mlsanityData to 7 Oct 2026

Deep Learning-based 3D Point Cloud Classification: A Systematic Survey and Outlook

arXiv 2311.02608 ↗First posted 5 Nov 2023cs.CVcited by 24

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Abstract (arXiv)

In recent years, point cloud representation has become one of the research hotspots in the field of computer vision, and has been widely used in many fields, such as autonomous driving, virtual reality, robotics, etc. Although deep learning techniques have achieved great success in processing regular structured 2D grid image data, there are still great challenges in processing irregular, unstructured point cloud data. Point cloud classification is the basis of point cloud analysis, and many deep learning-based methods have been widely used in this task. Therefore, the purpose of this paper is to provide researchers in this field with the latest research progress and future trends. First, we introduce point cloud acquisition, characteristics, and challenges. Second, we review 3D data representations, storage formats, and commonly used datasets for point cloud classification. We then summarize deep learning-based methods for point cloud classification and complement recent research work. Next, we compare and analyze the performance of the main methods. Finally, we discuss some challenges and future directions for point cloud classification.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). Builds on 68 of its 117 references that are in the map. First posted on arXiv 5 Nov 2023.

Builds on

68 of 117 references.

References · 68

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38 more
  1. 2018PointCNN: Convolution On X-Transformed Pointscited by 764
  2. 2020PCT: Point cloud transformercited by 730
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50 most cited of 68 All 68 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 8; 2025: 7; 2026: 9.

24 papers.

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  1. 2024PointNet with KAN versus PointNet with MLP for 3D Classification and Segmentation of Point Setscited by 15
  2. 2024Multi-Pair Temporal Sentence Grounding via Multi-Thread Knowledge Transfer Networkcited by 15
  3. 2025Taylor Series-Inspired Local Structure Fitting Network for Few-shot Point Cloud Semantic Segmentationcited by 14
  4. 2024GPSFormer: A Global Perception and Local Structure Fitting-based Transformer for Point Cloud Understandingcited by 12
  5. 2026Rethinking Weakly-supervised Video Temporal Grounding From a Game Perspectivecited by 8
  6. 2024Deep Learning on 3D Semantic Segmentation: A Detailed Reviewcited by 6
  7. 2024On the Efficiency of NLP-Inspired Methods for Tabular Deep Learningcited by 6
  8. 2025Quantum-enhanced Computer Vision: Going Beyond Classical Algorithmscited by 5
  9. 2024MaRINeR: Enhancing Novel Views by Matching Rendered Images with Nearby Referencescited by 4
  10. 2024Advancements in Point Cloud-Based 3D Defect Detection and Classification for Industrial Systems: A Comprehensive Surveycited by 3
  11. 2025Token Adaptation via Side Graph Convolution for Efficient Fine-tuning of 3D Point Cloud Transformerscited by 2
  12. 2025Evaluating the printability of stl files with MLcited by 1
12 more
  1. 2025Self-Supervised Cross-Modal Learning for Image-to-Point Cloud Registrationcited by 1
  2. 2026Overview and Comparison of AVS Point Cloud Compression Standardcited by 1
  3. 2024Sampling Foundational Transformer: A Theoretical Perspectivecited by 0
  4. 2025Explicit Context-Driven Neural Acoustic Modeling for High-Fidelity RIR Generationcited by 0
  5. 2025A Storage-Efficient Feature for 3D Concrete Defect Segmentation to Replace Normal Vectorcited by 0
  6. 2026Joint Optimization of Storage and Loading for High-Performance 3D Point Cloud Data Processingcited by 0
  7. 2026Layered Quantum Architecture Search for 3D Point Cloud Classificationcited by 0
  8. 2026Spatially-Aware Evaluation Framework for Aerial LiDAR Point Cloud Semantic Segmentation: Distance-Based Metrics on Challenging Regionscited by 0
  9. 2026A Unified Non-Parametric and Interpretable Point Cloud Analysis via t-FCW Graph Representationcited by 0
  10. 2026Learning Representations from 3D Gaussian Splatscited by 0
  11. 2026Synthetic LiDAR Data Generation and Deterministic Downsampling for Point Cloud Classification on the Edgecited by 0
  12. 2026Sign Language Recognition Using Original and Synthetic Depth Image Based Point Cloud Data Modelscited by 0