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EGH
2009
Springer

Data-parallel rasterization of micropolygons with defocus and motion blur

9 years 1 months ago
Data-parallel rasterization of micropolygons with defocus and motion blur
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.
Kayvon Fatahalian, Edward Luong, Solomon Boulos, K
Added 17 Feb 2011
Updated 17 Feb 2011
Type Journal
Year 2009
Where EGH
Authors Kayvon Fatahalian, Edward Luong, Solomon Boulos, Kurt Akeley, William R. Mark, Pat Hanrahan
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