Segmentation of Scanned Mesh into Analytic Surfaces Based on Robust Curvature Estimation and Region Growing

For effective application of laser or X-ray CT scanned mesh models in design, analysis, and inspection etc, it is preferable that they are segmented into desirable regions as a pre-processing. Engineering parts are commonly covered with analytic surfaces, such as planes, cylinders, spheres, cones, and tori. Therefore, the portions of the part’s boundary where each can be represented by a type of analytic surface have to be extracted as regions from the mesh model. In this paper, we propose a new mesh segmentation method for this purpose. We use the mesh curvature estimation with sharp edge recognition, and the non-iterative region growing to extract the regions. The proposed mesh curvature estimation is robust for measurement noise. Moreover, our proposed region growing enables to find more accurate boundaries of underlying surfaces, and to classify extracted analytic surfaces into higher-level classes of surfaces: fillet surface, linear extrusion surface and surface of revolution than those in the existing methods.