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It is based on three coupled Markov Random Fields that model smoothness, depth discontinuities and occlusions respectively and produces good result. The biggest problem of global method is that the data term and the smoothness term represent two processes competing against each other, resulting in incorrect matches in areas of weak texture and areas where prior model is violated. Although numerous methods exist for stereo matching, they are designed towards ordinary stereo vision purpose. The images acquired by our system (Fig.

Random sampling: Repeat the following three steps K times (in our experiments K=100) 1. Draw a random subsample of 3 different 3D points from ζ. 2. For this subsample, indexed by k (k = 1, .... , K), compute the plane parameters (a,b,c). 3. For this solution (a,b,c)k, compute the number of inliers among the entire set of 3D points contained in ζ, as mentioned above using ±5cm as a fixed threshold value. Solution: 1. Choose the solution that has the highest number of inliers. Let (a,b,c)i, be this solution.

Laurgeau, C. (2002). Vehicle yaw, pitch, roll and 3D lane shape recovery by vision, Proceedings of IEEE Intelligent Vehicles Symposium, pp. 619–625, Versailles, France, June 2002. Faugeras, O. & Luong, Q. (2001). The Geometry of Multiple Images: the Laws that Govern the Formation of Multiple Images of a Scene and Some of their Applications, MIT, 2001. Fischler M. & Bolles, R. (1981). Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography, Graphics and Image Processing, vol.

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