Semantic Classification of 3D Point Clouds with Multiscale Spherical Neighborhoods

Hugues Thomas 1 Jean-Emmanuel Deschaud 2 Beatriz Marcotegui 3 Francois Goulette 2 Yann Le Gall 4
1 STARS - Spatio-Temporal Activity Recognition Systems
CRISAM - Inria Sophia Antipolis - Méditerranée
4 Lab-STICC_ENIB_CID_TOMS
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance : UMR 6285
Abstract : This paper introduces a new definition of multiscale neighborhoods in 3D point clouds. This definition, based on spherical neighborhoods and proportional subsampling, allows the computation of features with a consistent geometrical meaning, which is not the case when using k-nearest neighbors. With an appropriate learning strategy, the proposed features can be used in a random forest to classify 3D points. In this semantic classification task, we show that our multiscale features outperform state-of-the-art features using the same experimental conditions. Furthermore, their classification power competes with more elaborate classification approaches including Deep Learning methods.
Type de document :
Communication dans un congrès
3DV 2018 - International Conference on 3D Vision, Sep 2018, Verone, Italy
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-01852565
Contributeur : Hugues Thomas <>
Soumis le : mercredi 1 août 2018 - 20:34:05
Dernière modification le : lundi 12 novembre 2018 - 11:03:10
Document(s) archivé(s) le : vendredi 2 novembre 2018 - 14:33:04

Fichier

3DV_2018_preprint.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-01852565, version 1

Citation

Hugues Thomas, Jean-Emmanuel Deschaud, Beatriz Marcotegui, Francois Goulette, Yann Le Gall. Semantic Classification of 3D Point Clouds with Multiscale Spherical Neighborhoods. 3DV 2018 - International Conference on 3D Vision, Sep 2018, Verone, Italy. 〈hal-01852565〉

Partager

Métriques

Consultations de la notice

297

Téléchargements de fichiers

221