Out-of-core Resampling of Gigantic Point Clouds

Bletterer, Arnaud; Payan, Frédéric; Antonini, Marc; Meftah, Anis

Out-of-core Resampling of Gigantic Point Clouds

Files

001-002.pdf (1.79 MB)

Date

2018

Authors

Bletterer, Arnaud
Payan, Frédéric
Antonini, Marc
Meftah, Anis

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.

        @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
}
}