系统仿真学报 ›› 2019, Vol. 31 ›› Issue (2): 218-226.doi: 10.16182/j.issn1004731x.joss.17DEA-010

• 论文 • 上一篇    下一篇

基于局部优化的重心Voronoi图计算

叶畋宇1, 王逸群2, 严冬明2, *, 雍俊海1   

  1. 1. 清华大学软件学院,北京 100084;
    2. 中国科学院自动化研究所模式识别国家重点实验室,北京 100190
  • 收稿日期:2016-10-12 修回日期:2017-01-30 出版日期:2019-02-15 发布日期:2019-02-15
  • 作者简介:叶畋宇(1990-),男,上海,博士生,研究方向为计算机辅助几何设计。
  • 基金资助:
    国家科技支撑计划(2015BAF23B03), 国家国际科技合作专项(2013DFE13120), 国家自然科学基金(61772523, 61272235, 61372168, 61672307)

Centroidal Voronoi Tessellation with Local Optimization

Ye Tianyu1, Wang Yiqun2, Yan Dongming2, *, Yong Junhai1   

  1. 1. School of Software, Tsinghua University, Beijing 100084, China;
    2. NLPR, Insistute of Automation, Chinese Academy of Sciences, Beijing 100190, China
  • Received:2016-10-12 Revised:2017-01-30 Online:2019-02-15 Published:2019-02-15

摘要: 重心Voronoi图(centroidal Voronoi tessellation,CVT)是一个重要的几何结构,在地理信息系统,信号处理,网格生成/优化,可视化等领域有着重要应用。针对传统全局生成、优化的方法的不足,比如奇异点多、收敛速度较慢等问题,提出了生成优化与随机扰动两种局部优化方法,以及一个整合了层次生成、局部优化、蒙特卡罗优化的CVT生成算法框架。实验结果表明,相比于已有算法,本文方法在速度与质量上有综合的提升。

关键词: 重心Voronoi图, 局部极小, 优化, 奇异点

Abstract: Centroidal Voronoi tessellation is a special geometric structure, which has many applications in various fields such as geographical information system, signal processing, mesh generation/optimization, visualization and so on. Due to the highly non-convex nature of the CVT energy function, the existing methods for computing CVT have several drawbacks, which always trap into local minima. We propose generation optimization and stochastic optimization schemes for further reducing the CVT energy. Experimental results show that the proposed method improves both quality and efficiency compared to the recent approaches.

Key words: centroidal Voronoi tessellation, local minimum, optimization, irregular point

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