Out-of-core Resampling of Gigantic Point Clouds
| dc.contributor.author | Bletterer, Arnaud | en_US |
| dc.contributor.author | Payan, Frédéric | en_US |
| dc.contributor.author | Antonini, Marc | en_US |
| dc.contributor.author | Meftah, Anis | en_US |
| dc.contributor.editor | Ju, Tao and Vaxman, Amir | en_US |
| dc.date.accessioned | 2018-07-08T15:27:55Z | |
| dc.date.available | 2018-07-08T15:27:55Z | |
| dc.date.issued | 2018 | |
| dc.description.abstract | Nowadays, LiDAR scanners are able to capture complex scenes of real life, leading to extremely detailed point clouds. However, the amount of points acquired (several billions) and their distribution raise the problem of sampling a surface optimally. Indeed, these point clouds finely describe the acquired scene, but also exhibit numerous defects in terms of sampling quality, and sometimes contain too many samples to be processed as they are. In this work, we introduce a local graph-based structure that enables to manipulate gigantic point clouds, by taking advantage of their inherent structure. In particular, we show how this structure allows to resample gigantic point clouds efficiently, with good blue-noise properties, whatever their size in a reasonable time. | en_US |
| dc.description.sectionheaders | Posters | |
| dc.description.seriesinformation | Symposium on Geometry Processing 2018- Posters | |
| dc.identifier.doi | 10.2312/sgp.20181178 | |
| dc.identifier.isbn | 978-3-03868-069-7 | |
| dc.identifier.issn | 1727-8384 | |
| dc.identifier.pages | 1-2 | |
| dc.identifier.uri | https://doi.org/10.2312/sgp.20181178 | |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.2312/sgp20181178 | |
| dc.publisher | The Eurographics Association | en_US |
| dc.title | Out-of-core Resampling of Gigantic Point Clouds | en_US |
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