Tilings and patterns
Random number generation and quasi-Monte Carlo methods
Random number generation and quasi-Monte Carlo methods
Pattern-based texturing revisited
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
An Efficient Method for Generating Discrete Random Variables with General Distributions
ACM Transactions on Mathematical Software (TOMS)
I3D '01 Proceedings of the 2001 symposium on Interactive 3D graphics
Pattern based procedural textures
I3D '03 Proceedings of the 2003 symposium on Interactive 3D graphics
Graphcut textures: image and video synthesis using graph cuts
ACM SIGGRAPH 2003 Papers
Wang Tiles for image and texture generation
ACM SIGGRAPH 2003 Papers
Tile-based texture mapping on graphics hardware
Proceedings of the ACM SIGGRAPH/EUROGRAPHICS conference on Graphics hardware
Texture design using a simplicial complex of morphable textures
ACM SIGGRAPH 2005 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)
Volume illustration using wang cubes
ACM Transactions on Graphics (TOG)
Generating an /spl omega/-tile set for texture synthesis
CGI '05 Proceedings of the Computer Graphics International 2005
Optimized tile-based texture synthesis
GI '07 Proceedings of Graphics Interface 2007
ACM SIGGRAPH 2007 papers
Tile-based methods for interactive applications
ACM SIGGRAPH 2008 classes
Color filter array design using random patterns with blue noise chromatic spectra
Image and Vision Computing
Texture tiling on arbitrary topological surfaces using wang tiles
EGSR'05 Proceedings of the Sixteenth Eurographics conference on Rendering Techniques
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We investigate semi-stochastic tilings based on Wang or corner tiles for the real-time synthesis of example-based textures. In particular, we propose two new tiling approaches: (1) to replace stochastic tilings with pseudo-random tilings based on the Halton low-discrepancy sequence, and (2) to allow the controllable generation of tilings based on a user-provided probability distribution. Our first method prevents local repetition of texture content as common with stochastic approaches and yields better results with smaller sets of utilized tiles. Our second method allows to directly influence the synthesis result which–in combination with an enhanced tile construction method that merges multiple source textures-extends synthesis tasks to globally–varying textures. We show that both methods can be implemented very efficiently in connection with tile-based texture mapping and also present a general rule that allows to significantly reduce resulting tile sets.