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A New Approach to Output-Sensitive Voronoi Diagrams and Delaunay Triangulations

机译:输出敏感Voronoi图和Delaunay三角剖分的新方法

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We describe a new algorithm for computing the Voronoi diagram of a set of n points in constant-dimensional Euclidean space. The running time of our algorithm is O(f log n log △) where f is the output complexity of the Voronoi diagram and △ is the spread of the input, the ratio of largest to smallest pairwise distances. Despite the simplicity of the algorithm and its analysis, it improves on the state of the art for all inputs with polynomial spread and near-linear output size. The key idea is to first build the Voronoi diagram of a superset of the input points using ideas from Voronoi refinement mesh generation. Then, the extra points are removed in a straightforward way that allows the total work to be bounded in terms of the output complexity, yielding the output sensitive bound. The removal only involves local flips and is inspired by kinetic data structures.
机译:我们描述了一种新算法,用于计算恒定维欧几里得空间中n个点的Voronoi图。我们的算法的运行时间为O(f log n log△),其中f是Voronoi图的输出复杂度,而△是输入的扩展,即最大对最小距离的比率。尽管该算法及其分析非常简单,但是它对于具有多项式扩展和近线性输出大小的所有输入都改善了现有技术。关键思想是首先使用Voronoi细化网格生成的思想来构建输入点超集的Voronoi图。然后,以直接的方式删除多余的点,从而允许根据输出复杂度限制总功,从而产生输出敏感范围。删除仅涉及局部翻转,并受动力学数据结构的启发。

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