Skip to Main content Skip to Navigation
Conference papers

Segmentation Sémantique à Grande Echelle par Graphe de Superpoints

Loic Landrieu 1, 2 Martin Simonovsky 3, 2, 4 
1 MATIS - Méthodes d'Analyses pour le Traitement d'Images et la Stéréorestitution
LaSTIG - Laboratoire des Sciences et Technologies de l'Information Géographique
2 imagine [Marne-la-Vallée]
LIGM - Laboratoire d'Informatique Gaspard-Monge, ENPC - École des Ponts ParisTech
Abstract : We propose a novel deep learning-based framework to tackle the challenge of semantic segmentation of large-scale point clouds of millions of points. We argue that the organization of 3D point clouds as interconnected object parts can be efficiently captured by a structure called superpoint graph (SPG). Our framework sets a new state of the art for segmenting outdoor LiDAR scans (+11.9 and +8.8 mIoU points for both Semantic3D test sets [13]), as well as indoor scans (+12.4 mIoU points for the S3DIS dataset [2]). This is a french translation of the article [25].
Complete list of metadata

Cited literature [43 references]  Display  Hide  Download
Contributor : Loic Landrieu Connect in order to contact the contributor
Submitted on : Thursday, November 29, 2018 - 11:58:34 AM
Last modification on : Saturday, January 15, 2022 - 3:56:40 AM


Files produced by the author(s)


  • HAL Id : hal-01939229, version 1


Loic Landrieu, Martin Simonovsky. Segmentation Sémantique à Grande Echelle par Graphe de Superpoints. RFIAP, Jun 2018, Marne-la-Vallée, France. ⟨hal-01939229⟩



Record views


Files downloads