Correlation-Aware Semi-Analytic Visibility for Antialiased Rendering

Cyril Crassin ; Chris Wyman ; Morgan McGuire ; Aaron Lefohn

HPG ’18: High-Performance Graphics (inproceedings)

Abstract

Geometric aliasing is a persistent challenge for real-time rendering. Hardware multisampling remains limited to 8 × , analytic coverage fails to capture correlated visibility samples, and spatial and temporal postfiltering primarily target edges of superpixel primitives. We describe a novel semi-analytic representation of coverage designed to make progress on geometric antialiasing for subpixel primitives and pixels containing many edges while handling correlated subpixel coverage. Although not yet fast enough to deploy, it crosses three critical thresholds: image quality comparable to 256× MSAA, faster than 64× MSAA, and constant space per pixel.

CWML18
Geometric aliasing is a persistent challenge for real-time rendering. Hardware multisampling remains limited to 8 × , analytic coverage fails to capture correlated visibility samples, and spatial and temporal postfiltering primarily target edges of superpixel primitives. We describe a novel semi-analytic representation of coverage designed to make progress on geometric antialiasing for subpixel primitives and pixels containing many edges while handling correlated subpixel coverage. Although not yet fast enough to deploy, it crosses three critical thresholds: image quality comparable to 256× MSAA, faster than 64× MSAA, and constant space per pixel.

Video


BibTex references

@inproceedings{CWML18,
author = {Cyril Crassin and Chris Wyman and Morgan McGuire and Aaron Lefohn},
title = {Correlation-Aware Semi-Analytic Visibility for Antialiased Rendering},
url = {https://research.nvidia.com/sites/default/files/pubs/2018-08_Correlation-Aware-Semi-Analytic-Visibility//CorrelationAwareVisibility_AuthorsVersion.pdf, Paper - Authors Version},
year = {2018},
date = {2018-08-10},
booktitle = {HPG ’18: High-Performance Graphics},
}
  

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