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
SMI '03 Proceedings of the Shape Modeling International 2003
Wang Tiles for image and texture generation
ACM SIGGRAPH 2003 Papers
Fast hierarchical importance sampling with blue noise properties
ACM SIGGRAPH 2004 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
Parallel Poisson disk sampling
ACM SIGGRAPH 2008 papers
ACM SIGGRAPH 2008 papers
Direct sampling on surfaces for high quality remeshing
Proceedings of the 2008 ACM symposium on Solid and physical modeling
Poisson Disk Point Sets by Hierarchical Dart Throwing
RT '07 Proceedings of the 2007 IEEE Symposium on Interactive Ray Tracing
Capacity-constrained point distributions: a variant of Lloyd's method
ACM SIGGRAPH 2009 papers
EGSR'09 Proceedings of the Twentieth Eurographics conference on Rendering
Anisotropic blue noise sampling
ACM SIGGRAPH Asia 2010 papers
Blue-noise point sampling using kernel density model
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
Point sampling with general noise spectrum
ACM Transactions on Graphics (TOG) - SIGGRAPH 2012 Conference Proceedings
A theory of monte carlo visibility sampling
ACM Transactions on Graphics (TOG)
Random discrete colour sampling
CAe '12 Proceedings of the Eighth Annual Symposium on Computational Aesthetics in Graphics, Visualization, and Imaging
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
Multi-Class Anisotropic Electrostatic Halftoning
Computer Graphics Forum
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)
Generating pointillism paintings based on Seurat's color composition
EGSR '13 Proceedings of the Eurographics Symposium on Rendering
Hi-index | 0.00 |
Sampling is a core process for a variety of graphics applications. Among existing sampling methods, blue noise sampling remains popular thanks to its spatial uniformity and absence of aliasing artifacts. However, research so far has been mainly focused on blue noise sampling with a single class of samples. This could be insufficient for common natural as well as man-made phenomena requiring multiple classes of samples, such as object placement, imaging sensors, and stippling patterns. We extend blue noise sampling to multiple classes where each individual class as well as their unions exhibit blue noise characteristics. We propose two flavors of algorithms to generate such multi-class blue noise samples, one extended from traditional Poisson hard disk sampling for explicit control of sample spacing, and another based on our soft disk sampling for explicit control of sample count. Our algorithms support uniform and adaptive sampling, and are applicable to both discrete and continuous sample space in arbitrary dimensions. We study characteristics of samples generated by our methods, and demonstrate applications in object placement, sensor layout, and color stippling.