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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].
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  • HAL Id : hal-01939229, version 1

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

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