mlsanityData to 7 Oct 2026

Advancements in Point Cloud Data Augmentation for Deep Learning: A Survey

arXiv 2308.12113 ↗First posted 23 Aug 2023cs.CVcited by 24

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Abstract (arXiv)

Deep learning (DL) has become one of the mainstream and effective methods for point cloud analysis tasks such as detection, segmentation and classification. To reduce overfitting during training DL models and improve model performance especially when the amount and/or diversity of training data are limited, augmentation is often crucial. Although various point cloud data augmentation methods have been widely used in different point cloud processing tasks, there are currently no published systematic surveys or reviews of these methods. Therefore, this article surveys these methods, categorizing them into a taxonomy framework that comprises basic and specialized point cloud data augmentation methods. Through a comprehensive evaluation of these augmentation methods, this article identifies their potentials and limitations, serving as a useful reference for choosing appropriate augmentation methods. In addition, potential directions for future research are recommended. This survey contributes to providing a holistic overview of the current state of point cloud data augmentation, promoting its wider application and development.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). Builds on 141 of its 194 references that are in the map. First posted on arXiv 23 Aug 2023.

Builds on

141 of 194 references.

References · 141

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38 more
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50 most cited of 141 All 141 (JSON)

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Citing papers per year: 2024: 11; 2025: 10; 2026: 3.

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  1. 2024Samba: Semantic Segmentation of Remotely Sensed Images with State Space Modelcited by 37
  2. 2024Rethinking Scanning Strategies with Vision Mamba in Semantic Segmentation of Remote Sensing Imagery: An Experimental Studycited by 24
  3. 2024Seg-LSTM: Performance of xLSTM for Semantic Segmentation of Remotely Sensed Imagescited by 5
  4. 2025BFANet: Revisiting 3D Semantic Segmentation with Boundary Feature Analysiscited by 5
  5. 2024Enhancing Environmental Monitoring through Multispectral Imaging: The WasteMS Dataset for Semantic Segmentation of Lakeside Wastecited by 3
  6. 2025CAGE: Continuity-Aware edGE Network Unlocks Robust Floorplan Reconstructioncited by 3
  7. 2024Trainable Pointwise Decoder Module for Point Cloud Segmentationcited by 2
  8. 2024Exploring Scene Affinity for Semi-Supervised LiDAR Semantic Segmentationcited by 2
  9. 2024QDGset: A Large Scale Grasping Dataset Generated with Quality-Diversitycited by 2
  10. 2024Evaluating the Impact of Point Cloud Colorization on Semantic Segmentation Accuracycited by 2
  11. 2024Horticultural Temporal Fruit Monitoring via 3D Instance Segmentation and Re-Identification using Colored Point Cloudscited by 1
  12. 2025ClassWise-CRF: Category-Specific Fusion for Enhanced Semantic Segmentation of Remote Sensing Imagerycited by 1
12 more
  1. 2025Memorization in 3D Shape Generation: An Empirical Studycited by 1
  2. 2026Overview and Comparison of AVS Point Cloud Compression Standardcited by 1
  3. 2024Enhancing Sampling Protocol for Point Cloud Classification Against Corruptionscited by 0
  4. 2024MSCrackMamba: Leveraging Vision Mamba for Crack Detection in Fused Multispectral Imagerycited by 0
  5. 2025An Experimental Study of SOTA LiDAR Segmentation Modelscited by 0
  6. 2025A Data-Centric Approach to 3D Semantic Segmentation of Railway Scenescited by 0
  7. 2025FA-KPConv: Introducing Euclidean Symmetries to KPConv via Frame Averagingcited by 0
  8. 2025Multi-modal Multi-task Pre-training for Improved Point Cloud Understandingcited by 0
  9. 2025Skeleton-based Robust Registration Framework for Corrupted 3D Point Cloudscited by 0
  10. 2025Large Language Models and 3D Vision for Intelligent Robotic Perception and Autonomycited by 0
  11. 2026A generalised pre-training strategy for deep learning networks in semantic segmentation of remotely sensed imagescited by 0
  12. 2026AnchorSplat: Fast and Structure Consistent Detail Synthesis for Gaussian Splattingcited by 0