Out-of-core Resampling of Gigantic Point Clouds

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Date
2018
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
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.
Description

        
@inproceedings{
10.2312:sgp.20181178
, booktitle = {
Symposium on Geometry Processing 2018- Posters
}, editor = {
Ju, Tao and Vaxman, Amir
}, title = {{
Out-of-core Resampling of Gigantic Point Clouds
}}, author = {
Bletterer, Arnaud
and
Payan, Frédéric
and
Antonini, Marc
and
Meftah, Anis
}, year = {
2018
}, publisher = {
The Eurographics Association
}, ISSN = {
1727-8384
}, ISBN = {
978-3-03868-069-7
}, DOI = {
10.2312/sgp.20181178
} }
Citation