mlsanityData to 7 Oct 2026

PointAugment: an Auto-Augmentation Framework for Point Cloud Classification

arXiv 2002.10876 ↗First posted 25 Feb 2020cs.CVcited by 65

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Abstract (arXiv)

We present PointAugment, a new auto-augmentation framework that automatically optimizes and augments point cloud samples to enrich the data diversity when we train a classification network. Different from existing auto-augmentation methods for 2D images, PointAugment is sample-aware and takes an adversarial learning strategy to jointly optimize an augmentor network and a classifier network, such that the augmentor can learn to produce augmented samples that best fit the classifier. Moreover, we formulate a learnable point augmentation function with a shape-wise transformation and a point-wise displacement, and carefully design loss functions to adopt the augmented samples based on the learning progress of the classifier. Extensive experiments also confirm PointAugment's effectiveness and robustness to improve the performance of various networks on shape classification and retrieval.

Key facts. Cited by 65 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Feb 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 17; 2022: 16; 2023: 13; 2024: 8; 2025: 10; 2026: 1.

65 papers.

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  1. 2022CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understandingcited by 198
  2. 2021RPVNet: A Deep and Efficient Range-Point-Voxel Fusion Network for LiDAR Point Cloud Segmentationcited by 173
  3. 2021PoseAug: A Differentiable Pose Augmentation Framework for 3D Human Pose Estimationcited by 88
  4. 2021Transfer Learning from Synthetic to Real LiDAR Point Cloud for Semantic Segmentationcited by 76
  5. 2021Point Cloud Upsampling via Disentangled Refinementcited by 74
  6. 2022PolarMix: A General Data Augmentation Technique for LiDAR Point Cloudscited by 74
  7. 2022Benchmarking and Analyzing Point Cloud Classification under Corruptionscited by 70
  8. 2021PointCutMix: Regularization Strategy for Point Cloud Classificationcited by 69
  9. 2021Regularization Strategy for Point Cloud via Rigidly Mixed Samplecited by 59
  10. 2021Point Cloud Augmentation with Weighted Local Transformationscited by 39
  11. 2021Zero-Shot Learning on 3D Point Cloud Objects and Beyondcited by 38
  12. 2022TeachAugment: Data Augmentation Optimization Using Teacher Knowledgecited by 37
38 more
  1. 2021Pseudo-labeling for Scalable 3D Object Detectioncited by 32
  2. 2021Direct Differentiable Augmentation Searchcited by 28
  3. 2023Advancements in Point Cloud Data Augmentation for Deep Learning: A Surveycited by 24
  4. 2023A Survey of Label-Efficient Deep Learning for 3D Point Cloudscited by 20
  5. 2025Generalized Few-shot 3D Point Cloud Segmentation with Vision-Language Modelcited by 17
  6. 2021Pattern-Aware Data Augmentation for LiDAR 3D Object Detectioncited by 15
  7. 2022Dual Adaptive Transformations for Weakly Supervised Point Cloud Segmentationcited by 15
  8. 2023When to Learn What: Model-Adaptive Data Augmentation Curriculumcited by 13
  9. 2022SageMix: Saliency-Guided Mixup for Point Cloudscited by 12
  10. 2024Benchmarking tree species classification from proximally-sensed laser scanning data: introducing the FOR-species20K datasetcited by 12
  11. 2023Fusion is Not Enough: Single Modal Attacks on Fusion Models for 3D Object Detectioncited by 11
  12. 2021PnP-3D: A Plug-and-Play for 3D Point Cloudscited by 10
  13. 2023A Closer Look at Few-Shot 3D Point Cloud Classificationcited by 10
  14. 2024MaskBEV: Towards A Unified Framework for BEV Detection and Map Segmentationcited by 10
  15. 2022Self-Supervised Text Erasing with Controllable Image Synthesiscited by 9
  16. 2022Semi-supervised 3D shape segmentation with multilevel consistency and part substitutioncited by 8
  17. 2022LidarAugment: Searching for Scalable 3D LiDAR Data Augmentationscited by 8
  18. 2023Few-Shot Point Cloud Semantic Segmentation via Contrastive Self-Supervision and Multi-Resolution Attentioncited by 8
  19. 2021Language-Level Semantics Conditioned 3D Point Cloud Segmentationcited by 7
  20. 2022PseudoAugment: Learning to Use Unlabeled Data for Data Augmentation in Point Cloudscited by 7
  21. 2023GPr-Net: Geometric Prototypical Network for Point Cloud Few-Shot Learningcited by 7
  22. 2024Hardness-Aware Scene Synthesis for Semi-Supervised 3D Object Detectioncited by 7
  23. 2022Towards 3D Scene Understanding by Referring Synthetic Modelscited by 6
  24. 2023Sample-adaptive Augmentation for Point Cloud Recognition Against Real-world Corruptionscited by 6
  25. 2024Arti-PG: A Toolbox for Procedurally Synthesizing Large-Scale and Diverse Articulated Objects with Rich Annotationscited by 6
  26. 2022Towards Class-agnostic Tracking Using Feature Decorrelation in Point Cloudscited by 5
  27. 2022ParaPose: Parameter and Domain Randomization Optimization for Pose Estimation using Synthetic Datacited by 5
  28. 2022Context-Aware Data Augmentation for LIDAR 3D Object Detectioncited by 5
  29. 2025NUC-Net: Non-uniform Cylindrical Partition Network for Efficient LiDAR Semantic Segmentationcited by 5
  30. 2021Staying in Shape: Learning Invariant Shape Representations using Contrastive Learningcited by 4
  31. 2021LATFormer: Locality-Aware Point-View Fusion Transformer for 3D Shape Recognitioncited by 4
  32. 2023PointPatchMix: Point Cloud Mixing with Patch Scoringcited by 4
  33. 2023Test-Time Augmentation for 3D Point Cloud Classification and Segmentationcited by 4
  34. 2025A Review of 3D Object Detection with Vision-Language Modelscited by 4
  35. 2023Model2Scene: Learning 3D Scene Representation via Contrastive Language-CAD Models Pre-trainingcited by 3
  36. 2024PointMT: Efficient Point Cloud Analysis with Hybrid MLP-Transformer Architecturecited by 3
  37. 2022Learning Object-level Point Augmentor for Semi-supervised 3D Object Detectioncited by 2
  38. 2025SG-LDM: Semantic-Guided LiDAR Generation via Latent-Aligned Diffusioncited by 2

50 most cited of 65 All 65 (JSON)