Hyperplane Culling for Stochastic Rasterization

Hyperplane Culling for Stochastic Rasterization

By Jacob Munkberg1, Tomas Akenine-Möller1,2
1Intel Corporation, 2Lund University

We present two novel culling tests for rasterization of simultaneous depth of field and motion blur. These tests efficiently reduce the set of xyuvt samples that need to be coverage tested within a screen space tile. The first test finds linear bounds in ut- and vt-space using a separating line algorithm. We also derive a hyperplane in xyuvt-space for each triangle edge, and all samples outside of these planes are culled in our second test. Based on these tests, we present an efficient stochastic rasterizer, which has substantially higher sample test efficiency and lower arithmetic cost than previous tile-based stochastic rasterizers.

Preprint Paper: Hyperplane Culling for Stochastic Rasterization [PDF 4.3 MB], Slides

Presented at: Eurographics 2012, Cagliari, Italy
Hyperplane Culling for Stochastic Rasterization. Jacob Munkberg, Tomas Akenine-Möller. In Eurographics 2012 –Short Papers, pp. 105-108
The definitive version is available at http://diglib.eg.org/

 

 

Einzelheiten zur Compiler-Optimierung finden Sie in unserem Optimierungshinweis.