A Fast and Accurate Plane Detection Algorithm for Large Noisy Point Clouds Using Filtered Normals and Voxel Growing - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année :

A Fast and Accurate Plane Detection Algorithm for Large Noisy Point Clouds Using Filtered Normals and Voxel Growing

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

Résumé

With the improvement of 3D scanners, we produce point clouds with more and more points often exceeding millions of points. Then we need a fast and accurate plane detection algorithm to reduce data size. In this article, we present a fast and accurate algorithm to detect planes in unorganized point clouds using filtered normals and voxel growing. Our work is based on a first step in estimating better normals at the data points, even in the presence of noise. In a second step, we compute a score of local plane in each point. Then, we select the best local seed plane and in a third step start a fast and robust region growing by voxels we call voxel growing. We have evaluated and tested our algorithm on different kinds of point cloud and compared its performance to other algorithms.
Fichier principal
Vignette du fichier
3DPVT_2010.pdf (676.36 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01097361 , version 1 (19-12-2014)

Identifiants

  • HAL Id : hal-01097361 , version 1

Citer

Jean-Emmanuel Deschaud, François Goulette. A Fast and Accurate Plane Detection Algorithm for Large Noisy Point Clouds Using Filtered Normals and Voxel Growing. 3DPVT, May 2010, Paris, France. ⟨hal-01097361⟩
1219 Consultations
10430 Téléchargements

Partager

Gmail Facebook Twitter LinkedIn More