POINT CLOUD NON LOCAL DENOISING USING LOCAL SURFACE DESCRIPTOR SIMILARITY

Jean-Emmanuel Deschaud (1) , François Goulette (1)

1 CAOR - Centre de Robotique

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.

Mots clés: Point Cloud; Denoising; LIDAR; Descriptor; Mesh

Domaines: Robotique [cs.RO]; Vision par ordinateur et reconnaissance de formes [cs.CV]; Modélisation et simulation

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⟩