mlsanityData to 14 Oct 2026

Deep Active Learning for Efficient Training of a LiDAR 3D Object Detector

arXiv 1901.10609 ↗First posted 29 Jan 2019cs.RO cs.CV cs.LGcited by 45

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Abstract (arXiv)

Training a deep object detector for autonomous driving requires a huge amount of labeled data. While recording data via on-board sensors such as camera or LiDAR is relatively easy, annotating data is very tedious and time-consuming, especially when dealing with 3D LiDAR points or radar data. Active learning has the potential to minimize human annotation efforts while maximizing the object detector's performance. In this work, we propose an active learning method to train a LiDAR 3D object detector with the least amount of labeled training data necessary. The detector leverages 2D region proposals generated from the RGB images to reduce the search space of objects and speed up the learning process. Experiments show that our proposed method works under different uncertainty estimations and query functions, and can save up to 60% of the labeling efforts while reaching the same network performance.

Key facts. Cited by 45 papers in the map (data through 14 Oct 2026). First posted on arXiv 29 Jan 2019.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 5; 2022: 9; 2023: 13; 2024: 8; 2025: 5; 2026: 5.

45 papers.

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  1. 2021Offboard 3D Object Detection from Point Cloud Sequencescited by 145
  2. 2021Active Learning for Deep Object Detection via Probabilistic Modelingcited by 79
  3. 2023Once Detected, Never Lost: Surpassing Human Performance in Offline LiDAR based 3D Object Detectioncited by 28
  4. 2023Exploring Active 3D Object Detection from a Generalization Perspectivecited by 26
  5. 2025Perspective-Invariant 3D Object Detectioncited by 18
  6. 2024The Why, When, and How to Use Active Learning in Large-Data-Driven 3D Object Detection for Safe Autonomous Driving: An Empirical Explorationcited by 17
  7. 2024ActiveAnno3D -- An Active Learning Framework for Multi-Modal 3D Object Detectioncited by 17
  8. 2022Semantic Segmentation with Active Semi-Supervised Learningcited by 16
  9. 2022Exploring Diversity-based Active Learning for 3D Object Detection in Autonomous Drivingcited by 16
  10. 2023KECOR: Kernel Coding Rate Maximization for Active 3D Object Detectioncited by 13
  11. 2023Monocular 3D Object Detection with LiDAR Guided Semi Supervised Active Learningcited by 10
  12. 2021Bayesian Active Learning for Sim-to-Real Robotic Perceptioncited by 8
33 more
  1. 2022ALBench: A Framework for Evaluating Active Learning in Object Detectioncited by 7
  2. 2022Deep Active Learning for Computer Vision: Past and Futurecited by 7
  3. 2023Deep Kernel Methods Learn Better: From Cards to Process Optimizationcited by 7
  4. 2024A Unified Approach Towards Active Learning and Out-of-Distribution Detectioncited by 7
  5. 2025Joint Out-of-Distribution Filtering and Data Discovery Active Learningcited by 7
  6. 2025Uncertainty Meets Diversity: A Comprehensive Active Learning Framework for Indoor 3D Object Detectioncited by 7
  7. 2023You Never Get a Second Chance To Make a Good First Impression: Seeding Active Learning for 3D Semantic Segmentationcited by 6
  8. 2023Stream-based Active Learning by Exploiting Temporal Properties in Perception with Temporal Predicted Losscited by 6
  9. 2023ReBound: An Open-Source 3D Bounding Box Annotation Tool for Active Learningcited by 4
  10. 2024UncertaintyTrack: Exploiting Detection and Localization Uncertainty in Multi-Object Trackingcited by 4
  11. 2022Object Detection as Probabilistic Set Predictioncited by 3
  12. 2023LMD: Light-weight Prediction Quality Estimation for Object Detection in Lidar Point Cloudscited by 3
  13. 2021Just Label What You Need: Fine-Grained Active Selection for Perception and Prediction through Partially Labeled Scenescited by 2
  14. 2022Query-based Hard-Image Retrieval for Object Detection at Test Timecited by 2
  15. 2023A Pre-trained Data Deduplication Model based on Active Learningcited by 2
  16. 2023ALE: A Simulation-Based Active Learning Evaluation Framework for the Parameter-Driven Comparison of Query Strategies for NLPcited by 2
  17. 2022Can I see an Example? Active Learning the Long Tail of Attributes and Relationscited by 1
  18. 2022Towards an Error-free Deep Occupancy Detector for Smart Camera Parking Systemcited by 1
  19. 2022MAViC: Multimodal Active Learning for Video Captioningcited by 1
  20. 2023Uncertainty Estimation in Instance Segmentation with Star-convex Shapescited by 1
  21. 2024Distribution Discrepancy and Feature Heterogeneity for Active 3D Object Detectioncited by 1
  22. 2024STONE: A Submodular Optimization Framework for Active 3D Object Detectioncited by 1
  23. 2026Active Learning for Decision Trees with Provable Guaranteescited by 1
  24. 2026Class-Distribution Guided Active Learning for 3D Occupancy Prediction in Autonomous Drivingcited by 1
  25. 2021Face-NMS: A Core-set Selection Approach for Efficient Face Recognitioncited by 0
  26. 2023Open-CRB: Towards Open World Active Learning for 3D Object Detectioncited by 0
  27. 2024Railway LiDAR semantic segmentation based on intelligent semi-automated data annotationcited by 0
  28. 2024TSceneJAL: Joint Active Learning of Traffic Scenes for 3D Object Detectioncited by 0
  29. 2025Breaking the SSL-AL Barrier: A Synergistic Semi-Supervised Active Learning Framework for 3D Object Detectioncited by 0
  30. 2025HeAL3D: Heuristical-enhanced Active Learning for 3D Object Detectioncited by 0
  31. 2026FuDU: A Fuzzy Dual-dimensional Uncertainty Framework for Streaming Active Learning in Industrial Defect Detectioncited by 0
  32. 2026Distance to Class Prototypes: Active Learning for Object Detectioncited by 0
  33. 2026GA-EIRFS: A Geometry-Augmented Repeat-Factor Sampling Method for Long-Tailed LiDAR 3D Object Detectioncited by 0