摘要
This paper presents a practical iterative algorithm for two-view metric reconstruction without any prior knowledge about the scene and motion in a nonsingular geometry configuration. The principal point is assumed to locate at the image center with zero skew and the same aspect ratio, and the interior parameters are fixed, so the self-calibration becomes focal-length cali- bration. Existing focal length calibration methods are direct solutions of a quadric composed of fundamental matrix, which are sensitive to noise. A quaternion-based linear iterative Least-Square Method is proposed in this paper, and one-dimensional searching for optimal focal length in a constrained region instead of solving optimization problems with inequality constraints is applied to simplify the computation complexity, then unique rotational matrix and translate vector are recovered. Experiments with simulation data and real images are given to verify the algorithm.
This paper presents a practical iterative algorithm for two-view metric reconstruction without any prior knowledge about the scene and motion in a nonsingular geometry configuration. The principal point is assumed to locate at the image center with zero skew and the same aspect ratio, and the interior parameters are fixed, so the self-calibration becomes focal-length calibration. Existing focal length calibration methods are direct solutions of a quadric composed of fundamental matrix, which are sensitive to noise. A quaternion-based linear iterative Least-Square Method is proposed in this paper, and one-dimensional searching for optimal focal length in a constrained region instead of solving optimization problems with inequality constraints is applied to simplify the computation complexity, then unique rotational matrix and translate vector are recovered. Experiments with simulation data and real images are given to verify the algorithm.
