Face clustering of a large-scale CAD model for surface mesh generation

Abstract

A detailed CAD model needs manual clean-up, or simplifying operations, before a finite element mesh can be automatically generated because such a model consists of hundreds or thousands of faces many of which may be smaller than a desired mesh element size. We propose an automated face clustering method used as a pre-process of surface mesh generation. By decomposing a model into face clusters so that each region can be projected onto a simple parametric surface such as a plane, we obtain a final mesh as an aggregation of sub-meshes for respective clusters without time-consuming manual preparation work. The projection onto a surface realises re-parametrization as well as suppression of small details. The main contribution of this work is the integration of: (1) a greedy algorithm for combining faces into clusters, and (2) 2 geometric indices that reflect various geometric aspects of a preferable shape for a cluster. The validity of the approach is demonstrated with results of clustering and mesh generation for a realscale CAD model.

Consistent segmentation of 3D models

“…They use techniques such as K-means [30], graph cuts [12], hierarchical clustering [7,8,11], random walks [16], core extraction [13], tubular primitive extraction [21], spectral clustering [19], and critical point analysis [18]. While some research tries to use segmentation criteria that are consistent between meshes in a class (such as shape diameter function [29]) or between articulated versions of a model (such as diffusion distance [5]), it is difficult in general to run segmentation methods independently on a set of meshes and obtain results with corresponding segments ( Figure 1a).…”

3D Mesh Segmentation Methodologies for CAD applications

“…al. [16] use a clustering algorithm similar to Garland [10] which is used to simplify the CAD model structure. The edge contraction cost is based on the following criteria : (i) area size -The area of a region should be large enough with respect to a mesh element area; (ii) border smoothness -The boundary of a region should be as smooth as possible; (iii) region flatness -Each region should be as flat as possible.…”

Interactive Multiresolution Visualization of 3D Mesh

“…Thirdly, there are approaches corresponding to the hierarchical partitioning among the existing work in this area [28], [29] and [30]. Finally, in the iterative partitioning, the research of optimal segmentation can be approximated by the iterative search for the best partition from a fixed number of clusters like as [31] and [32].…”