mlsanityData to 7 Oct 2026

SO-Net: Self-Organizing Network for Point Cloud Analysis

arXiv 1803.04249 ↗First posted 12 Mar 2018cs.CVcited by 210

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Abstract (arXiv)

This paper presents SO-Net, a permutation invariant architecture for deep learning with orderless point clouds. The SO-Net models the spatial distribution of point cloud by building a Self-Organizing Map (SOM). Based on the SOM, SO-Net performs hierarchical feature extraction on individual points and SOM nodes, and ultimately represents the input point cloud by a single feature vector. The receptive field of the network can be systematically adjusted by conducting point-to-node k nearest neighbor search. In recognition tasks such as point cloud reconstruction, classification, object part segmentation and shape retrieval, our proposed network demonstrates performance that is similar with or better than state-of-the-art approaches. In addition, the training speed is significantly faster than existing point cloud recognition networks because of the parallelizability and simplicity of the proposed architecture. Our code is available at the project website. https://github.com/lijx10/SO-Net

Key facts. Cited by 210 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Mar 2018.

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Citing papers per year: 2021: 78; 2022: 42; 2023: 49; 2024: 22; 2025: 12; 2026: 7.

210 papers.

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  1. 2021Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modelingcited by 647
  2. 2022Masked Autoencoders for Point Cloud Self-supervised Learningcited by 456
  3. 2022Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-trainingcited by 263
  4. 2021Self-Supervised Pretraining of 3D Features on any Point-Cloudcited by 217
  5. 2021PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Cloudscited by 211
  6. 2022Geometric Transformer for Fast and Robust Point Cloud Registrationcited by 200
  7. 2022CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understandingcited by 198
  8. 2021Spatio-temporal Self-Supervised Representation Learning for 3D Point Cloudscited by 180
  9. 2021Walk in the Cloud: Learning Curves for Point Clouds Shape Analysiscited by 172
  10. 2021CoFiNet: Reliable Coarse-to-fine Correspondences for Robust Point Cloud Registrationcited by 151
  11. 2021Revisiting Point Cloud Shape Classification with a Simple and Effective Baselinecited by 145
  12. 2022Learning 3D Representations from 2D Pre-trained Models via Image-to-Point Masked Autoencoderscited by 143
38 more
  1. 2023PointGPT: Auto-regressively Generative Pre-training from Point Cloudscited by 115
  2. 2021Beyond Self-attention: External Attention using Two Linear Layers for Visual Taskscited by 109
  3. 2023GeoTransformer: Fast and Robust Point Cloud Registration with Geometric Transformercited by 83
  4. 2021Equivariant Point Network for 3D Point Cloud Analysiscited by 81
  5. 2021Learning to Stylize Novel Viewscited by 76
  6. 2021PointMixer: MLP-Mixer for Point Cloud Understandingcited by 71
  7. 2024Point Mamba: A Novel Point Cloud Backbone Based on State Space Model with Octree-Based Ordering Strategycited by 71
  8. 2021Adaptive Graph Convolution for Point Cloud Analysiscited by 60
  9. 2021DeepI2P: Image-to-Point Cloud Registration via Deep Classificationcited by 58
  10. 2021PRA-Net: Point Relation-Aware Network for 3D Point Cloud Analysiscited by 58
  11. 2021Semantics for Robotic Mapping, Perception and Interaction: A Surveycited by 56
  12. 2021Generative Zero-Shot Learning for Semantic Segmentation of 3D Point Cloudscited by 55
  13. 2022Unsupervised Point Cloud Representation Learning with Deep Neural Networks: A Surveycited by 54
  14. 2021Self-Contrastive Learning with Hard Negative Sampling for Self-supervised Point Cloud Learningcited by 52
  15. 2023Joint-MAE: 2D-3D Joint Masked Autoencoders for 3D Point Cloud Pre-trainingcited by 52
  16. 2021Learning Semantic Segmentation of Large-Scale Point Clouds with Random Samplingcited by 51
  17. 2022Implicit Autoencoder for Point-Cloud Self-Supervised Representation Learningcited by 46
  18. 2021PatchFormer: An Efficient Point Transformer with Patch Attentioncited by 43
  19. 2022CorrI2P: Deep Image-to-Point Cloud Registration via Dense Correspondencecited by 43
  20. 2021PVT: Point-Voxel Transformer for Point Cloud Learningcited by 42
  21. 2021Image2Point: 3D Point-Cloud Understanding with 2D Image Pretrained Modelscited by 41
  22. 2021Zero-Shot Learning on 3D Point Cloud Objects and Beyondcited by 38
  23. 2023GeoMAE: Masked Geometric Target Prediction for Self-supervised Point Cloud Pre-Trainingcited by 38
  24. 2021RandomRooms: Unsupervised Pre-training from Synthetic Shapes and Randomized Layouts for 3D Object Detectioncited by 37
  25. 2021Voxel-based Network for Shape Completion by Leveraging Edge Generationcited by 32
  26. 2021Orienting Point Clouds with Dipole Propagationcited by 31
  27. 2021Geometry-Aware Self-Training for Unsupervised Domain Adaptationon Object Point Cloudscited by 31
  28. 2021Deep Learning Based 3D Segmentation: A Surveycited by 30
  29. 20213D Pose Transfer with Correspondence Learning and Mesh Refinementcited by 30
  30. 2022Masked Autoencoders in 3D Point Cloud Representation Learningcited by 30
  31. 2021Learning Inner-Group Relations on Point Cloudscited by 29
  32. 2022Weakly Supervised Semantic Segmentation for Large-Scale Point Cloudcited by 27
  33. 20232D3D-MATR: 2D-3D Matching Transformer for Detection-free Registration between Images and Point Cloudscited by 27
  34. 2021Dual Transformer for Point Cloud Analysiscited by 26
  35. 2023Deep Learning-based 3D Point Cloud Classification: A Systematic Survey and Outlookcited by 24
  36. 2024Hide in Thicket: Generating Imperceptible and Rational Adversarial Perturbations on 3D Point Cloudscited by 24
  37. 20213D Medical Point Transformer: Introducing Convolution to Attention Networks for Medical Point Cloud Analysiscited by 23
  38. 2022Refine-Net: Normal Refinement Neural Network for Noisy Point Cloudscited by 23

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