Paper Info
Title
PixelPie: maximal Poisson-disk sampling with rasterization
Abstract
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.
Year
DOI
Venue
2013
10.1145/2492045.2492047
High Performance Graphics
Keywords
Field
DocType
importance sampling,poisson-disk sampling problem,maximal poisson-disk sampling,poisson-disk sample,parallel manner,optimized graphics function,state-of-the-art maximal poisson-disk,graphics pipeline,efficient poisson-disk sampling algorithm,maximal distribution,step process,gpgpu
Graphics,Importance sampling,Graphics pipeline,Computer science,CUDA,Parallel computing,Sampling (statistics),General-purpose computing on graphics processing units,Poisson distribution,OpenGL
Conference
Citations 
PageRank 
References 
5
0.43
36
Authors
4
Name
Order
Citations
PageRank
Cheuk Yiu Ip11769.96
M. Adil Yalçin2252.71
David Luebke32196140.84
Amitabh Varshney41704172.25