mlsanityData to 14 Oct 2026

Walk in the Cloud: Learning Curves for Point Clouds Shape Analysis

arXiv 2105.01288 ↗First posted 4 May 2021cs.CVcited by 172

Not on the map. Why?

Abstract (arXiv)

Discrete point cloud objects lack sufficient shape descriptors of 3D geometries. In this paper, we present a novel method for aggregating hypothetical curves in point clouds. Sequences of connected points (curves) are initially grouped by taking guided walks in the point clouds, and then subsequently aggregated back to augment their point-wise features. We provide an effective implementation of the proposed aggregation strategy including a novel curve grouping operator followed by a curve aggregation operator. Our method was benchmarked on several point cloud analysis tasks where we achieved the state-of-the-art classification accuracy of 94.2% on the ModelNet40 classification task, instance IoU of 86.8 on the ShapeNetPart segmentation task, and cosine error of 0.11 on the ModelNet40 normal estimation task.

Key facts. Cited by 172 papers in the map (data through 14 Oct 2026). Builds on 40 of its 50 references that are in the map. First posted on arXiv 4 May 2021.

Builds on

40 of 50 references.

References · 40

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  3. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  4. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 7,005
  5. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  6. 2017Squeeze-and-Excitation Networkscited by 4,789
  7. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,401
  8. 2016Categorical Reparameterization with Gumbel-Softmaxcited by 2,739
  9. 2017Non-local Neural Networkscited by 2,293
  10. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,223
  11. 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,435
  12. 2016The Concrete Distribution: A Continuous Relaxation of Discrete Random Variablescited by 1,148
28 more
  1. 2018PointCNN: Convolution On X-Transformed Pointscited by 764
  2. 2020PCT: Point cloud transformercited by 732
  3. 2018PointConv: Deep Convolutional Networks on 3D Point Cloudscited by 657
  4. 2019RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Cloudscited by 585
  5. 2015Multi-view Convolutional Neural Networks for 3D Shape Recognitioncited by 565
  6. 2016Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modelingcited by 558
  7. 2019Graph U-Netscited by 492
  8. 2019Relation-Shape Convolutional Neural Network for Point Cloud Analysiscited by 346
  9. 2016OctNet: Learning Deep 3D Representations at High Resolutionscited by 342
  10. 2016Volumetric and Multi-View CNNs for Object Classification on 3D Datacited by 240
  11. 2020PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Samplingcited by 239
  12. 2021PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Cloudscited by 211
  13. 2018SO-Net: Self-Organizing Network for Point Cloud Analysiscited by 210
  14. 2019Modeling Point Clouds with Self-Attention and Gumbel Subset Samplingcited by 151
  15. 2017Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Modelscited by 142
  16. 2019Grid-GCN for Fast and Scalable Point Cloud Learningcited by 103
  17. 2018Point2Sequence: Learning the Shape Representation of 3D Point Clouds with an Attention-based Sequence to Sequence Networkcited by 100
  18. 2020A Closer Look at Local Aggregation Operators in Point Cloud Analysiscited by 87
  19. 2019PCAN: 3D Attention Map Learning Using Contextual Information for Point Cloud Based Retrievalcited by 68
  20. 2019A-CNN: Annularly Convolutional Neural Networks on Point Cloudscited by 66
  21. 2020JSENet: Joint Semantic Segmentation and Edge Detection Network for 3D Point Cloudscited by 62
  22. 2019Octree guided CNN with Spherical Kernels for 3D Point Cloudscited by 50
  23. 2021Exploiting Edge-Oriented Reasoning for 3D Point-based Scene Graph Analysiscited by 49
  24. 2020Point2Skeleton: Learning Skeletal Representations from Point Cloudscited by 37
  25. 2019Geometry Sharing Network for 3D Point Cloud Classification and Segmentationcited by 33
  26. 2016FPNN: Field Probing Neural Networks for 3D Datacited by 25
  27. 2020Shape-Oriented Convolution Neural Network for Point Cloud Analysiscited by 3
  28. 2020Local-Area-Learning Network: Meaningful Local Areas for Efficient Point Cloud Analysiscited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 14; 2022: 53; 2023: 33; 2024: 35; 2025: 24; 2026: 13.

172 papers.

Sort:

By area:

  1. 2021PointCLIP: Point Cloud Understanding by CLIPcited by 463
  2. 2022PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategiescited by 436
  3. 2022Point Transformer V2: Grouped Vector Attention and Partition-based Poolingcited by 388
  4. 2022Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Frameworkcited by 379
  5. 2023OmniObject3D: Large-Vocabulary 3D Object Dataset for Realistic Perception, Reconstruction and Generationcited by 326
  6. 2022ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understandingcited by 267
  7. 2023MVImgNet: A Large-scale Dataset of Multi-view Imagescited by 220
  8. 2022PointCLIP V2: Prompting CLIP and GPT for Powerful 3D Open-world Learningcited by 187
  9. 2022Learning 3D Representations from 2D Pre-trained Models via Image-to-Point Masked Autoencoderscited by 144
  10. 2022Surface Representation for Point Cloudscited by 84
  11. 2022Benchmarking and Analyzing Point Cloud Classification under Corruptionscited by 70
  12. 2022DP-NeRF: Deblurred Neural Radiance Field with Physical Scene Priorscited by 70
38 more
  1. 2024Point Cloud Mamba: Point Cloud Learning via State Space Modelcited by 66
  2. 2022A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspectivecited by 56
  3. 2022Shape-invariant 3D Adversarial Point Cloudscited by 50
  4. 2021PatchFormer: An Efficient Point Transformer with Patch Attentioncited by 43
  5. 2023Grad-PU: Arbitrary-Scale Point Cloud Upsampling via Gradient Descent with Learned Distance Functionscited by 43
  6. 2023Self-positioning Point-based Transformer for Point Cloud Understandingcited by 40
  7. 2023Parameter is Not All You Need: Starting from Non-Parametric Networks for 3D Point Cloud Analysiscited by 34
  8. 2022Transformers in 3D Point Clouds: A Surveycited by 31
  9. 2022Isometric 3D Adversarial Examples in the Physical Worldcited by 25
  10. 2022Let Images Give You More:Point Cloud Cross-Modal Training for Shape Analysiscited by 24
  11. 2023Deep Learning-based 3D Point Cloud Classification: A Systematic Survey and Outlookcited by 24
  12. 20213D Medical Point Transformer: Introducing Convolution to Attention Networks for Medical Point Cloud Analysiscited by 23
  13. 2022Physical-World Optical Adversarial Attacks on 3D Face Recognitioncited by 23
  14. 2021Voint Cloud: Multi-View Point Cloud Representation for 3D Understandingcited by 22
  15. 2022PointVector: A Vector Representation In Point Cloud Analysiscited by 21
  16. 20233DCoMPaT++: An improved Large-scale 3D Vision Dataset for Compositional Recognitioncited by 21
  17. 2022FBNet: Feedback Network for Point Cloud Completioncited by 20
  18. 2022Distillation with Contrast is All You Need for Self-Supervised Point Cloud Representation Learningcited by 19
  19. 2023Point Cloud Classification Using Content-based Transformer via Clustering in Feature Spacecited by 19
  20. 2022Rethinking the compositionality of point clouds through regularization in the hyperbolic spacecited by 18
  21. 2024PartSTAD: 2D-to-3D Part Segmentation Task Adaptationcited by 18
  22. 2022E2PN: Efficient SE(3)-Equivariant Point Networkcited by 17
  23. 2022Explainability in Graph Neural Networks: An Experimental Surveycited by 16
  24. 2022POS-BERT: Point Cloud One-Stage BERT Pre-Trainingcited by 16
  25. 2022PointDP: Diffusion-driven Purification against Adversarial Attacks on 3D Point Cloud Recognitioncited by 16
  26. 2023ConDaFormer: Disassembled Transformer with Local Structure Enhancement for 3D Point Cloud Understandingcited by 16
  27. 2024Parameter-efficient Prompt Learning for 3D Point Cloud Understandingcited by 16
  28. 2024Unlearnable 3D Point Clouds: Class-wise Transformation Is All You Needcited by 16
  29. 2022HiP: Hierarchical Perceivercited by 15
  30. 2024LSK3DNet: Towards Effective and Efficient 3D Perception with Large Sparse Kernelscited by 15
  31. 2024Backpropagation-free Network for 3D Test-time Adaptationcited by 14
  32. 2022AGConv: Adaptive Graph Convolution on 3D Point Cloudscited by 13
  33. 2022Leveraging Spatio-Temporal Dependency for Skeleton-Based Action Recognitioncited by 13
  34. 2022MM-3DScene: 3D Scene Understanding by Customizing Masked Modeling with Informative-Preserved Reconstruction and Self-Distilled Consistencycited by 12
  35. 2023Decoupled Local Aggregation for Point Cloud Learningcited by 12
  36. 20223DOS: Towards 3D Open Set Learning -- Benchmarking and Understanding Semantic Novelty Detection on Point Cloudscited by 11
  37. 2023TractCloud: Registration-free tractography parcellation with a novel local-global streamline point cloud representationcited by 11
  38. 2021DeltaConv: Anisotropic Operators for Geometric Deep Learning on Point Cloudscited by 10

50 most cited of 172 All 172 (JSON)