標題: Clip Space Sample Culling for Motion Blur and Defocus Blur
作者: Wu, Yi-Jeng
Way, Der-Lor
Tsai, Yu-Ting
Shih, Zen-Chung
多媒體工程研究所
Institute of Multimedia Engineering
關鍵字: motion blur;defocus blur;stochastic rasterization;sample test efficiency (STE);focal depth
公開日期: 1-五月-2015
摘要: Motion blur and defocus blur are two common visual effects for rendering realistic camera images. This paper presents a novel clip space culling for stochastic rasterization to render motion and defocus blur effects. Our proposed algorithm reduces the sample coverage using the clip space information in camera lens domain (UV) and time domain (T). First, samples outside the camera lens were culled in stage I using the linear relationship between camera lens and vertex position. Second, samples outside the time bounds were culled in stage II using the triangle similarity in clip space to find the intersection time. Each pixel was computed within two linear bounds only once. Our method achieves good sample test efficiency with low computation cost for real-time stochastic rasterization. Finally, the proposed method is demonstrated by means of various experiments, and a comparison is made with previous works. Our algorithm was able to handle these two blur effects simultaneously and performed better than others did.
URI: http://hdl.handle.net/11536/127932
ISSN: 1016-2364
期刊: JOURNAL OF INFORMATION SCIENCE AND ENGINEERING
Volume: 31
起始頁: 1071
結束頁: 1084
顯示於類別:期刊論文