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Out-of-core Resampling of Gigantic Point Clouds

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.
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Contributor : Frédéric Payan <>
Submitted on : Monday, September 17, 2018 - 3:27:30 PM
Last modification on : Friday, November 20, 2020 - 9:24:03 AM
Long-term archiving on: : Tuesday, December 18, 2018 - 2:07:49 PM


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  • HAL Id : hal-01875595, version 1



Arnaud Bletterer, Frédéric Payan, Marc Antonini, Anis Meftah. Out-of-core Resampling of Gigantic Point Clouds. Symposium on Geometry Processing 2018, 2018, Paris, France. ⟨hal-01875595⟩



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