Name
Abstract | ||
|---|---|---|
Monte Carlo is the only choice for a physically correct method to do global illumination in the field of realistic image synthesis. Generally Monte Carlo based algorithms require a lot of time to eliminate the noise to get an acceptable image. Adaptive sampling is an interesting tool to reduce noise, in which the evaluation of homogeneity of pixel's samples is the key point. In this paper, we propose a new homogeneity measure, namely the arithmetic mean - geometric mean difference (abbreviated to AM - GM difference), which is developed to execute adaptive sampling efficiently. Implementation results demonstrate that our novel adaptive sampling method can perform significantly better than classic ones. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-74477-1_56 | ICCSA |
Keywords | Field | DocType |
monte carlo global illumination,adaptive sampling,acceptable image,realistic image synthesis,new homogeneity measure,novel adaptive,global illumination,gm difference,am-gm difference,correct method,geometric mean difference,monte carlo,geometric mean,arithmetic mean | Slice sampling,Rejection sampling,Monte Carlo method,Mathematical optimization,Computer science,Adaptive sampling,Hybrid Monte Carlo,Pixel,Global illumination,Monte Carlo integration | Conference |
Volume | ISSN | ISBN |
4706 | 0302-9743 | 3-540-74475-4 |
Citations | PageRank | References |
0 | 0.34 | 14 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qing Xu | 1 | 48 | 12.40 |
| Mateu Sbert | 2 | 1108 | 123.95 |
| Miquel Feixas | 3 | 637 | 45.61 |
| Jianfeng Zhang | 4 | 3 | 0.99 |