离散曲率约束的三角网格模型拓扑分割算法

Discrete Curvature Constrained Triangle Mesh Model Segmenting Technique

针对三角形网格模型简化中保持细节特征的要求,提出了依据离散曲率划分三角网格顶点的特性,并结合区域增长法自适应地确定拓扑分支的优化算法.每个顶点根据其相邻面片顶点的曲率值划分属性区域,并在区域生长过程中重复选择K-ring碟形区域中具有相似属性值的顶点作为种子.为了有效地探索凸凹形状区域,以曲率极值点作为初始点,提出了有效的区域增长及合并的策略,突出了模型的局部特征和拓扑结构.最后通过一系列实验验证了该算法的快捷性.

Aiming at the requirement of keeping the detail features during the simplification of triangle mesh models, we proposed an optimized algorithm, which classified the vertex attribute based on discrete curvature estimation, and it then combined the region growing method to adaptively determine the topological structure of the 3D models. We organized all the vertices by attributes, and for each region we applied adaptive K-ring disk searching method to iteratively choose the similar attribute as a new seed. Our method started from curvature extrema points and searched concave and convex topological area by using effective region growing methods; it thus addressed models＇ features and topological structures. In the end a series of experiments show its efficiency.