Fast Maximal Poisson-Disk Sampling by Randomized Tiling
| dc.contributor.author | Wang, Tong | en_US |
| dc.contributor.author | Suda, Reiji | en_US |
| dc.contributor.editor | Vlastimil Havran and Karthik Vaiyanathan | en_US |
| dc.date.accessioned | 2017-12-06T19:47:41Z | |
| dc.date.available | 2017-12-06T19:47:41Z | |
| dc.date.issued | 2017 | |
| dc.description.abstract | It is generally accepted that Poisson disk sampling provides great properties in various applications in computer graphics. We present KD-tree based randomized tiling (KDRT), an e cient method to generate maximal Poisson-disk samples by replicating and conquering tiles clipped from a pa ern of very small size. Our method is a twostep process: rst, randomly clipping tiles from an MPS(Maximal Poisson-disk Sample) pa ern, and second, conquering these tiles together to form the whole sample plane. e results showed that this method can e ciently generate maximal Poisson-disk samples with very small trade-o in bias error. ere are two main contributions of this paper: First, a fast and robust Poisson-disk sample generation method is presented; Second, this method can be used to combine several groups of independently generated sample pa erns to form a larger one, thus can be applied as a general parallelization scheme of any MPS methods. | en_US |
| dc.description.sectionheaders | Real-Time Graphics Techniques | |
| dc.description.seriesinformation | Eurographics/ ACM SIGGRAPH Symposium on High Performance Graphics | |
| dc.identifier.doi | 10.1145/3105762.3105778 | |
| dc.identifier.isbn | 978-1-4503-5101-0 | |
| dc.identifier.issn | 2079-8679 | |
| dc.identifier.uri | https://doi.org/10.1145/3105762.3105778 | |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.1145/3105762-3105778 | |
| dc.publisher | ACM | en_US |
| dc.subject | Computing methodologies Computer graphics | |
| dc.subject | Poisson | |
| dc.subject | disk Sampling | |
| dc.title | Fast Maximal Poisson-Disk Sampling by Randomized Tiling | en_US |