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POINT CLOUD NON LOCAL DENOISING USING LOCAL SURFACE DESCRIPTOR SIMILARITY

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Jean-Emmanuel Deschaud
François Goulette

Résumé

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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Dates et versions

hal-01097420 , version 1 (19-12-2014)

Identifiants

  • HAL Id : hal-01097420 , version 1

Citer

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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