mlsanityData to 7 Oct 2026

A comprehensive overview of deep learning techniques for 3D point cloud classification and semantic segmentation

arXiv 2405.11903 ↗First posted 20 May 2024cs.CVcited by 14

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Abstract (arXiv)

Point cloud analysis has a wide range of applications in many areas such as computer vision, robotic manipulation, and autonomous driving. While deep learning has achieved remarkable success on image-based tasks, there are many unique challenges faced by deep neural networks in processing massive, unordered, irregular and noisy 3D points. To stimulate future research, this paper analyzes recent progress in deep learning methods employed for point cloud processing and presents challenges and potential directions to advance this field. It serves as a comprehensive review on two major tasks in 3D point cloud processing-- namely, 3D shape classification and semantic segmentation.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 30 of its 52 references that are in the map. First posted on arXiv 20 May 2024.

Builds on

30 of 52 references.

References · 30

  1. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,220
  2. 2020PCT: Point cloud transformercited by 730
  3. 2020Sparse Single Sweep LiDAR Point Cloud Segmentation via Learning Contextual Shape Priors from Scene Completioncited by 222
  4. 2018Point Convolutional Neural Networks by Extension Operatorscited by 154
  5. 2018Point2Sequence: Learning the Shape Representation of 3D Point Clouds with an Attention-based Sequence to Sequence Networkcited by 100
  6. 2018MeshNet: Mesh Neural Network for 3D Shape Representationcited by 95
  7. 2021Transfer Learning from Synthetic to Real LiDAR Point Cloud for Semantic Segmentationcited by 76
  8. 2022LidarMultiNet: Towards a Unified Multi-Task Network for LiDAR Perceptioncited by 74
  9. 2020Learning Geometry-Disentangled Representation for Complementary Understanding of 3D Object Point Cloudcited by 71
  10. 2018RGCNN: Regularized Graph CNN for Point Cloud Segmentationcited by 70
  11. 2017Paris-Lille-3D: a large and high-quality ground truth urban point cloud dataset for automatic segmentation and classificationcited by 65
  12. 2018Monte Carlo Convolution for Learning on Non-Uniformly Sampled Point Cloudscited by 62
18 more
  1. 2019ConvPoint: Continuous Convolutions for Point Cloud Processingcited by 62
  2. 2021PVT: Point-Voxel Transformer for Point Cloud Learningcited by 42
  3. 2021The Hessigheim 3D (H3D) Benchmark on Semantic Segmentation of High-Resolution 3D Point Clouds and Textured Meshes from UAV LiDAR and Multi-View-Stereocited by 37
  4. 2013Rotational Projection Statistics for 3D Local Surface Description and Object Recognitioncited by 36
  5. 2018PVNet: A Joint Convolutional Network of Point Cloud and Multi-View for 3D Shape Recognitioncited by 36
  6. 2020MeshWalker: Deep Mesh Understanding by Random Walkscited by 32
  7. 2019SRINet: Learning Strictly Rotation-Invariant Representations for Point Cloud Classification and Segmentationcited by 29
  8. 2019JSNet: Joint Instance and Semantic Segmentation of 3D Point Cloudscited by 27
  9. 2020Campus3D: A Photogrammetry Point Cloud Benchmark for Hierarchical Understanding of Outdoor Scenecited by 27
  10. 2020Semantic Segmentation on Swiss3DCities: A Benchmark Study on Aerial Photogrammetric 3D Pointcloud Datasetcited by 22
  11. 2021FPS-Net: A Convolutional Fusion Network for Large-Scale LiDAR Point Cloud Segmentationcited by 14
  12. 2021DeltaConv: Anisotropic Operators for Geometric Deep Learning on Point Cloudscited by 10
  13. 2018PVRNet: Point-View Relation Neural Network for 3D Shape Recognitioncited by 8
  14. 2019Point Cloud Processing via Recurrent Set Encodingcited by 5
  15. 2022PointSCNet: Point Cloud Structure and Correlation Learning Based on Space Filling Curve-Guided Samplingcited by 4
  16. 2021PIG-Net: Inception based Deep Learning Architecture for 3D Point Cloud Segmentationcited by 3
  17. 2020ODFNet: Using orientation distribution functions to characterize 3D point cloudscited by 2
  18. 2021PointCaps: Raw Point Cloud Processing using Capsule Networks with Euclidean Distance Routingcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 8; 2026: 6.

14 papers.

Sort:

  1. 2025PartInstruct: Part-level Instruction Following for Fine-grained Robot Manipulationcited by 4
  2. 2025CrossJEPA: Cross-Modal Joint-Embedding Predictive Architecture for Efficient 3D Representation Learning from 2D Imagescited by 4
  3. 2025A Deep Learning Approach to Identify Rock Bolts in Complex 3D Point Clouds of Underground Mines Captured Using Mobile Laser Scannerscited by 3
  4. 2025Imitating Radiological Scrolling: A Global-Local Attention Model for 3D Chest CT Volumes Multi-Label Anomaly Classificationcited by 2
  5. 2025TACO-Net: Topological Signatures Triumph in 3D Object Classificationcited by 2
  6. 2025SemanticBridge - A Dataset for 3D Semantic Segmentation of Bridges and Domain Gap Analysiscited by 1
  7. 2025HLS4PC: A Parametrizable Framework For Accelerating Point-Based 3D Point Cloud Models on FPGAcited by 1
  8. 2026Overview and Comparison of AVS Point Cloud Compression Standardcited by 1
  9. 2026PointGS: Semantic-Consistent Unsupervised 3D Point Cloud Segmentation with 3D Gaussian Splattingcited by 1
  10. 2025Graph-Guided Dual-Level Augmentation for 3D Scene Segmentationcited by 0
  11. 2026Layered Quantum Architecture Search for 3D Point Cloud Classificationcited by 0
  12. 2026Data-Efficient Semantic Segmentation of 3D Point Clouds via Open-Vocabulary Image Segmentation-based Pseudo-Labelingcited by 0
2 more
  1. 2026A Unified Non-Parametric and Interpretable Point Cloud Analysis via t-FCW Graph Representationcited by 0
  2. 2026Bayesian methods and Markov chain Monte Carlo algorithms for curve reconstruction and point cloud data analysiscited by 0