Automatic segmentation of unorganized noisy point clouds based on the Gaussian map

A nonparametric clustering algorithm, called cell mean shift (CMS), is developed to extract clusters of a set of points on the Gaussian sphere S2. It is computationally more efficient than the traditional mean shift (MS). Based on the singular value decomposition, the dimensional analysis is introduced to classify these clusters into point-, curve-, and area-form clusters. Each cluster is the Gaussian image of a set of points which will be examined by a connected search in R3. An orientation analysis of the Gaussian map to area-form clusters is applied to identify hyperbolic and elliptical regions. A signed point-to-plane distance function is used to identify points of convex and concave regions. Segmentation results of several real as well as synthetic point clouds, together with complexity analyses, are presented.