三角网格模型的最小值边界分割

摘要

针对目前网格模型块分割算法综合效果不理想、人工干预多等问题,提出一种基于凹凸信号的最小值边界检测的三角网格模型分割算法.首先通过全局控制顶点的Laplace光顺操作对网格模型进行光顺去噪;然后通过标准化和归一化的凹度信息发现符合人眼视觉的最小值规则的凹特征点;最后结合区域中心线提取算法以及扇形探射线算法构造出闭合的分割线,并用三维主动轮廓模型方法进行优化,通过分割线将模型分割为有意义的分块.实例结果表明,该算法可以快速有效地分割模型,得到有意义的分割结果.%The existing mesh segmentation algorithms often require manual intervention or large computational load, and are generally found to be very sensitive to the model shape variations. To tackle these problems, this paper concentrates on the concave vertex investigation and presents an efficient Minima Rule Boundary Detection approach for robust mesh segmentation. First, we smooth the complex mesh model with vertex tolerance con-straints to reduce the noisy impact, and then detect the Minima Rule concave vertexes by calculating the normal-ized convex concave signal of each vertex. Accordingly, the reasonable boundaries can be well constructed by linking the concave vertex with Skeletonizing algorithm and Dijkstra algorithm. Finally, these boundaries are smoothed and refined by means of the three-dimensional snakes, through which the semantic blocks within the mesh model can be efficiently segmented. The extensive experiments have demonstrated that the proposed algo-rithm is able to produce meaningful results rapidly and effectively.