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GaussianAntialiasing

An implementation of zero-cost antialiasing by probabilistically perturbing rasterization of pixels. Invented by Bob Burrough.

Install / Use

/learn @bburrough/GaussianAntialiasing
About this skill

Quality Score

0/100

Supported Platforms

Universal

README

Gaussian Anti-aliasing

Gaussian anti-aliasing is a improves image quality by randomly sampling the geometry within a pixel rather than sampling the geometry at the pixel center. The idea is brought over from ray-tracing where the color of a pixel is determined by randomly sampling geometry within each pixel. It achieves perceptual smoothing of rendered images with zero impact to rendering performance.

screenshot


Under traditional rasterization procedures, the color of a pixel is determined by checking whether a triangle overlaps the pixel at its exact center. This leads to a grating pattern commonly known as jaggies. Instead of sampling geometry at exact pixel centers, Gaussian anti-aliasing samples a random point within each pixel. As this occurs repeatedly as in the case with realtime renderered content, the perceived color of the pixel converges towards a more accurate depiction of the geometry that lies within that pixel.

https://user-images.githubusercontent.com/12551477/138059517-0ef61833-b336-4361-8afb-06e902ff11f4.mp4


Due to persistence of vision — the effect that causes us to see motion in a sequence of animated frames — successive frames appear blended. With high frequency displays and realtime rendered content, the user just sees a smooth image free of jaggies. Whereas temporal anti-aliasing adds additional processing steps by jittering and blending a sequence of frames, Gaussian anti-aliasing achieves a smoother image with zero impact to performance.

comparison

License

This project is made availble under either the MIT or public domain licenses. Choose whichever you prefer.

Related Skills

View on GitHub
GitHub Stars13
CategoryDevelopment
Updated2mo ago
Forks2

Languages

ShaderLab

Security Score

90/100

Audited on Jan 17, 2026

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