mlsanityData to 13 Oct 2026

3D Point Cloud Processing and Learning for Autonomous Driving

arXiv 2003.00601 ↗First posted 1 Mar 2020cs.CV eess.SPcited by 68

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Abstract (arXiv)

We present a review of 3D point cloud processing and learning for autonomous driving. As one of the most important sensors in autonomous vehicles, light detection and ranging (LiDAR) sensors collect 3D point clouds that precisely record the external surfaces of objects and scenes. The tools for 3D point cloud processing and learning are critical to the map creation, localization, and perception modules in an autonomous vehicle. While much attention has been paid to data collected from cameras, such as images and videos, an increasing number of researchers have recognized the importance and significance of LiDAR in autonomous driving and have proposed processing and learning algorithms to exploit 3D point clouds. We review the recent progress in this research area and summarize what has been tried and what is needed for practical and safe autonomous vehicles. We also offer perspectives on open issues that are needed to be solved in the future.

Key facts. Cited by 68 papers in the map (data through 13 Oct 2026). First posted on arXiv 1 Mar 2020.

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2021: 7; 2022: 10; 2023: 15; 2024: 17; 2025: 14; 2026: 5.

68 papers.

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  1. 2022Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Mapscited by 255
  2. 2021Learning Distilled Collaboration Graph for Multi-Agent Perceptioncited by 194
  3. 2023Point-Bind & Point-LLM: Aligning Point Cloud with Multi-modality for 3D Understanding, Generation, and Instruction Followingcited by 192
  4. 2023Leapfrog Diffusion Model for Stochastic Trajectory Predictioncited by 130
  5. 2022Robust Collaborative 3D Object Detection in Presence of Pose Errorscited by 129
  6. 2022Latency-Aware Collaborative Perceptioncited by 86
  7. 2023Collaboration Helps Camera Overtake LiDAR in 3D Detectioncited by 81
  8. 2021Revisiting Adversarial Robustness Distillation: Robust Soft Labels Make Student Bettercited by 77
  9. 2023Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveyscited by 75
  10. 2024Communication-Efficient Collaborative Perception via Information Filling with Codebookcited by 67
  11. 2023Asynchrony-Robust Collaborative Perception via Bird's Eye View Flowcited by 58
  12. 2022Graph Filters for Signal Processing and Machine Learning on Graphscited by 53
38 more
  1. 2023TBP-Former: Learning Temporal Bird's-Eye-View Pyramid for Joint Perception and Prediction in Vision-Centric Autonomous Drivingcited by 30
  2. 2023Interruption-Aware Cooperative Perception for V2X Communication-Aided Autonomous Drivingcited by 25
  3. 2022RaLiBEV: Radar and LiDAR BEV Fusion Learning for Anchor Box Free Object Detection Systemscited by 23
  4. 2023Point-TTA: Test-Time Adaptation for Point Cloud Registration Using Multitask Meta-Auxiliary Learningcited by 23
  5. 2021Deep Instance Segmentation with Automotive Radar Detection Pointscited by 22
  6. 2023Auxiliary Tasks Benefit 3D Skeleton-based Human Motion Predictioncited by 20
  7. 2024PointNCBW: Towards Dataset Ownership Verification for Point Clouds via Negative Clean-label Backdoor Watermarkcited by 20
  8. 2022GRASP-Net: Geometric Residual Analysis and Synthesis for Point Cloud Compressioncited by 17
  9. 2021Robustness Certification for Point Cloud Modelscited by 16
  10. 20232D-3D Interlaced Transformer for Point Cloud Segmentation with Scene-Level Supervisioncited by 16
  11. 2024RoCo:Robust Collaborative Perception By Iterative Object Matching and Pose Adjustmentcited by 12
  12. 2024CloudFixer: Test-Time Adaptation for 3D Point Clouds via Diffusion-Guided Geometric Transformationcited by 11
  13. 2021Detecting Backdoor Attacks Against Point Cloud Classifierscited by 10
  14. 2022TPC: Transformation-Specific Smoothing for Point Cloud Modelscited by 10
  15. 2023Test-Time Adaptation for Point Cloud Upsampling Using Meta-Learningcited by 10
  16. 2024Pragmatic Communication in Multi-Agent Collaborative Perceptioncited by 10
  17. 2023Milestones in Autonomous Driving and Intelligent Vehicles Part II: Perception and Planningcited by 9
  18. 2023Spatio-Temporal Branching for Motion Prediction using Motion Incrementscited by 9
  19. 2024Robust Collaborative Perception without External Localization and Clock Devicescited by 8
  20. 2022Number-Adaptive Prototype Learning for 3D Point Cloud Semantic Segmentationcited by 7
  21. 2024PIVOT-Net: Heterogeneous Point-Voxel-Tree-based Framework for Point Cloud Compressioncited by 7
  22. 2022Efficient LiDAR Point Cloud Geometry Compression Through Neighborhood Point Attentioncited by 6
  23. 2024A Linear Time and Space Local Point Cloud Geometry Encoder via Vectorized Kernel Mixture (VecKM)cited by 6
  24. 2025CoDynTrust: Robust Asynchronous Collaborative Perception via Dynamic Feature Trust Moduluscited by 6
  25. 2021COTReg:Coupled Optimal Transport based Point Cloud Registrationcited by 5
  26. 2024Self-Supervised Bird's Eye View Motion Prediction with Cross-Modality Signalscited by 5
  27. 2025CoDiff: Conditional Diffusion Model for Collaborative 3D Object Detectioncited by 4
  28. 2025LiDAR-VGGT: Cross-Modal Coarse-to-Fine Fusion for Globally Consistent and Metric-Scale Dense Mappingcited by 4
  29. 2025Rethinking Multimodal Point Cloud Completion: A Completion-by-Correction Perspectivecited by 4
  30. 2021On Adversarial Robustness of Point Cloud Semantic Segmentationcited by 3
  31. 2022PointNorm: Dual Normalization is All You Need for Point Cloud Analysiscited by 3
  32. 2024Joint Point Cloud Upsampling and Cleaning with Octree-based CNNscited by 3
  33. 2025Point-MoE: Large-Scale Multi-Dataset Training with Mixture-of-Experts for 3D Semantic Segmentationcited by 3
  34. 2023RoCNet: 3D Robust Registration of Point-Clouds using Deep Learningcited by 2
  35. 2023NoiseTrans: Point Cloud Denoising with Transformerscited by 2
  36. 2024Channel-Aware Throughput Maximization for Cooperative Data Fusion in CAVcited by 2
  37. 2025Rethinking Metrics and Diffusion Architecture for 3D Point Cloud Generationcited by 2
  38. 2026QD-PCQA: Quality-Aware Domain Adaptation for Point Cloud Quality Assessmentcited by 2

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