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Weakly supervised segmentation-aided classification of urban scenes from 3D LiDAR point clouds

Stéphane Guinard 1 Loic Landrieu 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 anthropic 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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Stéphane Guinard, Loic Landrieu. Weakly supervised segmentation-aided classification of urban scenes from 3D LiDAR point clouds. ISPRS Workshop 2017, Jun 2017, Hannover, Germany. ⟨hal-01497548v3⟩

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