Abstract
The problem of estimating vanishing points for visual scenes under the Manhattan world assumption [1, 2] 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 with a calibrated camera. We first present a one-unknown-parameter representation of the 3D line direction in the camera frame. Then derive a quadratic which is employed to solve three orthogonal vanishing points formed by a line triplet. Finally, we develop a RANSAC-based approach to fulfill the task. The performance of proposed approach is demonstrated on the York Urban Database[3] and compared to the state-of-the-art method.
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Zhang, L., Koch, R. (2013). Vanishing Points Estimation and Line Classification in a Manhattan World. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds) Computer Vision – ACCV 2012. ACCV 2012. Lecture Notes in Computer Science, vol 7725. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37444-9_4
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DOI: https://doi.org/10.1007/978-3-642-37444-9_4
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