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Elliptic Gabriel graph for finding neighbors in a point set and its application to normal vector estimation

机译:椭圆加百利图在点集中寻找邻居及其在法向矢量估计中的应用

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摘要

Point-based shape representation has received increased attention in recent years, mainly due to its simplicity. One of the most fundamental operations for point set processing is to find the neighbors of each point. Mesh structures and neighborhood graphs are commonly used for this purpose. However, though meshes are very popular in the field of computer graphics, neighbor relations encoded in a mesh are often distorted. Likewise, neighborhood graphs, such as the minimum spanning tree (MST), relative neighborhood graph (RNG), and Gabriel graph (GG), are also imperfect as they usually give too few neighbors for a given point. In this paper, we introduce a generalization of Gabriel graph, named elliptic Gabriel graph (EGG), which takes an elliptic influence region instead of the circular region in GG. In order to determine the appropriate aspect ratio of the elliptic influence region of EGG, this paper also presents the analysis between the aspect ratio of the elliptic influence region and the average valence of the resulting neighborhood. Analytic and empirical test results are included.
机译:基于点的形状表示法近年来由于其简单性而受到越来越多的关注。点集处理的最基本操作之一是找到每个点的邻居。网格结构和邻域图通常用于此目的。但是,尽管网格在计算机图形学领域非常流行,但是网格中编码的邻居关系经常会失真。同样,邻域图(例如最小生成树(MST),相对邻域图(RNG)和加百利图(GG))也不完美,因为它们通常给定点的邻域太少。在本文中,我们介绍了加百利图的广义化,称为椭圆加百利图(EGG),它采用了椭圆影响区域而不是GG中的圆形区域。为了确定EGG的椭圆影响区域的合适长宽比,本文还对椭圆影响区域的长宽比与所得邻域的平均价之间进行了分析。包括分析和经验测试结果。

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