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Camera calibration is one of the indispensable processes to obtain 3D depth information from 2D images in the field of computer vision. Camera self-calibration is more convenient and flexible, especially in the application of large depth of fields, wide fields of view, and scene conversion, as well as other occasions like zooms. In this paper, two selfcalibration methods respectively based on two vanishing points and homography are studied, and finally realizing the image mosaic based on self-calibration of the camera purely rotating around optical center. The geometric characteristic of disappear points formed by two groups of orthogonal parallel lines is applied to self-calibration based on two vanishing points. By using the vectors’ orthogonal properties of connection optical centers and the vanishing points, the constraint equations on the camera intrinsic parameters are established. By this method, four internal parameters of the camera can be solved though only four images taked from different viewpoints in a scene. Compared with the other selfcalibration based on homography, the method based on two vanishing points has more convenient calibration process and simple algorithm. To check the quality of the self-calibration, we create a spherical mosaic of the images that were used for the self-calibration based on homography. Compared with the experimental results of two methods respectively based on calibration plate and self-calibration method using machine vision software Halcon, the practicability and effectiveness of self-calibration respectively based on two vanishing points and homography is verified.
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