Generating Random Sampling Distributions Using Stochastic Rasterization

摘要

Stochastic rasterization may be used as a flexible volumetric sampling mechanism. By bounding and tessellating the sampling domain, uniform sampling distributions over an arbitrary domain can be efficiently generated in up to five dimensions. Sample placement allows pseudo-random, stratified random, or blue noise sampling. Random sampling with an adaptive density function may be achieved by adding one dimension.

主权项

1. A method comprising:
using a rasterizer to generate samples over an arbitrary domain by: bounding the sampling domain; tessellating a bounding shape into a number of primitives; rasterizing the primitives to generate random samples; and testing the samples against the sampling domain.