Perspective 3-D Euclidean Reconstruction With Varying Camera Parameters

The paper addresses the problem of 3-D Euclidean structure and motion recovery from video sequences based on perspective factorization. It is well known that projective depth recovery and camera calibration are two essential and difficult steps in metric reconstruction. We focus on the difficulties and propose two new algorithms 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, and optimize the depths iteratively by minimizing reprojection residues. The algorithm is more accurate than previous methods and converges quickly. Second, we propose a self-calibration method based on the Kruppa constraint to deal with more general camera model. The Euclidean structure can be recovered from factorization of the normalized tracking matrix. Extensive experiments on synthetic data and real sequences are performed to validate the proposed method and good improvements are observed.