知识驱动的三角网格模型分割

Knowledge driven triangular mesh segmentation

摘要:

为了解决基于特征的CAD模型重建中三角网格模型分割难以体现意义的问题,构建基本造型特征和加工特征组成的知识库来进行有意义的分割．首先以二次曲面的拟合误差和曲率作为分割准则,逐次地分割出对应曲面类型的子网格,从曲面这一基本造型元素的角度初步体现分割的意义．然后分别构建知识库中特征和初步分割的曲面集合的以面为节点的属性邻接图．最后以知识库中的特征的属性邻接图作为输入,匹配出模型中含有的对应特征类型的子网格,从工程语义的角度进一步体现分割的意义．实验结果表明该算法获得了有意义的分割结果,并且具有较高的时间效率．

Abstract:

The segmentation of triangular mesh model in CAD model reconstruction cannot embody significance. To solve this problem, the knowledge base composed by basic modeling features and machining features was constructed to provide prior knowledge for segmentation. Firstly, depending on the fitting error of quadric surface and curvature, every patch of the model corresponding to the surface was extracted successively and the significance of segmentation was reflected by the primitive design element of surface. Then, both features in the knowledge base and the surface set of preliminary segmentation were represented by the attributed adjacency graph (AAG). Finally, the AAG of the features were applied to match the parts of the model which had the isomorphic AAG. This embodied more meaning from the view of engineering semantics. The experimental results demonstrate that the proposed efficient algorithm can get meaningful segmentation.