A parallel algorithm for improving the maximal property of Poisson disk sampling in R
            <sup>2</sup>
            and R
            <sup>3</sup>

Xiang, Ying; Li, Zhenhua; He, Ying

doi:10.1145/2448196.2448227

Cited by 2 publications

(1 citation statement)

References 1 publication

(2 reference statements)

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“…Due to the broad interest created by the initial work on PDS, a large number of approaches to generate Poisson disk distributions have been developed over the last decade (Gamito and Maddock, 2009;Ebeida et al, 2012Ebeida et al, , 2011Ip et al, 2013;Bridson, 2007;Öztireli and Gross, 2012;Heck et al, 2013;Wei, 2008;Dunbar and Humphreys, 2006;Wei, 2010;Balzer et al, 2009;Geng et al, 2013;Wonka, 2012, 2013;Ying et al, 2013bYing et al, , 2014Hou et al, 2013;Ying et al, 2013a;Guo et al, 2014;Wachtel et al, 2014;Xu et al, 2014;Ebeida et al, 2014;de Goes et al, 2012;Zhou et al, 2012). Most Poisson disk sample generation methods are based on dart throwing (Dippé and Wold, 1985;Cook, 1986), which attempts to generate as many darts as necessary to cover the sampling domain while not violating the Poisson disk criterion.…”

Section: Poisson Disk Samplingmentioning

confidence: 99%

A Spectral Approach for the Design of Experiments: Design, Analysis and Algorithms

Kailkhura,

Thiagarajan,

Rastogi

et al. 2017

Preprint

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This paper proposes a new approach to construct high quality space-filling sample designs. First, we propose a novel technique to quantify the space-filling property and optimally trade-off uniformity and randomness in sample designs in arbitrary dimensions. Second, we connect the proposed metric (defined in the spatial domain) to the objective measure of the design performance (defined in the spectral domain). This connection serves as an analytic framework for evaluating the qualitative properties of space-filling designs in general. Using the theoretical insights provided by this spatial-spectral analysis, we derive the notion of optimal space-filling designs, which we refer to as space-filling spectral designs. Third, we propose an efficient estimator to evaluate the space-filling properties of sample designs in arbitrary dimensions and use it to develop an optimization framework to generate high quality space-filling designs. Finally, we carry out a detailed performance comparison on two different applications in 2 to 6 dimensions: a) image reconstruction and b) surrogate modeling on several benchmark optimization functions and an inertial confinement fusion (ICF) simulation code. We demonstrate that the propose spectral designs significantly outperform existing approaches especially in high dimensions.

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Section: Poisson Disk Samplingmentioning

confidence: 99%

A Spectral Approach for the Design of Experiments: Design, Analysis and Algorithms

Kailkhura,

Thiagarajan,

Rastogi

et al. 2017

Preprint

View full text Add to dashboard Cite

show abstract

Theoretical guarantees for poisson disk sampling using pair correlation function

Kailkhura

Thiagarajan

Bremer

et al. 2016

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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A parallel algorithm for improving the maximal property of Poisson disk sampling in R ² and R ³

Cited by 2 publications

References 1 publication

A Spectral Approach for the Design of Experiments: Design, Analysis and Algorithms

A Spectral Approach for the Design of Experiments: Design, Analysis and Algorithms

Theoretical guarantees for poisson disk sampling using pair correlation function

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A parallel algorithm for improving the maximal property of Poisson disk sampling in R 2 and R 3

Cited by 2 publications

References 1 publication

A Spectral Approach for the Design of Experiments: Design, Analysis and Algorithms

A Spectral Approach for the Design of Experiments: Design, Analysis and Algorithms

Theoretical guarantees for poisson disk sampling using pair correlation function

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A parallel algorithm for improving the maximal property of Poisson disk sampling in R ² and R ³