mlsanityData to 14 Oct 2026

Deep Learning for 3D Point Clouds: A Survey

arXiv 1912.12033 ↗First posted 27 Dec 2019cs.CV cs.LG cs.ROcited by 558

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Abstract (arXiv)

Point cloud learning has lately attracted increasing attention due to its wide applications in many areas, such as computer vision, autonomous driving, and robotics. As a dominating technique in AI, deep learning has been successfully used to solve various 2D vision problems. However, deep learning on point clouds is still in its infancy due to the unique challenges faced by the processing of point clouds with deep neural networks. Recently, deep learning on point clouds has become even thriving, with numerous methods being proposed to address different problems in this area. To stimulate future research, this paper presents a comprehensive review of recent progress in deep learning methods for point clouds. It covers three major tasks, including 3D shape classification, 3D object detection and tracking, and 3D point cloud segmentation. It also presents comparative results on several publicly available datasets, together with insightful observations and inspiring future research directions.

Key facts. Cited by 558 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 Dec 2019.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 90; 2022: 112; 2023: 103; 2024: 101; 2025: 95; 2026: 57.

558 papers.

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  2. 2022Decomposing NeRF for Editing via Feature Field Distillationcited by 371
  3. 2023VoxFormer: Sparse Voxel Transformer for Camera-based 3D Semantic Scene Completioncited by 313
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  6. 2021Group-Free 3D Object Detection via Transformerscited by 215
  7. 2023CLIP2Scene: Towards Label-efficient 3D Scene Understanding by CLIPcited by 181
  8. 2021Spherical Message Passing for 3D Graph Networkscited by 168
  9. 2021Revisiting Point Cloud Shape Classification with a Simple and Effective Baselinecited by 145
  10. 20223D Object Detection for Autonomous Driving: A Comprehensive Surveycited by 132
  11. 2022Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Cloudscited by 131
  12. 2023Robo3D: Towards Robust and Reliable 3D Perception against Corruptionscited by 129
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  3. 2021NeSF: Neural Semantic Fields for Generalizable Semantic Segmentation of 3D Scenescited by 119
  4. 20213D Object Detection for Autonomous Driving: A Surveycited by 106
  5. 2021Invisible for both Camera and LiDAR: Security of Multi-Sensor Fusion based Perception in Autonomous Driving Under Physical-World Attackscited by 101
  6. 2021Adversarial Sticker: A Stealthy Attack Method in the Physical Worldcited by 93
  7. 2021Generative Models as Distributions of Functionscited by 89
  8. 2022Latency-Aware Collaborative Perceptioncited by 86
  9. 2022Self-Supervised Learning for Videos: A Surveycited by 85
  10. 2021Equivariant Point Network for 3D Point Cloud Analysiscited by 81
  11. 2021Free-form Description Guided 3D Visual Graph Network for Object Grounding in Point Cloudcited by 81
  12. 2021Transfer Learning from Synthetic to Real LiDAR Point Cloud for Semantic Segmentationcited by 76
  13. 20213D Semantic Scene Completion: a Surveycited by 74
  14. 2021Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusioncited by 72
  15. 2021PointMixer: MLP-Mixer for Point Cloud Understandingcited by 71
  16. 2021PointCutMix: Regularization Strategy for Point Cloud Classificationcited by 69
  17. 2021Deep Implicit Moving Least-Squares Functions for 3D Reconstructioncited by 67
  18. 2021SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Cloudscited by 64
  19. 2024Advances in 3D Generation: A Surveycited by 63
  20. 2023Generative AI meets 3D: A Survey on Text-to-3D in AIGC Eracited by 61
  21. 2021Learning 3D Semantic Segmentation with only 2D Image Supervisioncited by 60
  22. 2023UniSeg: A Unified Multi-Modal LiDAR Segmentation Network and the OpenPCSeg Codebasecited by 57
  23. 2024Mamba3D: Enhancing Local Features for 3D Point Cloud Analysis via State Space Modelcited by 57
  24. 2021Semantics for Robotic Mapping, Perception and Interaction: A Surveycited by 56
  25. 2022Contrastive Boundary Learning for Point Cloud Segmentationcited by 56
  26. 2022CENet: Toward Concise and Efficient LiDAR Semantic Segmentation for Autonomous Drivingcited by 56
  27. 2022A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspectivecited by 56
  28. 2022Unsupervised Point Cloud Representation Learning with Deep Neural Networks: A Surveycited by 54
  29. 2022STPLS3D: A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point Cloud Datasetcited by 53
  30. 2021Learning Semantic Segmentation of Large-Scale Point Clouds with Random Samplingcited by 51
  31. 2022SensatUrban: Learning Semantics from Urban-Scale Photogrammetric Point Cloudscited by 50
  32. 2023FRNet: Frustum-Range Networks for Scalable LiDAR Segmentationcited by 50
  33. 2021HRegNet: A Hierarchical Network for Large-scale Outdoor LiDAR Point Cloud Registrationcited by 48
  34. 2021A Survey on Deep Domain Adaptation for LiDAR Perceptioncited by 47
  35. 2022Learning Multi-View Aggregation In the Wild for Large-Scale 3D Semantic Segmentationcited by 47
  36. 2021Defending against Model Stealing via Verifying Embedded External Featurescited by 46
  37. 2022Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detectioncited by 46
  38. 2021Style-based Point Generator with Adversarial Rendering for Point Cloud Completioncited by 45

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