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Secondary features segmentation from high-density tessellated surfaces

机译:高密度曲面表面的次要特征分割

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摘要

A new method for secondary features segmentation, performed on high-density tessellated geometric models, is proposed. Four types of secondary features are considered: fillets, rounds and grooves. Sharp edges are also recognised. The method is based on an algorithm that analyses the principal curvatures. The nodes, potentially attributable to a fillet of given geometry, are those with a certain value for the maximum principal curvature. Since the deterministic application of this simple working principle shows several problems, due to the uncertainties in the curvature estimation, a fuzzy approach is proposed. In order to segment the nodes of a tessellated model belonging to secondary features of a given radius, an appropriate set of membership functions is defined and evaluated based on some parameters, which affect the quality of the curvature estimation. A region-growing algorithm connects the nodes pertaining to a same secondary feature so that, for a given radius, one or more secondary features may be recognized. The method is applied and verified in some test cases.
机译:提出了一种关于高密度曲面细分的几何模型执行的次要特征分割方法。考虑了四种类型的次要特征:鱼片,圆形和凹槽。还认识到锋利的边缘。该方法基于分析主曲率的算法。潜在地归因于给定几何形状的圆角的节点是具有一定值的最大主曲率的那些。由于这种简单工作原理的确定性应用显示了几个问题,因此由于曲率估计的不确定性,提出了一种模糊方法。为了将属于给定半径的次要特征的曲折化模型的节点进行分割,基于一些影响曲率估计的质量来定义和评估一组适当的隶属函数。区域生长算法将有关相同的辅助特征的节点连接,使得对于给定的半径,可以识别一个或多个辅助特征。在一些测试用例中应用该方法并验证。

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