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

• Published in: The International journal of robotics research Vol. 37; no. 6; pp. 545 - 557
• Main Authors: Roynard, Xavier, Deschaud, Jean-Emmanuel, Goulette, François
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01-05-2018; SAGE PUBLICATIONS, INC
• Subjects: Classification; Computer Science; Computer Vision and Pattern Recognition; Datasets; Machine Learning; Object recognition; Post-processing; Segmentation; Three dimensional models; segmentation; mobile laser scanning; classification; dataset; Urban point cloud; Segmentation; Dataset; Mobile Laser Scanning; Classification; Urban Point Cloud
• ISSN: 0278-3649; 1741-3176
• DOI: 10.1177/0278364918767506

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 train pointwise classification algorithms; however, given that a great attention has been paid to the split between the different objects, this dataset can also be used to train the detection and segmentation of objects. The dataset consists of around
2
k
m
of MLS point cloud acquired in two cities. The number of points and range of classes mean that it can be used to train deep-learning methods. In addition, we show some results of automatic segmentation and classification. The dataset is available at: http://caor-mines-paristech.fr/fr/paris-lille-3d-dataset/.