Stochastic sampling in computer graphics
ACM Transactions on Graphics (TOG)
Digital halftoning
Generating antialiased images at low sampling densities
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
SIGGRAPH '92 Proceedings of the 19th annual conference on Computer graphics and interactive techniques
Computer speech: recognition, compression, synthesis
Computer speech: recognition, compression, synthesis
Antialiasing through stochastic sampling
SIGGRAPH '85 Proceedings of the 12th annual conference on Computer graphics and interactive techniques
A simple and efficient error-diffusion algorithm
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
NPAR '02 Proceedings of the 2nd international symposium on Non-photorealistic animation and rendering
Interactive geometry remeshing
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Advanced Global Illumination
Improving mid-tone quality of variable-coefficient error diffusion using threshold modulation
ACM SIGGRAPH 2003 Papers
Physically Based Rendering: From Theory to Implementation
Physically Based Rendering: From Theory to Implementation
Fast hierarchical importance sampling with blue noise properties
ACM SIGGRAPH 2004 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
Parallel white noise generation on a GPU via cryptographic hash
Proceedings of the 2008 symposium on Interactive 3D graphics and games
ACM SIGGRAPH 2008 papers
Procedural noise using sparse Gabor convolution
ACM SIGGRAPH 2009 papers
Capacity-constrained point distributions: a variant of Lloyd's method
ACM SIGGRAPH 2009 papers
Structure-aware error diffusion
ACM SIGGRAPH Asia 2009 papers
Multi-class blue noise sampling
ACM SIGGRAPH 2010 papers
Anisotropic blue noise sampling
ACM SIGGRAPH Asia 2010 papers
Spectral sampling of manifolds
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
ACM SIGGRAPH 2011 papers
Structure-preserving stippling by priority-based error diffusion
Proceedings of Graphics Interface 2011
Farthest-point optimized point sets with maximized minimum distance
Proceedings of the ACM SIGGRAPH Symposium on High Performance Graphics
A Simple Algorithm for Maximal Poisson-Disk Sampling in High Dimensions
Computer Graphics Forum
Efficient computation of blue noise point sets through importance sampling
EGSR'11 Proceedings of the Twenty-second Eurographics conference on Rendering
Blue noise through optimal transport
ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH Asia 2012
Fourier analysis of stochastic sampling strategies for assessing bias and variance in integration
ACM Transactions on Graphics (TOG) - SIGGRAPH 2013 Conference Proceedings
Line segment sampling with blue-noise properties
ACM Transactions on Graphics (TOG) - SIGGRAPH 2013 Conference Proceedings
Blue noise sampling with controlled aliasing
ACM Transactions on Graphics (TOG)
PixelPie: maximal Poisson-disk sampling with rasterization
Proceedings of the 5th High-Performance Graphics Conference
ACM Transactions on Graphics (TOG)
A shape-aware model for discrete texture synthesis
EGSR '13 Proceedings of the Eurographics Symposium on Rendering
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Point samples with different spectral noise properties (often defined using color names such as white, blue, green, and red) are important for many science and engineering disciplines including computer graphics. While existing techniques can easily produce white and blue noise samples, relatively little is known for generating other noise patterns. In particular, no single algorithm is available to generate different noise patterns according to user-defined spectra. In this paper, we describe an algorithm for generating point samples that match a user-defined Fourier spectrum function. Such a spectrum function can be either obtained from a known sampling method, or completely constructed by the user. Our key idea is to convert the Fourier spectrum function into a differential distribution function that describes the samples' local spatial statistics; we then use a gradient descent solver to iteratively compute a sample set that matches the target differential distribution function. Our algorithm can be easily modified to achieve adaptive sampling, and we provide a GPU-based implementation. Finally, we present a variety of different sample patterns obtained using our algorithm, and demonstrate suitable applications.