Sampled and Analytic Rasterization
| dc.contributor.author | Auzinger, Thomas | en_US |
| dc.contributor.author | Wimmer, Michael | en_US |
| dc.contributor.editor | Michael Bronstein and Jean Favre and Kai Hormann | en_US |
| dc.date.accessioned | 2014-02-01T16:26:18Z | |
| dc.date.available | 2014-02-01T16:26:18Z | |
| dc.date.issued | 2013 | en_US |
| dc.description.abstract | In this poster we present an overview of exact anti-aliasing (AA) methods in rasterization. In contrast to the common supersampling approaches for visibility AA (e.g. MSAA) or both visibility and shading AA (e.g. SSAA, decoupled sampling), prefiltering provides the mathematically exact solution to the aliasing problem. Instead of averaging a set a supersamples, the input data is convolved with a suitable low-pass filter before sampling is applied. Recent work showed that for both visibility signals and simple shading models, a closed-form solution to the convolution integrals can be found. As our main contribution, we present a classification of both sample-based and analytic AA approaches for rasterization and analyse their strengths and weaknesses. | en_US |
| dc.description.seriesinformation | Vision, Modeling & Visualization | en_US |
| dc.identifier.isbn | 978-3-905674-51-4 | en_US |
| dc.identifier.uri | https://doi.org/10.2312/PE.VMV.VMV13.223-224 | en_US |
| dc.publisher | The Eurographics Association | en_US |
| dc.subject | I.3.3 [Computer Graphics] | en_US |
| dc.subject | Picture/Image Generation | en_US |
| dc.subject | Line and curve generation | en_US |
| dc.title | Sampled and Analytic Rasterization | en_US |
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