2-Line Exhaustive Searching for Real-Time Vanishing Point Estimation in Manhattan World

This paper presents a very simple and efficient algorithm to estimate 1, 2 or 3 orthogonal vanishing point(s) on a calibrated image in Manhattan world. Unlike the traditional methods which apply 1, 3, 4, or 6 line(s) to generate vanishing point hypotheses, we propose to use 2 lines to get the first vanishing point v1, then uniformly take sample of the second vanishing point v2 on the great circle of v1 on the equivalent sphere, and finally calculate the third vanishing point v3 by the cross-product of v1 and v2. There are three advantages of the proposed method over traditional multi-line method. First, the 2-line model is much more robust and reliable than the multi-line method, which can be applied in the scene with 1, 2 or 3 orthogonal vanishing point(s). Second, the probability of the 2-line model being formed of inner line segments can be calculated given the outlier ratio, which means that the number of iterations can be determined, and thus the estimation of vanishing points can be performed in a very simple exhaustive way instead of the traditional RANSAC method. Third, the real-time performance is achieved by building a polar grid for the line intersection points, which functions as a lookup table for the validation of vanishing point hypotheses. Our algorithm has been validated successfully in the YUD dataset and sets of challenging real images.