Vanishing Point Estimation and Line Classification in a Manhattan World with a Unifying Camera Model

The problem of estimating vanishing points for visual scenes under the Manhattan world assumption has been addressed for more than a decade. Surprisingly, the special characteristic of the Manhattan world that lines should be orthogonal or parallel to each other is seldom well utilized. In this paper, we present an algorithm that accurately and efficiently estimates vanishing points and classifies lines by thoroughly taking advantage of this simple fact in the Manhattan world for images grabbed by a camera with a single effective viewpoint (e.g. perspective camera or central catadioptric camera). The algorithm is also extended to estimate the focal length of the camera when it is uncalibrated. The key novelty is to estimate three orthogonal line directions in the camera frame simultaneously instead of estimating vanishing points in the image plane directly. The performance of the proposed algorithm is demonstrated on four publicly available databases. Compared to the state-of-the-art methods, the experiments show its superiority in terms of both accuracy and efficiency.