Name
Abstract | ||
|---|---|---|
While supersampling is an essential element for high quality rendering, high sampling rates, routinely employed in offline rendering, are still considered quite burdensome for real-time ray tracing. In this paper, we propose a selective and adaptive supersampling technique aimed at the development of a real-time ray tracer on today's many-core processors. For efficient utilization of very precious computing time, this technique explores both image---space and object---space attributes, which can be easily gathered during the ray tracing computation, minimizing rendering artifacts by cleverly distributing ray samples to rendering elements according to priorities that are selectively set by a user. Our implementation on the current GPU demonstrates that the presented algorithm makes high sampling rates as effective as 9 to 16 samples per pixel more affordable than before for real-time ray tracing. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1145/1572769.1572788 | High Performance Graphics |
Keywords | Field | DocType |
real-time ray,space attribute,rendering artifact,ray sample,current gpu,high sampling rate,real-time ray tracer,offline rendering,adaptive supersampling technique,high quality rendering,gpu computing,real time,ray tracing | Computer vision,Computer graphics (images),3D rendering,Ray tracing (graphics),Computer science,Artificial intelligence,Pixel,Sampling (statistics),General-purpose computing on graphics processing units,Supersampling,Rendering (computer graphics),Computation | Conference |
Citations | PageRank | References |
6 | 0.48 | 18 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bongjun Jin | 1 | 6 | 0.48 |
| Insung Ihm | 2 | 368 | 40.07 |
| Byungjoon Chang | 3 | 43 | 3.14 |
| Chanmin Park | 4 | 12 | 3.55 |
| Won-Jong Lee | 5 | 129 | 13.38 |
| Seokyoon Jung | 6 | 70 | 5.65 |