Clip Space Sample Culling for Stochastic Rasterization
碩士 === 國立交通大學 === 多媒體工程研究所 === 101 === To render realistic camera images, two effects are common : motion blur and defocus blur. We present a novel clip space culling test of stochastic rasterization of motion and defocus blur. This 2-stage test use the clip space information to reduce the samples n...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Others |
| Language: | en_US |
| Published: |
2013
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| Online Access: | http://ndltd.ncl.edu.tw/handle/02164102420086016468 |
| Summary: | 碩士 === 國立交通大學 === 多媒體工程研究所 === 101 === To render realistic camera images, two effects are common : motion blur and defocus blur. We present a novel clip space culling test of stochastic rasterization of motion and defocus blur. This 2-stage test use the clip space information to reduce the samples needed to be coverage tested over camera lens domain (uv) and time domain (t).
First we do a rough test to get a conservative range of the camera lens uv bound, and cull the samples outside this bound. Then the second test finds a similar triangular equation for each triangle vertex in xyuvt space. Based on this equation, we cull the rest of samples outside. We present a simple method for the real-time stochastic rasterizer, and achieve a good sample test efficiency with low computation cost.
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