Generating random sampling distributions using stochastic rasterization
AssigneeIntel Corporation
InventorFranz P. Clarberg
Life of this patent
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Abstract
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.
Claims14
A method comprising:
- using a rasterizer to generate samples over an arbitrary domain by:
- bounding the arbitrary domain;
- tessellating a bounding shape in a form of a geometric object with flat sides into a number of non-overlapping adjacent primitives;
- rasterizing the primitives to generate random samples;
- testing the samples against the arbitrary domain;
- and
- generating adaptive sampling distributions by representing a density function, that shows a shape of the arbitrary domain, as a height field, adding one dimension to the arbitrary domain:
- sampling this new domain using the rasterizer;
- and
- projecting the samples on the original domain.
At least one non-transitory computer readable medium comprising a plurality of instructions and, in response to being executed on a computing device, causing the computing device to carry out a method comprising:
- using a stochastic rasterizer to generate samples over an arbitrary domain by:
- bounding the arbitrary domain;
- tessellating a bounding simploid into a number of non-overlapping adjacent primitives;
- rasterizing the primitives to generate random samples;
- testing the samples against the arbitrary domain;
- and
- generating adaptive sampling distributions by representing a density function, that shows a shape of the arbitrary domain, as a height field, adding one dimension to the arbitrary domain:
- sampling this new domain using the rasterizer;
- and
- projecting the samples on the original domain.
An apparatus comprising:
- a processor to use a rasterizer to generate samples over an arbitrary domain, bound the arbitrary domain, tessellate a bounding shape in a form of a geometric object with flat sides into a number of non-overlapping adjacent primitives, rasterize the primitives to generate random samples, test the samples against the arbitrary domain, and generate adaptive sampling distributions by representing a density function, that shows shapes of the arbitrary domain, as a height field, add one dimension to the arbitrary domain, sample this new domain using the rasterizer and produce the samples on the original domain;
- and
- a memory coupled to said processor.
Dependencies are inferred from the claim text.