mlsanityData to 7 Oct 2026

A Survey of Robust 3D Object Detection Methods in Point Clouds

arXiv 2204.00106 ↗First posted 31 Mar 2022cs.CVcited by 10

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Abstract (arXiv)

The purpose of this work is to review the state-of-the-art LiDAR-based 3D object detection methods, datasets, and challenges. We describe novel data augmentation methods, sampling strategies, activation functions, attention mechanisms, and regularization methods. Furthermore, we list recently introduced normalization methods, learning rate schedules and loss functions. Moreover, we also cover advantages and limitations of 10 novel autonomous driving datasets. We evaluate novel 3D object detectors on the KITTI, nuScenes, and Waymo dataset and show their accuracy, speed, and robustness. Finally, we mention the current challenges in 3D object detection in LiDAR point clouds and list some open issues.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 40 of its 60 references that are in the map. First posted on arXiv 31 Mar 2022.

Builds on

40 of 60 references.

References · 40

  1. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 4,993
  2. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,485
  3. 2019Scalability in Perception for Autonomous Driving: Waymo Open Datasetcited by 2,436
  4. 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,707
  5. 2020Center-based 3D Object Detection and Trackingcited by 1,143
  6. 2019PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detectioncited by 890
  7. 2019Argoverse: 3D Tracking and Forecasting with Rich Mapscited by 728
  8. 2019Deep Learning for 3D Point Clouds: A Surveycited by 557
  9. 20203DSSD: Point-based 3D Single Stage Object Detectorcited by 440
  10. 2018DropBlock: A regularization method for convolutional networkscited by 261
  11. 2019The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networkscited by 241
  12. 2020One Thousand and One Hours: Self-driving Motion Prediction Datasetcited by 239
28 more
  1. 2021PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detectioncited by 232
  2. 2020Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Netcited by 228
  3. 2021One Million Scenes for Autonomous Driving: ONCE Datasetcited by 218
  4. 2020A2D2: Audi Autonomous Driving Datasetcited by 189
  5. 2017Searching for Activation Functionscited by 158
  6. 2020Train in Germany, Test in The USA: Making 3D Object Detectors Generalizecited by 150
  7. 2021Offboard 3D Object Detection from Point Cloud Sequencescited by 144
  8. 2019IoU Loss for 2D/3D Object Detectioncited by 128
  9. 2021SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloudcited by 119
  10. 2019TANet: Robust 3D Object Detection from Point Clouds with Triple Attentioncited by 108
  11. 2020CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloudcited by 107
  12. 2019Faster AutoAugment: Learning Augmentation Strategies using Backpropagationcited by 96
  13. 20213D-MAN: 3D Multi-frame Attention Network for Object Detectioncited by 73
  14. 2019What You See is What You Get: Exploiting Visibility for 3D Object Detectioncited by 69
  15. 2021PointCutMix: Regularization Strategy for Point Cloud Classificationcited by 69
  16. 2020LiDAR-based Online 3D Video Object Detection with Graph-based Message Passing and Spatiotemporal Transformer Attentioncited by 65
  17. 2020Temporal-Channel Transformer for 3D Lidar-Based Video Object Detection in Autonomous Drivingcited by 55
  18. 2019Filter Response Normalization Layer: Eliminating Batch Dependence in the Training of Deep Neural Networkscited by 45
  19. 2020An LSTM Approach to Temporal 3D Object Detection in LiDAR Point Cloudscited by 45
  20. 20214D-Net for Learned Multi-Modal Alignmentcited by 44
  21. 2020Improving 3D Object Detection through Progressive Population Based Augmentationcited by 41
  22. 20193D BAT: A Semi-Automatic, Web-based 3D Annotation Toolbox for Full-Surround, Multi-Modal Data Streamscited by 35
  23. 2020LIBRE: The Multiple 3D LiDAR Datasetcited by 26
  24. 2021IPS300+: a Challenging Multimodal Dataset for Intersection Perception Systemcited by 26
  25. 20203D IoU-Net: IoU Guided 3D Object Detector for Point Cloudscited by 15
  26. 2019Attentive Normalizationcited by 13
  27. 2021Real-time 3D Object Detection using Feature Map Flowcited by 5
  28. 2021Temp-Frustum Net: 3D Object Detection with Temporal Fusioncited by 3

Built on by

Cited by year

Citing papers per year: 2023: 3; 2024: 4; 2025: 3.

10 papers.

  1. 2024TUMTraf V2X Cooperative Perception Datasetcited by 66
  2. 2023InfraDet3D: Multi-Modal 3D Object Detection based on Roadside Infrastructure Camera and LiDAR Sensorscited by 25
  3. 2024ActiveAnno3D -- An Active Learning Framework for Multi-Modal 3D Object Detectioncited by 17
  4. 2023Recent Advances in Multi-modal 3D Intelligence: A Comprehensive Survey and Evaluationcited by 12
  5. 20233D Multiple Object Tracking on Autonomous Driving: A Literature Reviewcited by 4
  6. 2024A Survey on Adversarial Robustness of LiDAR-based Machine Learning Perception in Autonomous Vehiclescited by 3
  7. 2024PointCompress3D: A Point Cloud Compression Framework for Roadside LiDARs in Intelligent Transportation Systemscited by 2
  8. 2025Towards Vision Zero: The TUM Traffic Accid3nD Datasetcited by 2
  9. 2025Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensorscited by 1
  10. 2025Safety-Critical Learning for Long-Tail Events: The TUM Traffic Accident Datasetcited by 1