Clip Space Sample Culling for Stochastic Rasterization

• Main Authors: Wu, Yi-Jeng, 吳怡正
• Other Authors: Shih, Zen-Chung
• Format: Others
• Language: en_US
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/02164102420086016468

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.