mlsanityData to 7 Oct 2026

Dual Transformer for Point Cloud Analysis

arXiv 2104.13044 ↗First posted 27 Apr 2021cs.CV cs.MMcited by 26

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Abstract (arXiv)

Following the tremendous success of transformer in natural language processing and image understanding tasks, in this paper, we present a novel point cloud representation learning architecture, named Dual Transformer Network (DTNet), which mainly consists of Dual Point Cloud Transformer (DPCT) module. Specifically, by aggregating the well-designed point-wise and channel-wise multi-head self-attention models simultaneously, DPCT module can capture much richer contextual dependencies semantically from the perspective of position and channel. With the DPCT module as a fundamental component, we construct the DTNet for performing point cloud analysis in an end-to-end manner. Extensive quantitative and qualitative experiments on publicly available benchmarks demonstrate the effectiveness of our proposed transformer framework for the tasks of 3D point cloud classification and segmentation, achieving highly competitive performance in comparison with the state-of-the-art approaches.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). Builds on 35 of its 63 references that are in the map. First posted on arXiv 27 Apr 2021.

Builds on

35 of 63 references.

References · 35

  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. 2015Spatial Transformer Networkscited by 1,423
  7. 2019KPConv: Flexible and Deformable Convolution for Point Cloudscited by 1,202
  8. 2018Image Transformercited by 770
  9. 2020PCT: Point cloud transformercited by 730
  10. 2019RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Cloudscited by 584
  11. 2015Multi-view Convolutional Neural Networks for 3D Shape Recognitioncited by 564
  12. 2019Deep Learning for 3D Point Clouds: A Surveycited by 557
23 more
  1. 2020PointContrast: Unsupervised Pre-training for 3D Point Cloud Understandingcited by 502
  2. 2016OctNet: Learning Deep 3D Representations at High Resolutionscited by 340
  3. 2019Deep Closest Point: Learning Representations for Point Cloud Registrationcited by 318
  4. 2017Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphscited by 307
  5. 2018SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filterscited by 261
  6. 2016Volumetric and Multi-View CNNs for Object Classification on 3D Datacited by 240
  7. 2020PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Samplingcited by 238
  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. 2019Local Relation Networks for Image Recognitioncited by 210
  11. 2018Point Convolutional Neural Networks by Extension Operatorscited by 154
  12. 2017Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Modelscited by 142
  13. 2019TANet: Robust 3D Object Detection from Point Clouds with Triple Attentioncited by 108
  14. 2018Point2Sequence: Learning the Shape Representation of 3D Point Clouds with an Attention-based Sequence to Sequence Networkcited by 100
  15. 2018Local Spectral Graph Convolution for Point Set Feature Learningcited by 84
  16. 2020Learning multiview 3D point cloud registrationcited by 78
  17. 2018RGCNN: Regularized Graph CNN for Point Cloud Segmentationcited by 70
  18. 2019Spherical Kernel for Efficient Graph Convolution on 3D Point Cloudscited by 57
  19. 2020FPConv: Learning Local Flattening for Point Convolutioncited by 55
  20. 2019Octree guided CNN with Spherical Kernels for 3D Point Cloudscited by 50
  21. 2020Dense-Resolution Network for Point Cloud Classification and Segmentationcited by 45
  22. 2019Adaptive Hierarchical Down-Sampling for Point Cloud Classificationcited by 40
  23. 2016Multichannel Variable-Size Convolution for Sentence Classificationcited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 9; 2023: 5; 2024: 5; 2025: 2; 2026: 4.

26 papers.

Sort:

  1. 2022Transformers in 3D Point Clouds: A Surveycited by 31
  2. 2022Recent Advances in Vision Transformer: A Survey and Outlook of Recent Workcited by 21
  3. 20223D Vision with Transformers: A Surveycited by 21
  4. 20223D-OAE: Occlusion Auto-Encoders for Self-Supervised Learning on Point Cloudscited by 18
  5. 2021DSPoint: Dual-scale Point Cloud Recognition with High-frequency Fusioncited by 17
  6. 2026Align then Adapt: Rethinking Parameter-Efficient Transfer Learning in 4D Perceptioncited by 17
  7. 2022Multi-Tailed Vision Transformer for Efficient Inferencecited by 10
  8. 2022Pyramid Fusion Transformer for Semantic Segmentationcited by 6
  9. 2022Improving transferability of 3D adversarial attacks with scale and shear transformationscited by 5
  10. 2024PointGL: A Simple Global-Local Framework for Efficient Point Cloud Analysiscited by 5
  11. 2024RISurConv: Rotation Invariant Surface Attention-Augmented Convolutions for 3D Point Cloud Classification and Segmentationcited by 5
  12. 2024Neuromorphic Vision-based Motion Segmentation with Graph Transformer Neural Networkcited by 4
14 more
  1. 2023SAT: Size-Aware Transformer for 3D Point Cloud Semantic Segmentationcited by 3
  2. 2024PointMT: Efficient Point Cloud Analysis with Hybrid MLP-Transformer Architecturecited by 3
  3. 2025Interpretable Affordance Detection on 3D Point Clouds with Probabilistic Prototypescited by 3
  4. 2023Dynamic Clustering Transformer Network for Point Cloud Segmentationcited by 2
  5. 2024Rethinking Attention Module Design for Point Cloud Analysiscited by 2
  6. 2022Hyperbolic Cosine Transformer for LiDAR 3D Object Detectioncited by 1
  7. 2023Object Preserving Siamese Network for Single Object Tracking on Point Cloudscited by 1
  8. 2023Sampled Transformer for Point Setscited by 1
  9. 2025PointODE: Lightweight Point Cloud Learning with Neural Ordinary Differential Equations on Edgecited by 1
  10. 20223DGTN: 3D Dual-Attention GLocal Transformer Network for Point Cloud Classification and Segmentationcited by 0
  11. 2023Bidirectional Knowledge Reconfiguration for Lightweight Point Cloud Analysiscited by 0
  12. 2026Efficient Point Cloud Processing with High-Dimensional Positional Encoding and Non-Local MLPscited by 0
  13. 2026Channel-Level Relation to Attentive Aggregation with Neighborhood-Homogeneity Constraint for Point Cloud Analysiscited by 0
  14. 2026A Cookbook of 3D Vision: Data, Learning Paradigms, and Applicationcited by 0