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Manhattan world are referred to manmade structures with planar surfaces. In many applications such as robot navigation or mapping it is vital to have a 3D perception of such environments. In this work a novel methodology is presented to perceive slant surfaces implemented on 3D point clouds. Using an enhanced Ncut clustering technique, the point cloud is classified into a number of clusters. To automatically perceive the existence of planes, a series of algorithms are implemented which consist of LS fitting, pruning and RANSAC. Experiments were carried out in MATLAB for both simulated data and real world data, making a total of 100 point clouds. According to the obtained results, the proposed methodolgy was able to successfully extract planar surfaces in each scene.The results confirmed significant improvement of the enhanced Ncut over the original Ncut and Kmeans method.
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