Anisotropic segmentation of unorganized sets of points

In this paper, we present a general framework for the segmentation of surfaces represented by 3D scattered data. The method we present is based on an anisotropic diffusion scheme. Contextual information at each data point involves the selection of optimal directions locally representing the shape. Graph based representations are well adapted to embody this kind of knowledge. Thus, we introduce two structures respectively denoted minimal and maximal escarpment trees. Then, an intrinsic segmentation of the data is operated by label diffusion over these structures. It proceeds in two important stages. The first stage consists in the extraction of atomic regions which are to be combined in the second merging stage. Novel aspect of our method is its ability to detect arbitrary topological types of features, as crease edge or boundaries between two smooth regions. This method has proven to be effective, as demonstrated below on both synthetic and real data.© (1998) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.