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中文摘要：
识别与提取模型几何特征在几何模型的编辑处理中起着重要作用,然而大多数已有算法在处理质量较差的三角网格模型时往往会失效,为此提出一种基于张量投票理论的特征边提取算法.首先根据张量投票矩阵特征值分布与顶点几何特征之间的对应关系对顶点进行分类;采用断点连接方法来保证顶点分类过程中能够正确地区分平滑特征上的边点及角点;根据顶点的类别结果进行区域增长,并提取区域增长后的边界,从而得到网格特征边.实验结果表明,文中算法对大多数模型可靠有效,能够处理网格分布不均匀,以及含有狭长三角形或含有孔、缝的模型,处理有噪声的模型也能达到较好的效果.
英文摘要：
Feature detection and extraction plays important role in mesh editing.However,most existing algorithms often fail in dealing with irregular meshes.To overcome those problems,an algorithm for extracting feature edges of triangle meshes based on tensor voting is presented.First,all vertices of an input mesh are classified according to the observation that there is a close correspondence between the eigenvalue distribution of the tensor voting matrix and geometrical features.The classified vertices are then optimized by connecting breakpoints.Region growing is performed for each seed triangle and the boundaries of the regions are extracted as the edges.The experimental results show that the proposed algorithm is effective in nearly all cases,including models with non-uniformly distributed triangles,long and narrow triangles or even holes.It is also robust on noisy data.
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