mlsanityData to 7 Oct 2026

Inconsistency-based Active Learning for LiDAR Object Detection

arXiv 2505.00511 ↗First posted 1 May 2025cs.CVcited by 1

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Abstract (arXiv)

Deep learning models for object detection in autonomous driving have recently achieved impressive performance gains and are already being deployed in vehicles worldwide. However, current models require increasingly large datasets for training. Acquiring and labeling such data is costly, necessitating the development of new strategies to optimize this process. Active learning is a promising approach that has been extensively researched in the image domain. In our work, we extend this concept to the LiDAR domain by developing several inconsistency-based sample selection strategies and evaluate their effectiveness in various settings. Our results show that using a naive inconsistency approach based on the number of detected boxes, we achieve the same mAP as the random sampling strategy with 50% of the labeled data.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 15 of its 24 references that are in the map. First posted on arXiv 1 May 2025.

Builds on

15 of 24 references.

References · 15

  1. 2020FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidencecited by 2,080
  2. 2020A Survey of Deep Active Learningcited by 547
  3. 2019Consistency-based Semi-supervised Active Learning: Towards Minimizing Labeling Costcited by 114
  4. 2021Active Learning for Deep Object Detection via Probabilistic Modelingcited by 79
  5. 2020Scalable Active Learning for Object Detectioncited by 58
  6. 2022Entropy-based Active Learning for Object Detection with Progressive Diversity Constraintcited by 53
  7. 2021Semi-Supervised Active Learning with Temporal Output Discrepancycited by 50
  8. 2018Localization-Aware Active Learning for Object Detectioncited by 48
  9. 2021Not All Labels Are Equal: Rationalizing The Labeling Costs for Training Object Detectioncited by 27
  10. 2024ActiveAnno3D -- An Active Learning Framework for Multi-Modal 3D Object Detectioncited by 17
  11. 2022Semantic Segmentation with Active Semi-Supervised Learningcited by 16
  12. 2022Exploring Diversity-based Active Learning for 3D Object Detection in Autonomous Drivingcited by 16
3 more
  1. 2023Monocular 3D Object Detection with LiDAR Guided Semi Supervised Active Learningcited by 10
  2. 2023MoBYv2AL: Self-supervised Active Learning for Image Classificationcited by 3
  3. 2023Active Semi-Supervised Learning by Exploring Per-Sample Uncertainty and Consistencycited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Online Monitoring Framework for Automotive Time Series Data using JEPA Embeddingscited by 0