mlsanityData to 7 Oct 2026

Point2Node: Correlation Learning of Dynamic-Node for Point Cloud Feature Modeling

arXiv 1912.10775 ↗First posted 23 Dec 2019cs.CVcited by 25

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Abstract (arXiv)

Fully exploring correlation among points in point clouds is essential for their feature modeling. This paper presents a novel end-to-end graph model, named Point2Node, to represent a given point cloud. Point2Node can dynamically explore correlation among all graph nodes from different levels, and adaptively aggregate the learned features. Specifically, first, to fully explore the spatial correlation among points for enhanced feature description, in a high-dimensional node graph, we dynamically integrate the node's correlation with self, local, and non-local nodes. Second, to more effectively integrate learned features, we design a data-aware gate mechanism to self-adaptively aggregate features at the channel level. Extensive experiments on various point cloud benchmarks demonstrate that our method outperforms the state-of-the-art.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Dec 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 13; 2022: 5; 2023: 1; 2024: 3; 2025: 2; 2026: 1.

25 papers.

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  1. 2021PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Cloudscited by 211
  2. 2021PRA-Net: Point Relation-Aware Network for 3D Point Cloud Analysiscited by 58
  3. 2022A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspectivecited by 56
  4. 2021SSPC-Net: Semi-supervised Semantic 3D Point Cloud Segmentation Networkcited by 54
  5. 2021Omni-supervised Point Cloud Segmentation via Gradual Receptive Field Component Reasoningcited by 22
  6. 2021Point Cloud Learning with Transformercited by 15
  7. 2021GA-NET: Global Attention Network for Point Cloud Semantic Segmentationcited by 10
  8. 2022GeoSegNet: Point Cloud Semantic Segmentation via Geometric Encoder-Decoder Modelingcited by 8
  9. 2021DLA-Net: Learning Dual Local Attention Features for Semantic Segmentation of Large-Scale Building Facade Point Cloudscited by 7
  10. 2021Language-Level Semantics Conditioned 3D Point Cloud Segmentationcited by 7
  11. 2021Investigate Indistinguishable Points in Semantic Segmentation of 3D Point Cloudcited by 6
  12. 2025BFANet: Revisiting 3D Semantic Segmentation with Boundary Feature Analysiscited by 5
13 more
  1. 2021PAPooling: Graph-based Position Adaptive Aggregation of Local Geometry in Point Cloudscited by 4
  2. 2021SPNet: Multi-Shell Kernel Convolution for Point Cloud Semantic Segmentationcited by 3
  3. 2024PointMT: Efficient Point Cloud Analysis with Hybrid MLP-Transformer Architecturecited by 3
  4. 2024PointViG: A Lightweight GNN-based Model for Efficient Point Cloud Analysiscited by 2
  5. 2021ActivationNet: Representation learning to predict contact quality of interacting 3-D surfaces in engineering designscited by 1
  6. 2021Cross-Level Cross-Scale Cross-Attention Network for Point Cloud Representationcited by 1
  7. 2023Dynamic Local Feature Aggregation for Learning on Point Cloudscited by 1
  8. 2022Learning task-specific features for 3D pointcloud graph creationcited by 0
  9. 2022Shrinking unit: a Graph Convolution-Based Unit for CNN-like 3D Point Cloud Feature Extractorscited by 0
  10. 2022Adaptive Edge-to-Edge Interaction Learning for Point Cloud Analysiscited by 0
  11. 2024Global Attention-Guided Dual-Domain Point Cloud Feature Learning for Classification and Segmentationcited by 0
  12. 2025SegGraph: Leveraging Graphs of SAM Segments for Few-Shot 3D Part Segmentationcited by 0
  13. 2026PT-WNO: Point Transformer with Wavelet Neural Operator for 3D Point Cloud Semantic Segmentationcited by 0