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

By Xavier Roynard, Jean-Emmanuel Deschaud, François Goulette. Int. J. Robotics Res., 2017

330 citations recorded by the source corpus.

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/.

Subfields: Computational Mechanics, Environmental Engineering, Geology

View Paper: https://www.semanticscholar.org/paper/4ae6296f89b6d57785d63d0b4a7d0e89bf6d2e84

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Citations in the corpus

Semantic Segmentation on Swiss3DCities: A Benchmark Study on Aerial Photogrammetric 3D Pointcloud Dataset (2020, background): “Most existing datasets [27], [3], [32] for outdoor 3D semantic segmentation are motivated by real-time autonomous driving applications, and are therefore acquired at low resolution by street-level Light Detection and Ranging (LiDAR) sensors; this yields incomplete point clouds (for example, areas fa”
PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation (2020, methodology): “To our knowledge, there are only three so far: the Audi dataset [10], Paris-Lille-3D [26] and the Semantic KITTI dataset [1].”
The Hessigheim 3D (H3D) Benchmark on Semantic Segmentation of High-Resolution 3D Point Clouds and Textured Meshes from UAV LiDAR and Multi-View-Stereo (2021, background): “Although Mobile Laser Scanning (MLS) point clouds of typical urban scenes are often provided as stand-alone products (Roynard et al., 2018; Munoz et al., 2009; Hackel et al., 2017), the concurrent availability of LiDAR data and imagery in the form of meshes is often pursued (Riemenschneider et al.,…”
SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences (2019, methodology): “Paris-Lille-3D [47] 3 143 9 (50) Velodyne HDL-32E point-wise 7”
A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities (2022, background): “Really scence, Satellite and medical image Cross-domain few-shot learning MIT Paris-Lille-3D Roynard et al. [33] 450000000/50 450000000/380000000 Point cloud Few-shot Semantic Segmentation CC BY-NC-ND 3.0 N-Digit MNIST Oh et al. [34] -/- -/- Character Metric Learning Apache License SUN397 Oh et al. ”
KPConv: Flexible and Deformable Convolution for Point Clouds (2019, background): “Scannet [6], for indoor cluttered scenes, S3DIS [1], for indoor large spaces, Semantic3D [11], for outdoor fixed scans, and NPM3D [31], for outdoor mobile scans.”
Campus3D: A Photogrammetry Point Cloud Benchmark for Hierarchical Understanding of Outdoor Scene (2020, methodology): “Firstly, the datasets may face various limitations from their sources which are are either RGB-D images [1, 9, 10, 28] or light detection and ranging (LiDAR) based mobile laser scanning (MLS) [3, 25, 26, 31] and terrestrial laser scanning (TLS) [14].”
Review: deep learning on 3D point clouds (2020, methodology): “The Nuage de Points et Modélisation 3D (NPM3D) dataset was developed by PSL Research University [128].”