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

TL;DR

This paper presents a comprehensive urban point cloud dataset from Mobile Laser Scanning in Paris and Lille, designed for training and evaluating automatic segmentation and classification algorithms, with detailed acquisition and labeling procedures.

Contribution

It introduces a large, high-quality urban point cloud dataset with careful object separation, suitable for deep learning-based segmentation and classification tasks.

Findings

Dataset enables training of deep learning models

Automatic segmentation and classification results are demonstrated

Dataset covers 2 km of MLS data in two cities

Abstract

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/