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| Content Provider | ACM Digital Library |
|---|---|
| Author | Isenburg, Martin Shewchuk, Jonathan Liu, Yuanxin Snoeyink, Jack |
| Abstract | We show how to greatly accelerate algorithms that compute Delaunay triangulations of huge, well-distributed point sets in 2D and 3D by exploiting the natural spatial coherence in a stream of points. We achieve large performance gains by introducing spatial finalization into point streams: we partition space into regions, and augment a stream of input points with finalization tags that indicate when a point is the last in its region. By extending an incremental algorithm for Delaunay triangulation to use finalization tags and produce streaming mesh output, we compute a billion-triangle terrain representation for the Neuse River system from 11.2 GB of LIDAR data in 48 minutes using only 70 MB of memory on a laptop with two hard drives. This is a factor of twelve faster than the previous fastest out-of-core Delaunay triangulation software. |
| Starting Page | 1049 |
| Ending Page | 1056 |
| Page Count | 8 |
| File Format | PDF QT / MOV |
| ISBN | 1595933646 |
| DOI | 10.1145/1179352.1141992 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2006-07-30 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Geometry processing Delaunay triangulation Stream processing Spatial finalization Tin terrain model |
| Content Type | Video Text |
| Resource Type | Article |
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