mlsanityData to 14 Oct 2026

UADA3D: Unsupervised Adversarial Domain Adaptation for 3D Object Detection with Sparse LiDAR and Large Domain Gaps

arXiv 2403.17633 ↗First posted 26 Mar 2024cs.CV cs.AI cs.ROcited by 12

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Abstract (arXiv)

In this study, we address a gap in existing unsupervised domain adaptation approaches on LiDAR-based 3D object detection, which have predominantly concentrated on adapting between established, high-density autonomous driving datasets. We focus on sparser point clouds, capturing scenarios from different perspectives: not just from vehicles on the road but also from mobile robots on sidewalks, which encounter significantly different environmental conditions and sensor configurations. We introduce Unsupervised Adversarial Domain Adaptation for 3D Object Detection (UADA3D). UADA3D does not depend on pre-trained source models or teacher-student architectures. Instead, it uses an adversarial approach to directly learn domain-invariant features. We demonstrate its efficacy in various adaptation scenarios, showing significant improvements in both self-driving car and mobile robot domains. Our code is open-source and will be available soon.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 55 of its 74 references that are in the map. First posted on arXiv 26 Mar 2024.

Builds on

55 of 74 references.

References · 55

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  2. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,523
  3. 2019Scalability in Perception for Autonomous Driving: Waymo Open Datasetcited by 2,451
  4. 2014Unsupervised Domain Adaptation by Backpropagationcited by 1,917
  5. 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,715
  6. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
  7. 2020Center-based 3D Object Detection and Trackingcited by 1,144
  8. 2017CyCADA: Cycle-Consistent Adversarial Domain Adaptationcited by 1,082
  9. 2018PointRCNN: 3D Object Proposal Generation and Detection from Point Cloudcited by 941
  10. 2022BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representationcited by 850
  11. 2023Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecastingcited by 707
  12. 2018ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentationcited by 623
38 more
  1. 2022TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformerscited by 466
  2. 2018Domain Adaptive Faster R-CNN for Object Detection in the Wildcited by 443
  3. 20203DSSD: Point-based 3D Single Stage Object Detectorcited by 440
  4. 2022BEVFusion: A Simple and Robust LiDAR-Camera Fusion Frameworkcited by 336
  5. 2019STD: Sparse-to-Dense 3D Object Detector for Point Cloudcited by 282
  6. 2018Strong-Weak Distribution Alignment for Adaptive Object Detectioncited by 270
  7. 2021ST3D: Self-training for Unsupervised Domain Adaptation on 3D Object Detectioncited by 159
  8. 2020Train in Germany, Test in The USA: Making 3D Object Detectors Generalizecited by 151
  9. 2020Exploring Categorical Regularization for Domain Adaptive Object Detectioncited by 131
  10. 2022Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Cloudscited by 131
  11. 2019xMUDA: Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentationcited by 129
  12. 2020Complete & Label: A Domain Adaptation Approach to Semantic Segmentation of LiDAR Point Cloudscited by 116
  13. 2019Multi-adversarial Faster-RCNN for Unrestricted Object Detectioncited by 112
  14. 2022MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformercited by 109
  15. 2021SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generationcited by 94
  16. 2021Unsupervised Domain Adaptive 3D Detection with Multi-Level Consistencycited by 63
  17. 2021ST3D++: Denoised Self-training for Unsupervised Domain Adaptation on 3D Object Detectioncited by 57
  18. 2022LiDAR Distillation: Bridging the Beam-Induced Domain Gap for 3D Object Detectioncited by 55
  19. 2021Adversarial Unsupervised Domain Adaptation with Conditional and Label Shift: Infer, Align and Iteratecited by 43
  20. 2021ConDA: Unsupervised Domain Adaptation for LiDAR Segmentation via Regularized Domain Concatenationcited by 41
  21. 2022Domain Adaptive Object Detection for Autonomous Driving under Foggy Weathercited by 41
  22. 2022Monocular 3D Object Detection with Depth from Motioncited by 38
  23. 2022SSDA3D: Semi-supervised Domain Adaptation for 3D Object Detection from Point Cloudcited by 37
  24. 2022Towards Online Domain Adaptive Object Detectioncited by 36
  25. 2023Density-Insensitive Unsupervised Domain Adaption on 3D Object Detectioncited by 31
  26. 2023Revisiting Domain-Adaptive 3D Object Detection by Reliable, Diverse and Class-balanced Pseudo-Labelingcited by 28
  27. 2022CL3D: Unsupervised Domain Adaptation for Cross-LiDAR 3D Detectioncited by 27
  28. 2023Bi3D: Bi-domain Active Learning for Cross-domain 3D Object Detectioncited by 22
  29. 2021Uncertainty-aware Mean Teacher for Source-free Unsupervised Domain Adaptive 3D Object Detectioncited by 18
  30. 2024MCD: Diverse Large-Scale Multi-Campus Dataset for Robot Perceptioncited by 16
  31. 2021See Eye to Eye: A Lidar-Agnostic 3D Detection Framework for Unsupervised Multi-Target Domain Adaptationcited by 15
  32. 2023MS3D++: Ensemble of Experts for Multi-Source Unsupervised Domain Adaptation in 3D Object Detectioncited by 14
  33. 2023LiDAR-CS Dataset: LiDAR Point Cloud Dataset with Cross-Sensors for 3D Object Detectioncited by 13
  34. 2023MS3D: Leveraging Multiple Detectors for Unsupervised Domain Adaptation in 3D Object Detectioncited by 12
  35. 2023GPA-3D: Geometry-aware Prototype Alignment for Unsupervised Domain Adaptive 3D Object Detection from Point Cloudscited by 8
  36. 2023Open Compound Domain Adaptation with Object Style Compensation for Semantic Segmentationcited by 8
  37. 2023BaSAL: Size-Balanced Warm Start Active Learning for LiDAR Semantic Segmentationcited by 8
  38. 2024SOAP: Cross-sensor Domain Adaptation for 3D Object Detection Using Stationary Object Aggregation Pseudo-labellingcited by 8

50 most cited of 55 All 55 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 8; 2026: 3.

12 papers.

  1. 2024S3PT: Scene Semantics and Structure Guided Clustering to Boost Self-Supervised Pre-Training for Autonomous Drivingcited by 4
  2. 2025CleverDistiller: Simple and Spatially Consistent Cross-modal Distillationcited by 3
  3. 2025MonoCT: Overcoming Monocular 3D Detection Domain Shift with Consistent Teacher Modelscited by 2
  4. 2025Cross-View Open-Vocabulary Object Detection in Aerial Imagerycited by 1
  5. 2025MORDA: A Synthetic Dataset to Facilitate Adaptation of Object Detectors to Unseen Real-target Domain While Preserving Performance on Real-source Domaincited by 0
  6. 2025An Investigation of Beam Density on LiDAR Object Detection Performancecited by 0
  7. 2025Robust Unsupervised Domain Adaptation for 3D Point Cloud Segmentation Under Source Adversarial Attackscited by 0
  8. 2025Domain Adaptation for Different Sensor Configurations in 3D Object Detectioncited by 0
  9. 2025BlendCLIP: Bridging Synthetic and Real Domains for Zero-Shot 3D Object Classification with Multimodal Pretrainingcited by 0
  10. 2026LCF3D: A Robust and Real-Time Late-Cascade Fusion Framework for 3D Object Detection in Autonomous Drivingcited by 0
  11. 2026Auto-Labelling-Based Domain Transfer for 3D Object Detection on a Bicycle-Mounted LiDAR Platformcited by 0
  12. 2026SimFuse3D: Source-Guided Target Simulation and Confidence-Guided Multi-Stage Localization Reweighting for Cross-Platform 3D Object Detectioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026UniDA3D: A Unified Domain-Adaptive Framework for Multi-View 3D Object Detection
  2. 2022Unsupervised Domain Adaptation for Monocular 3D Object Detection via Self-Training
  3. 2023Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features
  4. 2025Multi-Modal Assistance for Unsupervised Domain Adaptation on Point Cloud 3D Object Detection
  5. 2025Improving Generalization Ability for 3D Object Detection by Learning Sparsity-invariant Features
  6. 2025BEVUDA++: Geometric-aware Unsupervised Domain Adaptation for Multi-View 3D Object Detection
  7. 2022Li3DeTr: A LiDAR based 3D Detection Transformer
  8. 2024BlenDA: Domain Adaptive Object Detection through diffusion-based blending