mlsanityData to 7 Oct 2026

One Million Scenes for Autonomous Driving: ONCE Dataset

arXiv 2106.11037 ↗First posted 21 Jun 2021cs.CVcited by 218

Not on the map. Why?

Cited by 33 in End-to-end autonomous driving, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Current perception models in autonomous driving have become notorious for greatly relying on a mass of annotated data to cover unseen cases and address the long-tail problem. On the other hand, learning from unlabeled large-scale collected data and incrementally self-training powerful recognition models have received increasing attention and may become the solutions of next-generation industry-level powerful and robust perception models in autonomous driving. However, the research community generally suffered from data inadequacy of those essential real-world scene data, which hampers the future exploration of fully/semi/self-supervised methods for 3D perception. In this paper, we introduce the ONCE (One millioN sCenEs) dataset for 3D object detection in the autonomous driving scenario. The ONCE dataset consists of 1 million LiDAR scenes and 7 million corresponding camera images. The data is selected from 144 driving hours, which is 20x longer than the largest 3D autonomous driving dataset available (e.g. nuScenes and Waymo), and it is collected across a range of different areas, periods and weather conditions. To facilitate future research on exploiting unlabeled data for 3D detection, we additionally provide a benchmark in which we reproduce and evaluate a variety of self-supervised and semi-supervised methods on the ONCE dataset. We conduct extensive analyses on those methods and provide valuable observations on their performance related to the scale of used data. Data, code, and more information are available at https://once-for-auto-driving.github.io/index.html.

Key facts. Cited by 218 papers in the map (data through 7 Oct 2026). Builds on 48 of its 59 references that are in the map. First posted on arXiv 21 Jun 2021.

Builds on

48 of 59 references.

References · 48

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  5. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,485
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36 more
  1. 2016Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzlescited by 1,379
  2. 2015Learning Transferable Features with Deep Adaptation Networkscited by 1,323
  3. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
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  12. 2019PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detectioncited by 890
  13. 2019Argoverse: 3D Tracking and Forecasting with Rich Mapscited by 728
  14. 2016Multi-View 3D Object Detection Network for Autonomous Drivingcited by 721
  15. 2017Frustum PointNets for 3D Object Detection from RGB-D Datacited by 640
  16. 2019Self-labelling via simultaneous clustering and representation learningcited by 525
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  18. 2017Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning resultscited by 470
  19. 2019PointPainting: Sequential Fusion for 3D Object Detectioncited by 449
  20. 2020Meta Pseudo Labelscited by 372
  21. 2019PIXOR: Real-time 3D Object Detection from Point Cloudscited by 358
  22. 2019ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoringcited by 349
  23. 2017Joint 3D Proposal Generation and Object Detection from View Aggregationcited by 325
  24. 2021Self-Supervised Pretraining of 3D Features on any Point-Cloudcited by 217
  25. 2020Multi-Task Multi-Sensor Fusion for 3D Object Detectioncited by 203
  26. 2020A2D2: Audi Autonomous Driving Datasetcited by 189
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  30. 2020Train in Germany, Test in The USA: Making 3D Object Detectors Generalizecited by 150
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 4; 2022: 35; 2023: 57; 2024: 49; 2025: 49; 2026: 24.

218 papers.

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  1. 2023Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecastingcited by 701
  2. 2021Deep Long-Tailed Learning: A Surveycited by 416
  3. 2022DAIR-V2X: A Large-Scale Dataset for Vehicle-Infrastructure Cooperative 3D Object Detectioncited by 270
  4. 2021Voxel Transformer for 3D Object Detectioncited by 247
  5. 2023DriveMLM: Aligning Multi-Modal Large Language Models with Behavioral Planning States for Autonomous Drivingcited by 233
  6. 2024GenAD: Generalized Predictive Model for Autonomous Drivingcited by 150
  7. 2022SHIFT: A Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptationcited by 146
  8. 2023EmbodiedScan: A Holistic Multi-Modal 3D Perception Suite Towards Embodied AIcited by 146
  9. 2025OmniDrive: A Holistic Vision-Language Dataset for Autonomous Driving with Counterfactual Reasoningcited by 142
  10. 20223D Object Detection for Autonomous Driving: A Comprehensive Surveycited by 132
  11. 2022Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Cloudscited by 131
  12. 2023Vision Language Models in Autonomous Driving: A Survey and Outlookcited by 124
38 more
  1. 2022Delving into the Devils of Bird's-eye-view Perception: A Review, Evaluation and Recipecited by 122
  2. 2023Reason2Drive: Towards Interpretable and Chain-based Reasoning for Autonomous Drivingcited by 122
  3. 2023V2X-Seq: A Large-Scale Sequential Dataset for Vehicle-Infrastructure Cooperative Perception and Forecastingcited by 114
  4. 2023Benchmarking Robustness of 3D Object Detection to Common Corruptions in Autonomous Drivingcited by 112
  5. 2022CODA: A Real-World Road Corner Case Dataset for Object Detection in Autonomous Drivingcited by 100
  6. 2022Differentiable Raycasting for Self-supervised Occupancy Forecastingcited by 83
  7. 2023Zenseact Open Dataset: A large-scale and diverse multimodal dataset for autonomous drivingcited by 83
  8. 2021Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detectioncited by 79
  9. 2022Rope3D: TheRoadside Perception Dataset for Autonomous Driving and Monocular 3D Object Detection Taskcited by 79
  10. 2023Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveyscited by 75
  11. 2022Multi-modal Sensor Fusion for Auto Driving Perception: A Surveycited by 73
  12. 2023SSCBench: A Large-Scale 3D Semantic Scene Completion Benchmark for Autonomous Drivingcited by 72
  13. 2023CLIP2: Contrastive Language-Image-Point Pretraining from Real-World Point Cloud Datacited by 69
  14. 2022ALSO: Automotive Lidar Self-supervision by Occupancy estimationcited by 68
  15. 2024A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlookcited by 65
  16. 2025Impromptu VLA: Open Weights and Open Data for Driving Vision-Language-Action Modelscited by 60
  17. 2022Occupancy-MAE: Self-supervised Pre-training Large-scale LiDAR Point Clouds with Masked Occupancy Autoencoderscited by 57
  18. 2022GD-MAE: Generative Decoder for MAE Pre-training on LiDAR Point Cloudscited by 57
  19. 2022Unsupervised Point Cloud Representation Learning with Deep Neural Networks: A Surveycited by 54
  20. 2023Synthetic Datasets for Autonomous Driving: A Surveycited by 48
  21. 2025Pure Vision Language Action (VLA) Models: A Comprehensive Surveycited by 46
  22. 2022Semi-supervised 3D Object Detection with Proficient Teacherscited by 45
  23. 2022ONCE-3DLanes: Building Monocular 3D Lane Detectioncited by 44
  24. 2023LATR: 3D Lane Detection from Monocular Images with Transformercited by 44
  25. 2022ProposalContrast: Unsupervised Pre-training for LiDAR-based 3D Object Detectioncited by 43
  26. 2023GeoMAE: Masked Geometric Target Prediction for Self-supervised Point Cloud Pre-Trainingcited by 38
  27. 2024LION: Linear Group RNN for 3D Object Detection in Point Cloudscited by 38
  28. 2025Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysiscited by 37
  29. 2022LidarCLIP or: How I Learned to Talk to Point Cloudscited by 33
  30. 2022aiMotive Dataset: A Multimodal Dataset for Robust Autonomous Driving with Long-Range Perceptioncited by 29
  31. 2023Data-driven Traffic Simulation: A Comprehensive Reviewcited by 29
  32. 2023Towards Robust Robot 3D Perception in Urban Environments: The UT Campus Object Datasetcited by 28
  33. 2022DBQ-SSD: Dynamic Ball Query for Efficient 3D Object Detectioncited by 25
  34. 2023Grid-Centric Traffic Scenario Perception for Autonomous Driving: A Comprehensive Reviewcited by 25
  35. 2024DrivingDojo Dataset: Advancing Interactive and Knowledge-Enriched Driving World Modelcited by 25
  36. 2023BEVContrast: Self-Supervision in BEV Space for Automotive Lidar Point Cloudscited by 24
  37. 2024Forging Vision Foundation Models for Autonomous Driving: Challenges, Methodologies, and Opportunitiescited by 24
  38. 2023LiDAR-based 4D Occupancy Completion and Forecastingcited by 23

50 most cited of 218 All 218 (JSON)