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Contribution Details
| Type | Journal Article |
| Scope | Discipline-based scholarship |
| Title | Streaming surface sampling using Gaussian ε-nets |
| Organization Unit | |
| Authors |
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| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Journal Title | The Visual Computer |
| Publisher | Springer |
| Geographical Reach | international |
| ISSN | 0178-2789 |
| Volume | 25 |
| Number | 5-7 |
| Page Range | 411 - 421 |
| Date | 2009 |
| Abstract Text | We propose a robust, feature preserving and user-steerable mesh sampling algorithm, based on the one-to-many mapping of a regular sampling of the Gaussian sphere onto a given manifold surface. Most of the operations are local, and no global information is maintained. For this reason, our algorithm is amenable to a parallel or streaming implementation and is most suitable in situations when it is not possible to hold all the input data in memory at the same time. Using ε-nets, we analyze the sampling method and propose solutions to avoid shortcomings inherent to all localized sampling methods. Further, as a byproduct of our sampling algorithm, a shape approximation is produced. Finally, we demonstrate a streaming implementation that handles large meshes with a small memory footprint. |
| Digital Object Identifier | 10.1007/s00371-009-0351-3 |
| Other Identification Number | merlin-id:219 |
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| Keywords | Normal quantization - Surface sampling - Shape approximation - Epsilon-nets |
| Additional Information | The original publication is available at www.springerlink.com |