10264

PixelPie: Maximal Poisson-disk Sampling with Rasterization

Cheuk Yiu Ip, M. Adil Yalcin, David Luebke, Amitabh Varshney
University of Maryland, College Park
5th High-Performance Graphics Conference (HPG ’13), 2013

@inproceedings{ip2013pixelpie,

   title={PixelPie: maximal Poisson-disk sampling with rasterization},

   author={Ip, Cheuk Yiu and Yal{c{c}}in, M Adil and Luebke, David and Varshney, Amitabh},

   booktitle={Proceedings of the 5th High-Performance Graphics Conference},

   pages={17–26},

   year={2013},

   organization={ACM}

}

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We present PixelPie, a highly parallel geometric formulation of the Poisson-disk sampling problem on the graphics pipeline. Traditionally, generating a distribution by throwing darts and removing conflicts has been viewed as an inherently sequential process. In this paper, we present an efficient Poisson-disk sampling algorithm that uses rasterization in a highly parallel manner. Our technique is an iterative two step process. The first step of each iteration involves rasterization of random darts at varying depths. The second step involves culling conflicted darts. Successive iterations identify and fill in the empty regions to obtain maximal distributions. Our approach maps well to the parallel and optimized graphics functions on the GPU and can be easily extended to perform importance sampling. Our implementation can generate Poisson-disk samples at the rate of nearly 7 million samples per second on a GeForce GTX 580 and is significantly faster than the state-of-the-art maximal Poisson-disk sampling techniques.
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