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In this paper, we propose a new method for reconstructing a scene, from different views through high-distortion lens camera. Unlike most other approaches, no a priori calibrations nor specific test patterns are required. Only several pairs of correspondence between input images are used to estimate intrinsic parameters such as focal length and distortion coefficients. Also, from these correspondences, relative movement of the camera between input images are computed as rotation matrices. We assumed radial lens distortion, modeled with a third order polynomial with two distortion coefficients, which covers highly distorted zoom lenses. Since we allow distortion with two coefficients and focal length to be unknown, it is not easy to get these three parameters explicitly from the correspondence alone. To avoid time consumption and the problem of local minima, we take steps as follows. First, uniform searches in the reduced dimension, Second, fitting a function to get better guess of focal length. Finally, polishing solutions by repeating the uniform search once again to get the final coefficients of distortion. Total number of evaluations is remarkably reduced by this multi-stage optimization. Some experimental results are presented, showing that more than 5% of lens distortion is reduced and the rotation of the camera is recovered, and we show a registration of four outdoor pictures.