Data-Parallel Rasterization of Micropolygons with Defocus and Motion Blur
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Date
2009
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Current GPUs rasterize micropolygons (polygons approximately one pixel in size) inefficiently. We design and analyze the costs of three alternative data-parallel algorithms for rasterizing micropolygon workloads for the real-time domain. First, we demonstrate that efficient micropolygon rasterization requires parallelism across many polygons, not just within a single polygon. Second, we produce a data-parallel implementation of an existing stochastic rasterization algorithm by Pixar, which is able to produce motion blur and depth-of-field effects. Third, we provide an algorithm that leverages interleaved sampling for motion blur and camera defocus. This algorithm outperforms Pixar s algorithm when rendering objects undergoing moderate defocus or high motion and has the added benefit of predictable performance.
Description
@inproceedings{10.1145:1572769.1572780,
booktitle = {High-Performance Graphics},
editor = {David Luebke and Philipp Slusallek},
title = {{Data-Parallel Rasterization of Micropolygons with Defocus and Motion Blur}},
author = {Fatahalian, Kayvon and Luong, Edward and Boulos, Solomon and Akeley, Kurt and Mark, William R. and Hanrahan, Pat},
year = {2009},
publisher = {The Eurographics Association},
ISSN = {2079-8687},
ISBN = {978-1-60558-603-8},
DOI = {10.1145/1572769.1572780}
}