离散曲面的近似Poisson盘采样  

Approximate Poisson disk sampling on mesh

作　　者：耿博[1] 张慧娟[1] 王衡[1] 汪国平[1,2] 

机构地区：[1]北京大学计算机系图形与交互技术实验室,北京100871 [2]北京大学机械感知与智能教育部重点实验室,北京100871

出　　处：《中国科学：信息科学》2012年第6期703-716,共14页Scientia Sinica(Informationis)

基　　金：国家重点基础研究发展规划项目(批准号:2010CB328002);国家自然科学基金(批准号:90915010;60925007;60833007)资助项目

摘　　要：Poisson盘采样作为计算机图形学的一个重要课题,在重网格化、过程纹理、物体分布、光照计算等方面都有重要应用.虽然最近几年对于2维平面Poisson盘采样的研究比较密集,但是直接对于2维流形表面上的Poisson盘采样的研究却比较少.在本文中,我们提出了一种可以直接在Mesh表面生成近似Poisson盘分布的方法.此方法实现简单,同时可以通过简单修改适用于保特征的采样和自适应采样.文中引入了张量投票的方法来实现特征识别和自适应采样半径的计算,并给出了采样后的重网格化结果,作为此算法的一个后期应用.通过大量实例表明,本文方法快速、鲁棒、适用广泛.Poisson disk sampling has been widely used in many applications such as remeshing, procedural texturing, object distribution, illumination, etc. While 2D Poisson disk sampling is intensively studied in recent years, direct Poisson disk sampling on 2-manifold surface is rarely covered. In this paper, we present a novel framework which generates approximate Poisson disk distribution directly on mesh, a discrete representation of 2-manifold surfaces. Our framework is easy to implement and provides extra flexibility to specified sampling issues like feature-preserving sampling and adaptive sampling. We integrate the tensor voting method into feature detection and adaptive sample radius calculation. Remeshing as a special downstream application is also addressed. According to our experiment results, our framework is efficient, robust, and widely applicable.

关 键 词：Poisson盘采样 保特征采样 自适应采样 重网格化 张量投票 网格生成 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]