Distributed Surface Reconstruction
| dc.contributor.author | Marin, Diana | en_US |
| dc.contributor.author | Komon, Patrick | en_US |
| dc.contributor.author | Ohrhallinger, Stefan | en_US |
| dc.contributor.author | Wimmer, Michael | en_US |
| dc.contributor.editor | Liu, Lingjie | en_US |
| dc.contributor.editor | Averkiou, Melinos | en_US |
| dc.date.accessioned | 2024-04-30T08:20:58Z | |
| dc.date.available | 2024-04-30T08:20:58Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | Recent advancements in scanning technologies and their rise in availability have shifted the focus from reconstructing surfaces from point clouds of small areas to large, e.g., city-wide scenes, containing massive amounts of data. We adapt a surface reconstruction method to work in a distributed fashion on a high-performance cluster, reconstructing datasets with millions of vertices in seconds. We exploit the locality of the connectivity required by the reconstruction algorithm to efficiently divide-andconquer the problem of creating triangulations from very large unstructured point clouds. | en_US |
| dc.description.sectionheaders | Posters | |
| dc.description.seriesinformation | Eurographics 2024 - Posters | |
| dc.identifier.doi | 10.2312/egp.20241037 | |
| dc.identifier.isbn | 978-3-03868-239-4 | |
| dc.identifier.issn | 1017-4656 | |
| dc.identifier.pages | 2 pages | |
| dc.identifier.uri | https://doi.org/10.2312/egp.20241037 | |
| dc.identifier.uri | https://diglib.eg.org/handle/10.2312/egp20241037 | |
| dc.publisher | The Eurographics Association | en_US |
| dc.rights | Attribution 4.0 International License | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | CCS Concepts: Computing methodologies → Point-based models | |
| dc.subject | Computing methodologies → Point | |
| dc.subject | based models | |
| dc.title | Distributed Surface Reconstruction | en_US |