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poisson disk sampling through disk packing

Computational Visual Media 2015年第1期1卷 17-26页

作者： Guanghui Liang Lin Lu Zhonggui Chen Chenglei Yang School of Computer Science and Technology Shandong University Department of Computer Science Xiamen University

poisson disk sampling is an important problem in computer graphics and has a wide variety of applications in imaging, geometry, rendering, etc. In this paper, we propose a novel poisson disk sampling algorithm based on disk packing. The key idea uses the observation that a relatively dense disk packing layout naturally satisfies the poisson disk distribution property that each point is no closer to the others than a specified minimum distance, i.e., the poisson disk radius. We use this property to propose a relaxation algorithm that achieves a good balance between the random and uniform properties needed for poisson disk distributions. Our algorithm is easily adapted to image stippling by extending identical disk packing to unequal disks. Experimental results demonstrate the efficacy of our approaches.

关键词： disk packing image stippling poisson disk sampling power diagram

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Approximate poisson disk sampling on mesh

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Science China(Information Sciences) 2013年第9期56卷 194-205页

作者： GENG Bo ZHANG HuiJuan WANG Heng WANG GuoPing Graphics and Interactive Technology Lab of Dept. of Computer Science Peking University The Key Lab of Machine perception and intelligent MOE

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 disk sampling feature preserving sampling adaptive sampling remeshing tensor voting

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