mlsanityData to 7 Oct 2026

Point Cloud Learning with Transformer

arXiv 2104.13636 ↗First posted 28 Apr 2021cs.CVcited by 15

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Abstract (arXiv)

Remarkable performance from Transformer networks in Natural Language Processing promote the development of these models in dealing with computer vision tasks such as image recognition and segmentation. In this paper, we introduce a novel framework, called Multi-level Multi-scale Point Transformer (MLMSPT) that works directly on the irregular point clouds for representation learning. Specifically, a point pyramid transformer is investigated to model features with diverse resolutions or scales we defined, followed by a multi-level transformer module to aggregate contextual information from different levels of each scale and enhance their interactions. While a multi-scale transformer module is designed to capture the dependencies among representations across different scales. Extensive evaluation on public benchmark datasets demonstrate the effectiveness and the competitive performance of our methods on 3D shape classification, segmentation tasks.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 42 of its 62 references that are in the map. First posted on arXiv 28 Apr 2021.

Builds on

42 of 62 references.

References · 42

  1. 2020End-to-End Object Detection with Transformerscited by 7,384
  2. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  3. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
  4. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,220
  5. 2020Point Transformercited by 1,462
  6. 2019KPConv: Flexible and Deformable Convolution for Point Cloudscited by 1,202
  7. 2021Transformers in Vision: A Surveycited by 1,009
  8. 2018PointCNN: Convolution On X-Transformed Pointscited by 764
  9. 2020PCT: Point cloud transformercited by 730
  10. 2018PointConv: Deep Convolutional Networks on 3D Point Cloudscited by 656
  11. 2019RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Cloudscited by 584
  12. 2015Multi-view Convolutional Neural Networks for 3D Shape Recognitioncited by 564
30 more
  1. 2019Deep Learning for 3D Point Clouds: A Surveycited by 557
  2. 2017Large-scale Point Cloud Semantic Segmentation with Superpoint Graphscited by 384
  3. 2019Relation-Shape Convolutional Neural Network for Point Cloud Analysiscited by 346
  4. 2016OctNet: Learning Deep 3D Representations at High Resolutionscited by 340
  5. 2019Deep Closest Point: Learning Representations for Point Cloud Registrationcited by 318
  6. 2017Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphscited by 307
  7. 2018SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filterscited by 261
  8. 2021PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Cloudscited by 211
  9. 2018SO-Net: Self-Organizing Network for Point Cloud Analysiscited by 210
  10. 2017SEGCloud: Semantic Segmentation of 3D Point Cloudscited by 178
  11. 2017SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentationcited by 168
  12. 2021Deep Parametric Continuous Convolutional Neural Networkscited by 161
  13. 2018Point Convolutional Neural Networks by Extension Operatorscited by 154
  14. 2020PF-Net: Point Fractal Network for 3D Point Cloud Completioncited by 146
  15. 2017Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Modelscited by 142
  16. 2018Point2Sequence: Learning the Shape Representation of 3D Point Clouds with an Attention-based Sequence to Sequence Networkcited by 100
  17. 2020Feature Pyramid Transformercited by 94
  18. 2018Local Spectral Graph Convolution for Point Set Feature Learningcited by 84
  19. 2020Learning multiview 3D point cloud registrationcited by 78
  20. 2018RGCNN: Regularized Graph CNN for Point Cloud Segmentationcited by 70
  21. 2019Spherical Kernel for Efficient Graph Convolution on 3D Point Cloudscited by 57
  22. 2020FPConv: Learning Local Flattening for Point Convolutioncited by 55
  23. 2019Octree guided CNN with Spherical Kernels for 3D Point Cloudscited by 50
  24. 2020Dense-Resolution Network for Point Cloud Classification and Segmentationcited by 45
  25. 2019Adaptive Hierarchical Down-Sampling for Point Cloud Classificationcited by 40
  26. 2020Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentationcited by 40
  27. 2019Point2Node: Correlation Learning of Dynamic-Node for Point Cloud Feature Modelingcited by 25
  28. 2018Pointwise Rotation-Invariant Network with Adaptive Sampling and 3D Spherical Voxel Convolutioncited by 16
  29. 2020PBP-Net: Point Projection and Back-Projection Network for 3D Point Cloud Segmentationcited by 4
  30. 2020Shape-Oriented Convolution Neural Network for Point Cloud Analysiscited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 7; 2023: 3; 2024: 3; 2025: 0; 2026: 1.

15 papers.

Sort:

  1. 2022Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Frameworkcited by 376
  2. 2021EPNet++: Cascade Bi-directional Fusion for Multi-Modal 3D Object Detectioncited by 49
  3. 2022Transformers in 3D Point Clouds: A Surveycited by 31
  4. 20223DCTN: 3D Convolution-Transformer Network for Point Cloud Classificationcited by 25
  5. 2022Recent Advances in Vision Transformer: A Survey and Outlook of Recent Workcited by 21
  6. 20223D Vision with Transformers: A Surveycited by 21
  7. 2022APP-Net: Auxiliary-point-based Push and Pull Operations for Efficient Point Cloud Classificationcited by 4
  8. 2023Exploiting Inductive Bias in Transformer for Point Cloud Classification and Segmentationcited by 2
  9. 2024Rethinking Attention Module Design for Point Cloud Analysiscited by 2
  10. 20223DGTN: 3D Dual-Attention GLocal Transformer Network for Point Cloud Classification and Segmentationcited by 0
  11. 2023Multi-scale Geometry-aware Transformer for 3D Point Cloud Classificationcited by 0
  12. 2023pCTFusion: Point Convolution-Transformer Fusion with Semantic Aware Loss for Outdoor LiDAR Point Cloud Segmentationcited by 0
3 more
  1. 2024Gap Completion in Point Cloud Scene occluded by Vehicles using SGC-Netcited by 0
  2. 2024Global Attention-Guided Dual-Domain Point Cloud Feature Learning for Classification and Segmentationcited by 0
  3. 2026A Robust Point Cloud Analysis Framework Inspired By Primary Visual Cortexcited by 0