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

With the development of segmentation and classification methods of 3D point clouds by machine-learning, more and more data are needed in quantity and quality (number of points, number of classes, quality of segmentation). 1 Figure 1: Part of our dataset (top: reflectance from blue(0) to red(255), middle: object label (different color for each), bottom: object class) 2 There are always more datasets of classification and segmentation of images, visual and LiDAR odometry or SLAM, detection of vehicles and pedestrians on videos, stereo-vision, optical flow, etc. But it is still difficult to find datasets of segmented and classified urban 3D point clouds. The only comparable datasets are the one described in section Available Datasets. Each of them have their advantages and disadvantages, but we estimate that none has the quality and quantity required for new issues such as deep learning methods. In section Our Dataset: Paris-Lille-3D, we present a new urban dataset that we have created, where the objects are sufficiently segmented that the task of segmentation can be learned very precisely. Our dataset can be found at the following address: http://caor-mines-paristech.fr/fr/paris-lille-