mlsanityData to 14 Oct 2026

PCT: Point cloud transformer

arXiv 2012.09688 ↗First posted 17 Dec 2020cs.CVcited by 732

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Abstract (arXiv)

The irregular domain and lack of ordering make it challenging to design deep neural networks for point cloud processing. This paper presents a novel framework named Point Cloud Transformer(PCT) for point cloud learning. PCT is based on Transformer, which achieves huge success in natural language processing and displays great potential in image processing. It is inherently permutation invariant for processing a sequence of points, making it well-suited for point cloud learning. To better capture local context within the point cloud, we enhance input embedding with the support of farthest point sampling and nearest neighbor search. Extensive experiments demonstrate that the PCT achieves the state-of-the-art performance on shape classification, part segmentation and normal estimation tasks.

Key facts. Cited by 732 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Dec 2020.

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Citing papers per year: 2021: 91; 2022: 161; 2023: 159; 2024: 153; 2025: 110; 2026: 58.

732 papers.

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  1. 2021Perceiver: General Perception with Iterative Attentioncited by 1,104
  2. 2021Transformers in Vision: A Surveycited by 1,013
  3. 2021Multiscale Vision Transformerscited by 897
  4. 2022AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognitioncited by 719
  5. 2021Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modelingcited by 653
  6. 2023Point Transformer V3: Simpler, Faster, Strongercited by 589
  7. 2021PointCLIP: Point Cloud Understanding by CLIPcited by 463
  8. 2022Masked Autoencoders for Point Cloud Self-supervised Learningcited by 459
  9. 20233DShape2VecSet: A 3D Shape Representation for Neural Fields and Generative Diffusion Modelscited by 458
  10. 2022PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategiescited by 436
  11. 2022Multimodal Learning with Transformers: A Surveycited by 400
  12. 2022Point Transformer V2: Grouped Vector Attention and Partition-based Poolingcited by 388
38 more
  1. 2022Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Frameworkcited by 379
  2. 2023OmniObject3D: Large-Vocabulary 3D Object Dataset for Realistic Perception, Reconstruction and Generationcited by 326
  3. 2024A Survey on Vision-Language-Action Models for Embodied AIcited by 279
  4. 2022FlowFormer: A Transformer Architecture for Optical Flowcited by 275
  5. 2022ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understandingcited by 267
  6. 2022Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-trainingcited by 264
  7. 2022SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentationcited by 252
  8. 2021TransWeather: Transformer-based Restoration of Images Degraded by Adverse Weather Conditionscited by 244
  9. 2021Attention Mechanisms in Computer Vision: A Surveycited by 232
  10. 2023MVImgNet: A Large-scale Dataset of Multi-view Imagescited by 220
  11. 2022Visual Attention Networkcited by 210
  12. 2021FaceFormer: Speech-Driven 3D Facial Animation with Transformerscited by 209
  13. 2021Rethinking and Improving Relative Position Encoding for Vision Transformercited by 207
  14. 2021Embracing Single Stride 3D Object Detector with Sparse Transformercited by 193
  15. 2021Walk in the Cloud: Learning Curves for Point Clouds Shape Analysiscited by 172
  16. 2021SPViT: Enabling Faster Vision Transformers via Soft Token Pruningcited by 167
  17. 2024PointMamba: A Simple State Space Model for Point Cloud Analysiscited by 167
  18. 2021SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformercited by 164
  19. 2023Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretrainingcited by 164
  20. 2022Masked Discrimination for Self-Supervised Learning on Point Cloudscited by 155
  21. 2022Learning 3D Representations from 2D Pre-trained Models via Image-to-Point Masked Autoencoderscited by 144
  22. 2021Sinkformers: Transformers with Doubly Stochastic Attentioncited by 138
  23. 2022Anti-Oversmoothing in Deep Vision Transformers via the Fourier Domain Analysis: From Theory to Practicecited by 136
  24. 2023Meta-Transformer: A Unified Framework for Multimodal Learningcited by 136
  25. 2021Query2Label: A Simple Transformer Way to Multi-Label Classificationcited by 128
  26. 2021IA-RED2: Interpretability-Aware Redundancy Reduction for Vision Transformerscited by 127
  27. 2023Transformer-Based Visual Segmentation: A Surveycited by 124
  28. 2022Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning?cited by 122
  29. 2021A Survey of Visual Transformerscited by 117
  30. 2023FlowFormer++: Masked Cost Volume Autoencoding for Pretraining Optical Flow Estimationcited by 114
  31. 2022Is Attention All That NeRF Needs?cited by 113
  32. 2023Swin3D: A Pretrained Transformer Backbone for 3D Indoor Scene Understandingcited by 113
  33. 2021Beyond Self-attention: External Attention using Two Linear Layers for Visual Taskscited by 109
  34. 2023OctFormer: Octree-based Transformers for 3D Point Cloudscited by 109
  35. 2021Improving 3D Object Detection with Channel-wise Transformercited by 108
  36. 2021Fast Point Transformercited by 104
  37. 2022CALIP: Zero-Shot Enhancement of CLIP with Parameter-free Attentioncited by 103
  38. 2022Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Cloudscited by 102

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