the performance of perspective factorization. The first one is a hybrid method for projective depths estimation. It initializes the depth scales via a projective structure reconstructed from two views with large camera movement, which are then optimized iteratively by minimizing reprojection residues. The algorithm is more accurate than previous methods and converges quickly. The second one is on camera self-calibration based on Kruppa constraints which can deal with a more general camera model. Then the Euclidean structure is recovered from factorization of the normalized tracking matrix. Extensive experiments on synthetic data and real sequences are performed for validation and comparison.