mlsanityData to 7 Oct 2026

SSDA3D: Semi-supervised Domain Adaptation for 3D Object Detection from Point Cloud

arXiv 2212.02845 ↗First posted 6 Dec 2022cs.CV cs.AIcited by 37

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Abstract (arXiv)

LiDAR-based 3D object detection is an indispensable task in advanced autonomous driving systems. Though impressive detection results have been achieved by superior 3D detectors, they suffer from significant performance degeneration when facing unseen domains, such as different LiDAR configurations, different cities, and weather conditions. The mainstream approaches tend to solve these challenges by leveraging unsupervised domain adaptation (UDA) techniques. However, these UDA solutions just yield unsatisfactory 3D detection results when there is a severe domain shift, e.g., from Waymo (64-beam) to nuScenes (32-beam). To address this, we present a novel Semi-Supervised Domain Adaptation method for 3D object detection (SSDA3D), where only a few labeled target data is available, yet can significantly improve the adaptation performance. In particular, our SSDA3D includes an Inter-domain Adaptation stage and an Intra-domain Generalization stage. In the first stage, an Inter-domain Point-CutMix module is presented to efficiently align the point cloud distribution across domains. The Point-CutMix generates mixed samples of an intermediate domain, thus encouraging to learn domain-invariant knowledge. Then, in the second stage, we further enhance the model for better generalization on the unlabeled target set. This is achieved by exploring Intra-domain Point-MixUp in semi-supervised learning, which essentially regularizes the pseudo label distribution. Experiments from Waymo to nuScenes show that, with only 10% labeled target data, our SSDA3D can surpass the fully-supervised oracle model with 100% target label. Our code is available at https://github.com/yinjunbo/SSDA3D.

Key facts. Cited by 37 papers in the map (data through 7 Oct 2026). Builds on 38 of its 47 references that are in the map. First posted on arXiv 6 Dec 2022.

Builds on

38 of 47 references.

References · 38

  1. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,155
  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. 2019CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Featurescited by 2,875
  5. 2019Scalability in Perception for Autonomous Driving: Waymo Open Datasetcited by 2,436
  6. 2019RandAugment: Practical automated data augmentation with a reduced search spacecited by 2,261
  7. 2020FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidencecited by 2,080
  8. 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,707
  9. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
  10. 2017Adversarial Discriminative Domain Adaptationcited by 1,309
  11. 2020Center-based 3D Object Detection and Trackingcited by 1,143
  12. 2018PointRCNN: 3D Object Proposal Generation and Detection from Point Cloudcited by 939
26 more
  1. 2019PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detectioncited by 890
  2. 20173D Semantic Segmentation with Submanifold Sparse Convolutional Networkscited by 746
  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. 2015Return of Frustratingly Easy Domain Adaptationcited by 389
  6. 2018SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloudcited by 228
  7. 2021ST3D: Self-training for Unsupervised Domain Adaptation on 3D Object Detectioncited by 158
  8. 2020Train in Germany, Test in The USA: Making 3D Object Detectors Generalizecited by 150
  9. 2022Focal Sparse Convolutional Networks for 3D Object Detectioncited by 142
  10. 2022Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Cloudscited by 131
  11. 2020Complete & Label: A Domain Adaptation Approach to Semantic Segmentation of LiDAR Point Cloudscited by 116
  12. 2020CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloudcited by 107
  13. 2020Self-Supervised Learning for Domain Adaptation on Point-Cloudscited by 99
  14. 2021SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generationcited by 93
  15. 2021FusionPainting: Multimodal Fusion with Adaptive Attention for 3D Object Detectioncited by 92
  16. 2019SESS: Self-Ensembling Semi-Supervised 3D Object Detectioncited by 89
  17. 20203DIoUMatch: Leveraging IoU Prediction for Semi-Supervised 3D Object Detectioncited by 89
  18. 2021Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detectioncited by 79
  19. 2021Transfer Learning from Synthetic to Real LiDAR Point Cloud for Semantic Segmentationcited by 76
  20. 2022MPPNet: Multi-Frame Feature Intertwining with Proxy Points for 3D Temporal Object Detectioncited by 65
  21. 2020ePointDA: An End-to-End Simulation-to-Real Domain Adaptation Framework for LiDAR Point Cloud Segmentationcited by 56
  22. 2022SASA: Semantics-Augmented Set Abstraction for Point-based 3D Object Detectioncited by 52
  23. 2022Semi-supervised 3D Object Detection with Proficient Teacherscited by 45
  24. 2019Domain Adaptation for Vehicle Detection from Bird's Eye View LiDAR Point Cloud Datacited by 35
  25. 2019Transferable Semi-supervised 3D Object Detection from RGB-D Datacited by 22
  26. 2022Graph Neural Network and Spatiotemporal Transformer Attention for 3D Video Object Detection from Point Cloudscited by 17

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 8; 2024: 17; 2025: 9; 2026: 2.

37 papers.

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  1. 2024IS-Fusion: Instance-Scene Collaborative Fusion for Multimodal 3D Object Detectioncited by 57
  2. 2023A Survey of Label-Efficient Deep Learning for 3D Point Cloudscited by 20
  3. 2024RoboFusion: Towards Robust Multi-Modal 3D Object Detection via SAMcited by 20
  4. 2023Weakly Supervised Monocular 3D Object Detection using Multi-View Projection and Direction Consistencycited by 18
  5. 2023Diffusion-SS3D: Diffusion Model for Semi-supervised 3D Object Detectioncited by 14
  6. 2024DG-PIC: Domain Generalized Point-In-Context Learning for Point Cloud Understandingcited by 13
  7. 2024UADA3D: Unsupervised Adversarial Domain Adaptation for 3D Object Detection with Sparse LiDAR and Large Domain Gapscited by 12
  8. 2024PCoTTA: Continual Test-Time Adaptation for Multi-Task Point Cloud Understandingcited by 11
  9. 2023Deep Transfer Learning for Intelligent Vehicle Perception: a Surveycited by 10
  10. 2024An Empirical Study of the Generalization Ability of Lidar 3D Object Detectors to Unseen Domainscited by 10
  11. 2022LWSIS: LiDAR-guided Weakly Supervised Instance Segmentation for Autonomous Drivingcited by 9
  12. 2023Towards Generalizable Multi-Camera 3D Object Detection via Perspective Debiasingcited by 9
25 more
  1. 2024Advancing 3D Point Cloud Understanding through Deep Transfer Learning: A Comprehensive Surveycited by 9
  2. 2023GeoAdapt: Self-Supervised Test-Time Adaptation in LiDAR Place Recognition Using Geometric Priorscited by 8
  3. 2023COLA: COarse-LAbel multi-source LiDAR semantic segmentation for autonomous drivingcited by 8
  4. 2024SOAP: Cross-sensor Domain Adaptation for 3D Object Detection Using Stationary Object Aggregation Pseudo-labellingcited by 8
  5. 2025Ev-3DOD: Pushing the Temporal Boundaries of 3D Object Detection with Event Camerascited by 8
  6. 2024RepVF: A Unified Vector Fields Representation for Multi-task 3D Perceptioncited by 5
  7. 2024Learning 3D Perception from Others' Predictionscited by 4
  8. 2025Unleashing the Temporal Potential of Stereo Event Cameras for Continuous-Time 3D Object Detectioncited by 3
  9. 2023Tensor Factorization for Leveraging Cross-Modal Knowledge in Data-Constrained Infrared Object Detectioncited by 2
  10. 2024PRENet: A Plane-Fit Redundancy Encoding Point Cloud Sequence Network for Real-Time 3D Action Recognitioncited by 2
  11. 2024Exploring Scene Affinity for Semi-Supervised LiDAR Semantic Segmentationcited by 2
  12. 2025Towards Railway Domain Adaptation for LiDAR-based 3D Detection: Road-to-Rail and Sim-to-Real via SynDRA-BBoxcited by 2
  13. 2025Few-Shot Learning in Video and 3D Object Detection: A Surveycited by 2
  14. 2024Semi-Supervised Domain Adaptation Using Target-Oriented Domain Augmentation for 3D Object Detectioncited by 1
  15. 2024DyConfidMatch: Dynamic Thresholding and Re-sampling for 3D Semi-supervised Learningcited by 1
  16. 2025Pillar-Voxel Fusion Network for 3D Object Detection in Airborne Hyperspectral Point Cloudscited by 1
  17. 2024SGV3D:Towards Scenario Generalization for Vision-based Roadside 3D Object Detectioncited by 0
  18. 2024AGLP: A Graph Learning Perspective for Semi-supervised Domain Adaptationcited by 0
  19. 2024DREAM: Domain-agnostic Reverse Engineering Attributes of Black-box Modelcited by 0
  20. 2025Class-Aware PillarMix: Can Mixed Sample Data Augmentation Enhance 3D Object Detection with Radar Point Clouds?cited by 0
  21. 2025From Dataset to Real-world: General 3D Object Detection via Generalized Cross-domain Few-shot Learningcited by 0
  22. 2025Domain Adaptation for Different Sensor Configurations in 3D Object Detectioncited by 0
  23. 2025Semi-Supervised Diversity-Aware Domain Adaptation for 3D Object detectioncited by 0
  24. 2026RoAD Benchmark: How LiDAR Models Fail under Coupled Domain Shifts and Label Evolutioncited by 0
  25. 2026Towards robust multimodal 3D object detection via visual foundation modelscited by 0