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.
Liste complète des métadonnées

Littérature citée [16 références]  Voir  Masquer  Télécharger

https://hal-mines-paristech.archives-ouvertes.fr/hal-01097420
Contributeur : Jean-Emmanuel Deschaud <>
Soumis le : vendredi 19 décembre 2014 - 15:51:28
Dernière modification le : lundi 12 novembre 2018 - 10:59:04
Document(s) archivé(s) le : lundi 23 mars 2015 - 18:28:08

Fichier

PCV_2010.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-01097420, version 1

Citation

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⟩

Partager

Métriques

Consultations de la notice

226

Téléchargements de fichiers

209