mlsanityData to 14 Oct 2026

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

arXiv 1712.00032 ↗First posted 30 Nov 2017cs.LG cs.CV stat.MLcited by 65

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Abstract (arXiv)

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/

Key facts. Cited by 65 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 Nov 2017.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 19; 2022: 11; 2023: 12; 2024: 11; 2025: 7; 2026: 5.

65 papers.

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  1. 2022A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunitiescited by 163
  2. 2023OpenIns3D: Snap and Lookup for 3D Open-vocabulary Instance Segmentationcited by 82
  3. 20213D Semantic Scene Completion: a Surveycited by 74
  4. 2024A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlookcited by 66
  5. 2021SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Cloudscited by 64
  6. 2023Segment3D: Learning Fine-Grained Class-Agnostic 3D Segmentation without Manual Labelscited by 64
  7. 2021Learning 3D Semantic Segmentation with only 2D Image Supervisioncited by 60
  8. 2022Contrastive Boundary Learning for Point Cloud Segmentationcited by 56
  9. 2022STPLS3D: A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point Cloud Datasetcited by 53
  10. 2021Learning Semantic Segmentation of Large-Scale Point Clouds with Random Samplingcited by 51
  11. 2022SensatUrban: Learning Semantics from Urban-Scale Photogrammetric Point Cloudscited by 50
  12. 2021A Survey on Deep Domain Adaptation for LiDAR Perceptioncited by 47
38 more
  1. 2022EarthNets: Empowering AI in Earth Observationcited by 40
  2. 2021The Hessigheim 3D (H3D) Benchmark on Semantic Segmentation of High-Resolution 3D Point Clouds and Textured Meshes from UAV LiDAR and Multi-View-Stereocited by 37
  3. 2021BuildingNet: Learning to Label 3D Buildingscited by 33
  4. 2021Deep Learning Based 3D Segmentation: A Surveycited by 30
  5. 2021LiDAR-based Recurrent 3D Semantic Segmentation with Temporal Memory Alignmentcited by 26
  6. 2021Paris-CARLA-3D: A Real and Synthetic Outdoor Point Cloud Dataset for Challenging Tasks in 3D Mappingcited by 25
  7. 2023UrbanBIS: a Large-scale Benchmark for Fine-grained Urban Building Instance Segmentationcited by 24
  8. 2021SUM: A Benchmark Dataset of Semantic Urban Meshescited by 23
  9. 2023CityRefer: Geography-aware 3D Visual Grounding Dataset on City-scale Point Cloud Datacited by 18
  10. 2022Online Segmentation of LiDAR Sequences: Dataset and Algorithmcited by 14
  11. 2024A comprehensive overview of deep learning techniques for 3D point cloud classification and semantic segmentationcited by 14
  12. 2022PointMCD: Boosting Deep Point Cloud Encoders via Multi-view Cross-modal Distillation for 3D Shape Recognitioncited by 10
  13. 2024ECLAIR: A High-Fidelity Aerial LiDAR Dataset for Semantic Segmentationcited by 10
  14. 2021Exploiting Local Geometry for Feature and Graph Construction for Better 3D Point Cloud Processing with Graph Neural Networkscited by 9
  15. 2023A Review of Panoptic Segmentation for Mobile Mapping Point Cloudscited by 8
  16. 2023Towards accurate instance segmentation in large-scale LiDAR point cloudscited by 8
  17. 2024Panoptic Perception: A Novel Task and Fine-grained Dataset for Universal Remote Sensing Image Interpretationcited by 8
  18. 2021DLA-Net: Learning Dual Local Attention Features for Semantic Segmentation of Large-Scale Building Facade Point Cloudscited by 7
  19. 2021OpenGF: An Ultra-Large-Scale Ground Filtering Dataset Built Upon Open ALS Point Clouds Around the Worldcited by 6
  20. 2021Automatic labelling of urban point clouds using data fusioncited by 6
  21. 2024Deep Learning on 3D Semantic Segmentation: A Detailed Reviewcited by 6
  22. 2022One Class One Click: Quasi Scene-level Weakly Supervised Point Cloud Semantic Segmentation with Active Learningcited by 5
  23. 2023TUM-FAcCADE: Reviewing and enriching point cloud benchmarks for faccade segmentationcited by 5
  24. 2023Knowledge Distillation from 3D to Bird's-Eye-View for LiDAR Semantic Segmentationcited by 4
  25. 2023Urban GeoBIM construction by integrating semantic LiDAR point clouds with as-designed BIM modelscited by 4
  26. 2024ZAHA: Introducing the Level of Facade Generalization and the Large-Scale Point Cloud Facade Semantic Segmentation Benchmark Datasetcited by 4
  27. 2023COVERED, CollabOratiVE Robot Environment Dataset for 3D Semantic segmentationcited by 3
  28. 2023Arbitrary point cloud upsampling via Dual Back-Projection Networkcited by 3
  29. 2024Advancements in Point Cloud-Based 3D Defect Detection and Classification for Industrial Systems: A Comprehensive Surveycited by 3
  30. 20253DSES: an indoor Lidar point cloud segmentation dataset with real and pseudo-labels from a 3D modelcited by 3
  31. 2022Multi-scale Network with Attentional Multi-resolution Fusion for Point Cloud Semantic Segmentationcited by 2
  32. 2023Instance-incremental Scene Graph Generation from Real-world Point Clouds via Normalizing Flowscited by 2
  33. 2024Open-World Panoptic Segmentationcited by 2
  34. 2025Point Cloud Based Scene Segmentation: A Surveycited by 2
  35. 2025TrueCity: Real and Simulated Urban Data for Cross-Domain 3D Scene Understandingcited by 2
  36. 2021Simulated LiDAR Repositioning: a novel point cloud data augmentation methodcited by 1
  37. 2021Neighborhood Spatial Aggregation MC Dropout for Efficient Uncertainty-aware Semantic Segmentation in Point Cloudscited by 1
  38. 2022Descriptellation: Deep Learned Constellation Descriptorscited by 1

50 most cited of 65 All 65 (JSON)