Paris-Lille-3D: a large and high-quality ground truth urban point cloud dataset for automatic segmentation and classification

Xavier Roynard (1) , Jean-Emmanuel Deschaud (1) , François Goulette (1)

1 CAOR - Centre de Robotique

This paper introduces a new Urban Point Cloud Dataset for Automatic Segmentation and Classification acquired by Mobile Laser Scanning (MLS). We describe how the dataset is obtained from acquisition to post-processing and labeling. This dataset can be used to learn classification algorithm, however, given that a great attention has been paid to the split between the different objects, this dataset can also be used to learn the segmentation. The dataset consists of around 2km of MLS point cloud acquired in two cities. The number of points and range of classes make us consider that it can be used to train Deep-Learning methods. Besides we show some results of automatic segmentation and classification. The dataset is available at: http://caor-mines-paristech.fr/fr/paris-lille-3d-dataset/.

Mots clés: Mobile Laser Scanning; Urban Point Cloud; Classification; Segmentation; Dataset

Domaines: Informatique [cs]; Apprentissage [cs.LG]; Informatique [cs]; Vision par ordinateur et reconnaissance de formes [cs.CV]

Xavier Roynard, Jean-Emmanuel Deschaud, François Goulette. Paris-Lille-3D: a large and high-quality ground truth urban point cloud dataset for automatic segmentation and classification. The International Journal of Robotics Research, 2018, ⟨10.1177/0278364918767506⟩. ⟨hal-01695873v2⟩