3D data segmentation using a non-parametric density estimation approach

Abstract

In this paper, a new segmentation approach for sets of 3D unorganized points is proposed. The method is based on a clustering procedure that separates the modes of a non-parametric multimodal density, following the mean-shift paradigm. The main idea consists in projecting the source 3D data into a set of independent sub-spaces, forming a joint multidimensional space. Each sub-space describes a geometric aspect of the data set, such as the normals and principal curvatures, so as a dense region in a particular sub-space indicates a set of 3D points sharing a similar value of that feature. A non-parametric clustering method is applied in this joint space by using a multidimensional kernel. This kernel smoothly takes into account for all the subspaces, moving towards high density regions in the joint space, separating them and providing "natural" clusters of 3D points. The algorithm can be implemented very easily and only few parameters need to be freely tuned. Experiments are reported, both on synthetic and real data, assessing the quality of the proposed approach and promoting further developments.