Self-calibration algorithms that use only the information in the image have been actively researched. However, most algorithms require bundle adjustment in the projective reconstruction or in the nonlinear minimization. We propose a practical self-calibration algorithm that only requires a linear projective reconstruction. We overcome the sensitivity of the algorithm due to image noises by adding another constraint on the principal point. Also, we propose a variant of linear auto-calibration algorithm which uses the similar assumption of the work of Pollefeys et al. (1998), based on the property of the absolute quadric. Experimental results using real and synthetic images demonstrate the feasibility of the proposed algorithm.

We present an algorithm for the calibration of a camera and the recovery of 3D scene structure up to a scale from image sequences using known angles between lines in the scene. The proposed method computes the intrinsic parameters of the camera using the invariance of angles under the similarity transformation. Specifically, we recover the matrix that is the homography between the projective structure and the Euclidean structure using angles. Since this matrix is a unique one in the given set of image sequences, we can easily deal with the problem of varying intrinsic parameters of the camera. Experimental results on the synthetic and real images demonstrate the feasibility of the proposed algorithm.