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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
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Submitted on : Thursday, April 13, 2017 - 10:43:46 AM
Last modification on : Tuesday, May 12, 2020 - 8:28:22 AM

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  • HAL Id : hal-01499571, version 2

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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-01499571v2⟩

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