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POINT CLOUD NON LOCAL DENOISING USING LOCAL SURFACE DESCRIPTOR SIMILARITY

Abstract : This article addresses the problem of denoising 3D data from LIDAR. It is a step often required to allow a good reconstruction of surfaces represented by point clouds. In this paper, we present an original algorithm inspired by a recent method developed by (Buades and Morel, 2005) in the field of image processing, the Non Local Denoising (NLD). With a local geometric descriptor, we look for points that have similarities in order to reduce noise while preserving the surface details. We describe local geometry by MLS surfaces and we use a local reference frame invariant by rotation for denoising points. We present our results on synthetic and real data.
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https://hal-mines-paristech.archives-ouvertes.fr/hal-01097420
Contributor : Jean-Emmanuel Deschaud <>
Submitted on : Friday, December 19, 2014 - 3:51:28 PM
Last modification on : Thursday, September 24, 2020 - 5:04:02 PM
Long-term archiving on: : Monday, March 23, 2015 - 6:28:08 PM

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  • HAL Id : hal-01097420, version 1

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Jean-Emmanuel Deschaud, François Goulette. POINT CLOUD NON LOCAL DENOISING USING LOCAL SURFACE DESCRIPTOR SIMILARITY. PCV (Photogrammetric Computer Vision), Sep 2010, Paris, France. ⟨hal-01097420⟩

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