The goal of this project is to generate stochastic anisotropic samples with Poisson-disk characteristic over a two-dimensional domain. In contrast to isotropic samples, we understand anisotropic samples as non-overlapping ellipses whose size and density match a given anisotropic metric. Anisotropic noise samples are useful for many visualization and graphics applications. The spot samples can be used as input for texture generation, e.g., line integral convolution (LIC), but can also be used directly for visualization. The definition of the spot samples by a metric tensor makes them especially suitable for the visualization of tensor fields that can be translated into a metric. Our work combines ideas from sampling theory and mesh generation.


Anisotropic Sampling of Planar and Two-Manifold Domains for Texture Generation and Glyph Distribution Transactions on Visualization and Computer Graphics (TVCG), Vol.19, pp. 1782-1794, 2013 Andrea Kratz, Daniel Baum, Ingrid Hotz BibTeX
Anisotropic Sampling
Particle-Based Anisotropic Sampling for Two-Dimensional Tensor Field Visualization Vision Modeling and Visualization (VMV’11), pp. 145-152, 2011 Andrea Kratz, Nino Kettlitz, Ingrid Hotz BibTeX
Anisotropic Sampling