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Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review

arXiv 2005.09830 ↗First posted 20 May 2020cs.CVcited by 114

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Abstract (arXiv)

Recently, the advancement of deep learning in discriminative feature learning from 3D LiDAR data has led to rapid development in the field of autonomous driving. However, automated processing uneven, unstructured, noisy, and massive 3D point clouds is a challenging and tedious task. In this paper, we provide a systematic review of existing compelling deep learning architectures applied in LiDAR point clouds, detailing for specific tasks in autonomous driving such as segmentation, detection, and classification. Although several published research papers focus on specific topics in computer vision for autonomous vehicles, to date, no general survey on deep learning applied in LiDAR point clouds for autonomous vehicles exists. Thus, the goal of this paper is to narrow the gap in this topic. More than 140 key contributions in the recent five years are summarized in this survey, including the milestone 3D deep architectures, the remarkable deep learning applications in 3D semantic segmentation, object detection, and classification; specific datasets, evaluation metrics, and the state of the art performance. Finally, we conclude the remaining challenges and future researches.

Key facts. Cited by 114 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 May 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 10; 2022: 19; 2023: 35; 2024: 14; 2025: 24; 2026: 12.

114 papers.

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  1. 2023Rethinking Range View Representation for LiDAR Segmentationcited by 126
  2. 2021Robust Point Cloud Registration Framework Based on Deep Graph Matchingcited by 96
  3. 2023Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveyscited by 75
  4. 20213D Semantic Scene Completion: a Surveycited by 74
  5. 2022Multi-modal Sensor Fusion for Auto Driving Perception: A Surveycited by 73
  6. 2023LiDAR Data Synthesis with Denoising Diffusion Probabilistic Modelscited by 53
  7. 2023Pyramid Diffusion for Fine 3D Large Scene Generationcited by 44
  8. 2022GFNet: Geometric Flow Network for 3D Point Cloud Semantic Segmentationcited by 36
  9. 2022Cross-modal Learning for Image-Guided Point Cloud Shape Completioncited by 36
  10. 2025Towards Robust and Secure Embodied AI: A Survey on Vulnerabilities and Attackscited by 32
  11. 2022Imperceptible and Robust Backdoor Attack in 3D Point Cloudcited by 25
  12. 2023Milestones in Autonomous Driving and Intelligent Vehicles Part I: Control, Computing System Design, Communication, HD Map, Testing, and Human Behaviorscited by 25
38 more
  1. 2022RaLiBEV: Radar and LiDAR BEV Fusion Learning for Anchor Box Free Object Detection Systemscited by 23
  2. 2024An End-to-End Robust Point Cloud Semantic Segmentation Network with Single-Step Conditional Diffusion Modelscited by 22
  3. 20223D Vision with Transformers: A Surveycited by 21
  4. 2025SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Drivingcited by 21
  5. 2022Towards Robust Adaptive Object Detection under Noisy Annotationscited by 20
  6. 2023Erasing, Transforming, and Noising Defense Network for Occluded Person Re-Identificationcited by 19
  7. 2021Full-Velocity Radar Returns by Radar-Camera Fusioncited by 16
  8. 2022MaskRange: A Mask-classification Model for Range-view based LiDAR Segmentationcited by 15
  9. 2024PointNet with KAN versus PointNet with MLP for 3D Classification and Segmentation of Point Setscited by 15
  10. 2021Attentive Prototypes for Source-free Unsupervised Domain Adaptive 3D Object Detectioncited by 13
  11. 2023CPCM: Contextual Point Cloud Modeling for Weakly-supervised Point Cloud Semantic Segmentationcited by 12
  12. 2023Select2Col: Leveraging Spatial-Temporal Importance of Semantic Information for Efficient Collaborative Perceptioncited by 12
  13. 2023SCP: Spherical-Coordinate-based Learned Point Cloud Compressioncited by 12
  14. 2024SmartCooper: Vehicular Collaborative Perception with Adaptive Fusion and Judger Mechanismcited by 11
  15. 2023Deep Transfer Learning for Intelligent Vehicle Perception: a Surveycited by 10
  16. 2024Explicitly Guided Information Interaction Network for Cross-modal Point Cloud Completioncited by 9
  17. 2024Dual-level Adaptive Self-Labeling for Novel Class Discovery in Point Cloud Segmentationcited by 9
  18. 2021Self-supervised Point Cloud Representation Learning via Separating Mixed Shapescited by 8
  19. 2022A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapescited by 8
  20. 2023A Review of Panoptic Segmentation for Mobile Mapping Point Cloudscited by 8
  21. 2023GPA-3D: Geometry-aware Prototype Alignment for Unsupervised Domain Adaptive 3D Object Detection from Point Cloudscited by 8
  22. 2023RSRD: A Road Surface Reconstruction Dataset and Benchmark for Safe and Comfortable Autonomous Drivingcited by 8
  23. 2025Collaborative Perception Datasets for Autonomous Driving: A Reviewcited by 8
  24. 2022Invariance-Aware Randomized Smoothing Certificatescited by 7
  25. 2023PointWavelet: Learning in Spectral Domain for 3D Point Cloud Analysiscited by 7
  26. 2023Wigner kernels: body-ordered equivariant machine learning without a basiscited by 7
  27. 2021A Learnable Self-supervised Task for Unsupervised Domain Adaptation on Point Cloudscited by 6
  28. 2021Aerial Images Meet Crowdsourced Trajectories: A New Approach to Robust Road Extractioncited by 6
  29. 2021Full Transformer Framework for Robust Point Cloud Registration with Deep Information Interactioncited by 6
  30. 2022Data Augmentation-free Unsupervised Learning for 3D Point Cloud Understandingcited by 6
  31. 2023TUM-FAcCADE: Reviewing and enriching point cloud benchmarks for faccade segmentationcited by 5
  32. 2025INTACT: Inducing Noise Tolerance through Adversarial Curriculum Training for LiDAR-based Safety-Critical Perception and Autonomycited by 5
  33. 2025A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospectscited by 5
  34. 2025U4D: Uncertainty-Aware 4D World Modeling from LiDAR Sequencescited by 5
  35. 20264DPC2hat: Towards Dynamic Point Cloud Understanding with Failure-Aware Bootstrappingcited by 5
  36. 2021Mesh Convolution with Continuous Filters for 3D Surface Parsingcited by 4
  37. 2022GeoGCN: Geometric Dual-domain Graph Convolution Network for Point Cloud Denoisingcited by 4
  38. 2022Enhanced Low-resolution LiDAR-Camera Calibration Via Depth Interpolation and Supervised Contrastive Learningcited by 4

50 most cited of 114 All 114 (JSON)