Stochastic sampling in computer graphics
ACM Transactions on Graphics (TOG)
Generating antialiased images at low sampling densities
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Spectrally optimal sampling for distribution ray tracing
Proceedings of the 18th annual conference on Computer graphics and interactive techniques
SIGGRAPH '92 Proceedings of the 19th annual conference on Computer graphics and interactive techniques
Hierarchical Poisson disk sampling distributions
Proceedings of the conference on Graphics interface '92
Fast texture synthesis using tree-structured vector quantization
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Wang Tiles for image and texture generation
ACM SIGGRAPH 2003 Papers
Fast hierarchical importance sampling with blue noise properties
ACM SIGGRAPH 2004 Papers
Parallel controllable texture synthesis
ACM SIGGRAPH 2005 Papers
A procedural object distribution function
ACM Transactions on Graphics (TOG)
A spatial data structure for fast Poisson-disk sample generation
ACM SIGGRAPH 2006 Papers
Recursive Wang tiles for real-time blue noise
ACM SIGGRAPH 2006 Papers
An alternative for Wang tiles: colored edges versus colored corners
ACM Transactions on Graphics (TOG)
ACM SIGGRAPH 2007 papers
Fast Poisson disk sampling in arbitrary dimensions
ACM SIGGRAPH 2007 sketches
Stochastic rasterization using time-continuous triangles
Proceedings of the 22nd ACM SIGGRAPH/EUROGRAPHICS symposium on Graphics hardware
Parallel white noise generation on a GPU via cryptographic hash
Proceedings of the 2008 symposium on Interactive 3D graphics and games
Poisson Disk Point Sets by Hierarchical Dart Throwing
RT '07 Proceedings of the 2007 IEEE Symposium on Interactive Ray Tracing
Cluster-based probability model and its application to image and texture processing
IEEE Transactions on Image Processing
Adaptive numerical cumulative distribution functions for efficient importance sampling
EGSR'05 Proceedings of the Sixteenth Eurographics conference on Rendering Techniques
Capacity-constrained point distributions: a variant of Lloyd's method
ACM SIGGRAPH 2009 papers
Accurate multidimensional Poisson-disk sampling
ACM Transactions on Graphics (TOG)
Multi-class blue noise sampling
ACM SIGGRAPH 2010 papers
Parallel Poisson disk sampling with spectrum analysis on surfaces
ACM SIGGRAPH Asia 2010 papers
Anisotropic blue noise sampling
ACM SIGGRAPH Asia 2010 papers
Blue-noise point sampling using kernel density model
ACM SIGGRAPH 2011 papers
Efficient maximal poisson-disk sampling
ACM SIGGRAPH 2011 papers
Differential domain analysis for non-uniform sampling
ACM SIGGRAPH 2011 papers
Farthest-point optimized point sets with maximized minimum distance
Proceedings of the ACM SIGGRAPH Symposium on High Performance Graphics
Proceedings of the 2011 SIGGRAPH Asia Conference
Efficient and good Delaunay meshes from random points
Computer-Aided Design
Parallel and accurate Poisson disk sampling on arbitrary surfaces
SIGGRAPH Asia 2011 Sketches
Applications of Geometry Processing: Blue noise sampling of surfaces
Computers and Graphics
A theory of monte carlo visibility sampling
ACM Transactions on Graphics (TOG)
A Low-Memory, Straightforward and Fast Bilateral Filter Through Subsampling in Spatial Domain
Computer Graphics Forum
A Simple Algorithm for Maximal Poisson-Disk Sampling in High Dimensions
Computer Graphics Forum
Fast Generation of Approximate Blue Noise Point Sets
Computer Graphics Forum
Parallel Blue-noise Sampling by Constrained Farthest Point Optimization
Computer Graphics Forum
Analysis and synthesis of point distributions based on pair correlation
ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH Asia 2012
Blue noise through optimal transport
ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH Asia 2012
Efficient computation of blue noise point sets through importance sampling
EGSR'11 Proceedings of the Twenty-second Eurographics conference on Rendering
EGSR'09 Proceedings of the Twentieth Eurographics conference on Rendering
Design and novel uses of higher-dimensional rasterization
EGGH-HPG'12 Proceedings of the Fourth ACM SIGGRAPH / Eurographics conference on High-Performance Graphics
Line segment sampling with blue-noise properties
ACM Transactions on Graphics (TOG) - SIGGRAPH 2013 Conference Proceedings
PixelPie: maximal Poisson-disk sampling with rasterization
Proceedings of the 5th High-Performance Graphics Conference
ACM Transactions on Graphics (TOG)
Gap processing for adaptive maximal poisson-disk sampling
ACM Transactions on Graphics (TOG)
k-d Darts: Sampling by k-dimensional flat searches
ACM Transactions on Graphics (TOG)
A parallel algorithm for improving the maximal property of Poisson disk sampling
Computer-Aided Design
Improving spatial coverage while preserving the blue noise of point sets
Computer-Aided Design
Parallel structure-aware halftoning
Multimedia Tools and Applications
Fast adaptive blue noise on polygonal surfaces
Graphical Models
A shape-aware model for discrete texture synthesis
EGSR '13 Proceedings of the Eurographics Symposium on Rendering
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Sampling is important for a variety of graphics applications include rendering, imaging, and geometry processing. However, producing sample sets with desired efficiency and blue noise statistics has been a major challenge, as existing methods are either sequential with limited speed, or are parallel but only through pre-computed datasets and thus fall short in producing samples with blue noise statistics. We present a Poisson disk sampling algorithm that runs in parallel and produces all samples on the fly with desired blue noise properties. Our main idea is to subdivide the sample domain into grid cells and we draw samples concurrently from multiple cells that are sufficiently far apart so that their samples cannot conflict one another. We present a parallel implementation of our algorithm running on a GPU with constant cost per sample and constant number of computation passes for a target number of samples. Our algorithm also works in arbitrary dimension, and allows adaptive sampling from a user-specified importance field. Furthermore, our algorithm is simple and easy to implement, and runs faster than existing techniques.