mlsanityData to 7 Oct 2026

DDS3D: Dense Pseudo-Labels with Dynamic Threshold for Semi-Supervised 3D Object Detection

arXiv 2303.05079 ↗First posted 9 Mar 2023cs.CVcited by 18

Not on the map. Why?

Abstract (arXiv)

In this paper, we present a simple yet effective semi-supervised 3D object detector named DDS3D. Our main contributions have two-fold. On the one hand, different from previous works using Non-Maximal Suppression (NMS) or its variants for obtaining the sparse pseudo labels, we propose a dense pseudo-label generation strategy to get dense pseudo-labels, which can retain more potential supervision information for the student network. On the other hand, instead of traditional fixed thresholds, we propose a dynamic threshold manner to generate pseudo-labels, which can guarantee the quality and quantity of pseudo-labels during the whole training process. Benefiting from these two components, our DDS3D outperforms the state-of-the-art semi-supervised 3d object detection with mAP of 3.1% on the pedestrian and 2.1% on the cyclist under the same configuration of 1% samples. Extensive ablation studies on the KITTI dataset demonstrate the effectiveness of our DDS3D. The code and models will be made publicly available at https://github.com/hust-jy/DDS3D

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 41 of its 51 references that are in the map. First posted on arXiv 9 Mar 2023.

Builds on

41 of 51 references.

References · 41

  1. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  2. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
  3. 2020FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidencecited by 2,080
  4. 2019MixMatch: A Holistic Approach to Semi-Supervised Learningcited by 1,390
  5. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
  6. 2020Center-based 3D Object Detection and Trackingcited by 1,143
  7. 2016Temporal Ensembling for Semi-Supervised Learningcited by 1,054
  8. 2018PointRCNN: 3D Object Proposal Generation and Detection from Point Cloudcited by 939
  9. 20173D Semantic Segmentation with Submanifold Sparse Convolutional Networkscited by 746
  10. 2019Deep Hough Voting for 3D Object Detection in Point Cloudscited by 648
  11. 2017Frustum PointNets for 3D Object Detection from RGB-D Datacited by 640
  12. 2021FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labelingcited by 537
29 more
  1. 2017Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning resultscited by 470
  2. 2016Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learningcited by 441
  3. 20203DSSD: Point-based 3D Single Stage Object Detectorcited by 440
  4. 2020Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detectioncited by 409
  5. 2019ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoringcited by 349
  6. 2021End-to-End Semi-Supervised Object Detection with Soft Teachercited by 315
  7. 2021Unbiased Teacher for Semi-Supervised Object Detectioncited by 305
  8. 2021In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learningcited by 301
  9. 2020A Simple Semi-Supervised Learning Framework for Object Detectioncited by 282
  10. 2015Semi-Supervised Learning with Ladder Networkscited by 246
  11. 2020EPNet: Enhancing Point Features with Image Semantics for 3D Object Detectioncited by 164
  12. 2021Offboard 3D Object Detection from Point Cloud Sequencescited by 144
  13. 2022Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Cloudscited by 131
  14. 2021SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloudcited by 119
  15. 2021Humble Teachers Teach Better Students for Semi-Supervised Object Detectioncited by 115
  16. 2021Instant-Teaching: An End-to-End Semi-Supervised Object Detection Frameworkcited by 112
  17. 2019TANet: Robust 3D Object Detection from Point Clouds with Triple Attentioncited by 108
  18. 2019SESS: Self-Ensembling Semi-Supervised 3D Object Detectioncited by 89
  19. 20203DIoUMatch: Leveraging IoU Prediction for Semi-Supervised 3D Object Detectioncited by 89
  20. 2022Unbiased Teacher v2: Semi-supervised Object Detection for Anchor-free and Anchor-based Detectorscited by 69
  21. 2022PseCo: Pseudo Labeling and Consistency Training for Semi-Supervised Object Detectioncited by 51
  22. 2021EPNet++: Cascade Bi-directional Fusion for Multi-Modal 3D Object Detectioncited by 49
  23. 2022Dense Teacher: Dense Pseudo-Labels for Semi-supervised Object Detectioncited by 48
  24. 2021Rethinking Pseudo Labels for Semi-Supervised Object Detectioncited by 45
  25. 2021FGR: Frustum-Aware Geometric Reasoning for Weakly Supervised 3D Vehicle Detectioncited by 29
  26. 2022DetMatch: Two Teachers are Better Than One for Joint 2D and 3D Semi-Supervised Object Detectioncited by 20
  27. 2021Superpoint-guided Semi-supervised Semantic Segmentation of 3D Point Cloudscited by 19
  28. 2023StereoDistill: Pick the Cream from LiDAR for Distilling Stereo-based 3D Object Detectioncited by 7
  29. 2022Multi-Class 3D Object Detection with Single-Class Supervisioncited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 8; 2025: 4; 2026: 2.

18 papers.

Sort:

By area:

  1. 2025HERMES: A Unified Self-Driving World Model for Simultaneous 3D Scene Understanding and Generationcited by 56
  2. 2023SAM3D: Zero-Shot 3D Object Detection via Segment Anything Modelcited by 40
  3. 2025UniFuture: A 4D Driving World Model for Future Generation and Perceptioncited by 21
  4. 2023SOOD: Towards Semi-Supervised Oriented Object Detectioncited by 19
  5. 2023Doubly Robust Self-Trainingcited by 18
  6. 2024SOOD++: Leveraging Unlabeled Data to Boost Oriented Object Detectioncited by 12
  7. 2023Diffusion-based 3D Object Detection with Random Boxescited by 5
  8. 2024You Only Look Bottom-Up for Monocular 3D Object Detectioncited by 3
  9. 2026HERMES++: Toward a Unified Driving World Model for 3D Scene Understanding and Generationcited by 3
  10. 2024Semi-Supervised 3D Object Detection with Channel Augmentation using Transformation Equivariancecited by 2
  11. 2026PointTPA: Dynamic Network Parameter Adaptation for 3D Scene Understandingcited by 2
  12. 2024SSF3D: Strict Semi-Supervised 3D Object Detection with Switching Filtercited by 1
6 more
  1. 2024CP-VoteNet: Contrastive Prototypical VoteNet for Few-Shot Point Cloud Object Detectioncited by 1
  2. 2025GeoTeacher: Geometry-Guided Semi-Supervised 3D Object Detectioncited by 1
  3. 2024Power of Cooperative Supervision: Multiple Teachers Framework for Enhanced 3D Semi-Supervised Object Detectioncited by 0
  4. 2024TrajSSL: Trajectory-Enhanced Semi-Supervised 3D Object Detectioncited by 0
  5. 2024TSceneJAL: Joint Active Learning of Traffic Scenes for 3D Object Detectioncited by 0
  6. 2025Learning Adaptive Pseudo-Label Selection for Semi-Supervised 3D Object Detectioncited by 0