mlsanityData to 7 Oct 2026

Exploring Diversity-based Active Learning for 3D Object Detection in Autonomous Driving

arXiv 2205.07708 ↗First posted 16 May 2022cs.CVcited by 16

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Abstract (arXiv)

3D object detection has recently received much attention due to its great potential in autonomous vehicle (AV). The success of deep learning based object detectors relies on the availability of large-scale annotated datasets, which is time-consuming and expensive to compile, especially for 3D bounding box annotation. In this work, we investigate diversity-based active learning (AL) as a potential solution to alleviate the annotation burden. Given limited annotation budget, only the most informative frames and objects are automatically selected for human to annotate. Technically, we take the advantage of the multimodal information provided in an AV dataset, and propose a novel acquisition function that enforces spatial and temporal diversity in the selected samples. We benchmark the proposed method against other AL strategies under realistic annotation cost measurement, where the realistic costs for annotating a frame and a 3D bounding box are both taken into consideration. We demonstrate the effectiveness of the proposed method on the nuScenes dataset and show that it outperforms existing AL strategies significantly. Code is available at https://github.com/Linkon87/Exploring-Diversity-based-Active-Learning-for-3D-Object-Detection-in-Autonomous-Driving

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 38 of its 69 references that are in the map. First posted on arXiv 16 May 2022.

Builds on

38 of 69 references.

References · 38

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  2. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,485
  3. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
  4. 2015SSD: Single Shot MultiBox Detectorcited by 3,125
  5. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,220
  6. 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,707
  7. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
  8. 2020Center-based 3D Object Detection and Trackingcited by 1,143
  9. 2018PointRCNN: 3D Object Proposal Generation and Detection from Point Cloudcited by 939
  10. 2017Deep Bayesian Active Learning with Image Datacited by 812
  11. 2016Multi-View 3D Object Detection Network for Autonomous Drivingcited by 721
  12. 2019Deep Hough Voting for 3D Object Detection in Point Cloudscited by 648
26 more
  1. 2017Frustum PointNets for 3D Object Detection from RGB-D Datacited by 640
  2. 2019Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Boundscited by 602
  3. 2020A Survey of Deep Active Learningcited by 547
  4. 2019PointPainting: Sequential Fusion for 3D Object Detectioncited by 449
  5. 20203DSSD: Point-based 3D Single Stage Object Detectorcited by 440
  6. 2019Class-balanced Grouping and Sampling for Point Cloud 3D Object Detectioncited by 350
  7. 2019Learning Loss for Active Learningcited by 335
  8. 2019Variational Adversarial Active Learningcited by 284
  9. 2015Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inferencecited by 159
  10. 2020Cold-start Active Learning through Self-supervised Language Modelingcited by 128
  11. 2019Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detectioncited by 124
  12. 2019Consistency-based Semi-supervised Active Learning: Towards Minimizing Labeling Costcited by 114
  13. 2019Object as Hotspots: An Anchor-Free 3D Object Detection Approach via Firing of Hotspotscited by 84
  14. 2021Multiple instance active learning for object detectioncited by 77
  15. 2019Active Learning for Deep Detection Neural Networkscited by 62
  16. 2022Entropy-based Active Learning for Object Detection with Progressive Diversity Constraintcited by 53
  17. 2021ReDAL: Region-based and Diversity-aware Active Learning for Point Cloud Semantic Segmentationcited by 52
  18. 2018Active Learning for Deep Object Detectioncited by 49
  19. 2018Localization-Aware Active Learning for Object Detectioncited by 48
  20. 2018CEREALS - Cost-Effective REgion-based Active Learning for Semantic Segmentationcited by 47
  21. 2019Deep Active Learning for Efficient Training of a LiDAR 3D Object Detectorcited by 45
  22. 2021Consistency-based Active Learning for Object Detectioncited by 32
  23. 2017Dropout Inference in Bayesian Neural Networks with Alpha-divergencescited by 28
  24. 2021TALISMAN: Targeted Active Learning for Object Detection with Rare Classes and Slices using Submodular Mutual Informationcited by 27
  25. 2017Active Self-Paced Learning for Cost-Effective and Progressive Face Identificationcited by 19
  26. 2021Label-Efficient Point Cloud Semantic Segmentation: An Active Learning Approachcited by 17

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 3; 2024: 7; 2025: 4; 2026: 1.

16 papers.

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  1. 2024Towards Explainable, Safe Autonomous Driving with Language Embeddings for Novelty Identification and Active Learning: Framework and Experimental Analysis with Real-World Data Setscited by 25
  2. 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
  3. 2024ActiveAnno3D -- An Active Learning Framework for Multi-Modal 3D Object Detectioncited by 17
  4. 2024ActiveAD: Planning-Oriented Active Learning for End-to-End Autonomous Drivingcited by 16
  5. 2023BaSAL: Size-Balanced Warm Start Active Learning for LiDAR Semantic Segmentationcited by 8
  6. 2023Deep Kernel Methods Learn Better: From Cards to Process Optimizationcited by 7
  7. 2024A Unified Approach Towards Active Learning and Out-of-Distribution Detectioncited by 7
  8. 2025Uncertainty Meets Diversity: A Comprehensive Active Learning Framework for Indoor 3D Object Detectioncited by 7
  9. 2024Human-In-The-Loop Machine Learning for Safe and Ethical Autonomous Vehicles: Principles, Challenges, and Opportunitiescited by 6
  10. 2023ReBound: An Open-Source 3D Bounding Box Annotation Tool for Active Learningcited by 4
  11. 2022MUS-CDB: Mixed Uncertainty Sampling with Class Distribution Balancing for Active Annotation in Aerial Object Detectioncited by 3
  12. 2025To Label or Not to Label: PALM -- A Predictive Model for Evaluating Sample Efficiency in Active Learning Modelscited by 2
4 more
  1. 2025Inconsistency-based Active Learning for LiDAR Object Detectioncited by 1
  2. 2025Table Detection with Active Learningcited by 1
  3. 2026Class-Distribution Guided Active Learning for 3D Occupancy Prediction in Autonomous Drivingcited by 1
  4. 2024TSceneJAL: Joint Active Learning of Traffic Scenes for 3D Object Detectioncited by 0