mlsanityData to 14 Oct 2026

Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways

arXiv 2003.08284 ↗First posted 18 Mar 2020cs.CVcited by 77

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Abstract (arXiv)

Semantic segmentation of large-scale outdoor point clouds is essential for urban scene understanding in various applications, especially autonomous driving and urban high-definition (HD) mapping. With rapid developments of mobile laser scanning (MLS) systems, massive point clouds are available for scene understanding, but publicly accessible large-scale labeled datasets, which are essential for developing learning-based methods, are still limited. This paper introduces Toronto-3D, a large-scale urban outdoor point cloud dataset acquired by a MLS system in Toronto, Canada for semantic segmentation. This dataset covers approximately 1 km of point clouds and consists of about 78.3 million points with 8 labeled object classes. Baseline experiments for semantic segmentation were conducted and the results confirmed the capability of this dataset to train deep learning models effectively. Toronto-3D is released to encourage new research, and the labels will be improved and updated with feedback from the research community.

Key facts. Cited by 77 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Mar 2020.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 15; 2022: 9; 2023: 13; 2024: 13; 2025: 16; 2026: 11.

77 papers.

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  1. 2021KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3Dcited by 575
  2. 2021Panoptic nuScenes: A Large-Scale Benchmark for LiDAR Panoptic Segmentation and Trackingcited by 166
  3. 2023OpenIns3D: Snap and Lookup for 3D Open-vocabulary Instance Segmentationcited by 82
  4. 20213D Semantic Scene Completion: a Surveycited by 74
  5. 2024A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlookcited by 66
  6. 2021SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Cloudscited by 64
  7. 2022STPLS3D: A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point Cloud Datasetcited by 53
  8. 2021Learning Semantic Segmentation of Large-Scale Point Clouds with Random Samplingcited by 51
  9. 2022SensatUrban: Learning Semantics from Urban-Scale Photogrammetric Point Cloudscited by 50
  10. 2022EarthNets: Empowering AI in Earth Observationcited by 40
  11. 2025Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysiscited by 38
  12. 2023GrowSP: Unsupervised Semantic Segmentation of 3D Point Cloudscited by 34
38 more
  1. 2021BuildingNet: Learning to Label 3D Buildingscited by 33
  2. 2021Paris-CARLA-3D: A Real and Synthetic Outdoor Point Cloud Dataset for Challenging Tasks in 3D Mappingcited by 25
  3. 2023UrbanBIS: a Large-scale Benchmark for Fine-grained Urban Building Instance Segmentationcited by 24
  4. 2021SUM: A Benchmark Dataset of Semantic Urban Meshescited by 23
  5. 2023Open-sourced Data Ecosystem in Autonomous Driving: the Present and Futurecited by 23
  6. 2023CityRefer: Geography-aware 3D Visual Grounding Dataset on City-scale Point Cloud Datacited by 18
  7. 2022Online Segmentation of LiDAR Sequences: Dataset and Algorithmcited by 14
  8. 2022PointMCD: Boosting Deep Point Cloud Encoders via Multi-view Cross-modal Distillation for 3D Shape Recognitioncited by 10
  9. 2024ECLAIR: A High-Fidelity Aerial LiDAR Dataset for Semantic Segmentationcited by 10
  10. 2021Revisiting Point Cloud Simplification: A Learnable Feature Preserving Approachcited by 9
  11. 2022PointVST: Self-Supervised Pre-training for 3D Point Clouds via View-Specific Point-to-Image Translationcited by 7
  12. 2021OpenGF: An Ultra-Large-Scale Ground Filtering Dataset Built Upon Open ALS Point Clouds Around the Worldcited by 6
  13. 2021Automatic labelling of urban point clouds using data fusioncited by 6
  14. 2021UrbanNet: Leveraging Urban Maps for Long Range 3D Object Detectioncited by 6
  15. 2021diffConv: Analyzing Irregular Point Clouds with an Irregular Viewcited by 6
  16. 2024Deep Learning on 3D Semantic Segmentation: A Detailed Reviewcited by 6
  17. 2022LACV-Net: Semantic Segmentation of Large-Scale Point Cloud Scene via Local Adaptive and Comprehensive VLADcited by 5
  18. 2023TUM-FAcCADE: Reviewing and enriching point cloud benchmarks for faccade segmentationcited by 5
  19. 2025TUM2TWIN: Introducing the Large-Scale Multimodal Urban Digital Twin Benchmark Datasetcited by 5
  20. 2023Human Vision Based 3D Point Cloud Semantic Segmentation of Large-Scale Outdoor Scenecited by 4
  21. 2023Tinto: Multisensor Benchmark for 3D Hyperspectral Point Cloud Segmentation in the Geosciencescited by 4
  22. 2024Subspace Prototype Guidance for Mitigating Class Imbalance in Point Cloud Semantic Segmentationcited by 4
  23. 2024ZAHA: Introducing the Level of Facade Generalization and the Large-Scale Point Cloud Facade Semantic Segmentation Benchmark Datasetcited by 4
  24. 2025LiDAR Remote Sensing Meets Weak Supervision: Concepts, Methods, and Perspectivescited by 4
  25. 2022Semantic Segmentation for Point Cloud Scenes via Dilated Graph Feature Aggregation and Pyramid Decoderscited by 3
  26. 2023Small but Mighty: Enhancing 3D Point Clouds Semantic Segmentation with U-Next Frameworkcited by 3
  27. 2024Advancements in Point Cloud-Based 3D Defect Detection and Classification for Industrial Systems: A Comprehensive Surveycited by 3
  28. 20253DSES: an indoor Lidar point cloud segmentation dataset with real and pseudo-labels from a 3D modelcited by 3
  29. 2021Revisiting LiDAR Registration and Reconstruction: A Range Image Perspectivecited by 2
  30. 2023Dynamic Clustering Transformer Network for Point Cloud Segmentationcited by 2
  31. 2025Point Cloud Based Scene Segmentation: A Surveycited by 2
  32. 2025TrueCity: Real and Simulated Urban Data for Cross-Domain 3D Scene Understandingcited by 2
  33. 2021Simulated LiDAR Repositioning: a novel point cloud data augmentation methodcited by 1
  34. 2023Benchmarking Deep Learning Architectures for Urban Vegetation Point Cloud Semantic Segmentation from MLScited by 1
  35. 20233DFusion, A real-time 3D object reconstruction pipeline based on streamed instance segmented datacited by 1
  36. 2024OpenTrench3D: A Photogrammetric 3D Point Cloud Dataset for Semantic Segmentation of Underground Utilitiescited by 1
  37. 20243D Learnable Supertoken Transformer for LiDAR Point Cloud Scene Segmentationcited by 1
  38. 2024Multilateral Cascading Network for Semantic Segmentation of Large-Scale Outdoor Point Cloudscited by 1

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