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Pré-segmentation pour la classification faiblement supervisée de scènes urbaines à partir de nuages de points 3D LIDAR

Stéphane Guinard 1 Loic Landrieu 1 Bruno Vallet 1
1 MATIS - Méthodes d'Analyses pour le Traitement d'Images et la Stéréorestitution
LaSTIG - Laboratoire des Sciences et Technologies de l'Information Géographique
Abstract : We consider the problem of the semantic classification of 3D LiDAR point clouds obtained from urban scenes when the training set is limited. We propose a non-parametric segmentation model for urban scenes composed of anthro-pic objects of simple shapes, partionning the scene into geometrically-homogeneous segments which size is determined by the local complexity. This segmentation can be integrated into a conditional random field classifier (CRF) in order to capture the high-level structure of the scene. For each cluster, this allows us to aggregate the noisy predictions of a weakly-supervised classifier to produce a higher confidence data term. We demonstrate the improvement provided by our method over two publicly-available large-scale data sets.
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https://hal.archives-ouvertes.fr/hal-01499571
Contributor : Loic Landrieu <>
Submitted on : Monday, April 3, 2017 - 5:56:39 PM
Last modification on : Wednesday, September 23, 2020 - 2:34:05 PM
Long-term archiving on: : Tuesday, July 4, 2017 - 12:27:03 PM

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  • HAL Id : hal-01499571, version 1

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Stéphane Guinard, Loic Landrieu, Bruno Vallet. Pré-segmentation pour la classification faiblement supervisée de scènes urbaines à partir de nuages de points 3D LIDAR. 2017. ⟨hal-01499571v1⟩

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