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

arXiv: https://arxiv.org/abs/1912.10775 · HTML: https://mlsanity.com/paper/1912.10775/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Dec 2019.

## Built on by (most cited first, 25 of 25)

- [PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Clouds](https://mlsanity.com/paper/2103.14635/) (2021)
- [PRA-Net: Point Relation-Aware Network for 3D Point Cloud Analysis](https://mlsanity.com/paper/2112.04903/) (2021)
- [A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective](https://mlsanity.com/paper/2209.13232/) (2022)
- [SSPC-Net: Semi-supervised Semantic 3D Point Cloud Segmentation Network](https://mlsanity.com/paper/2104.07861/) (2021)
- [Omni-supervised Point Cloud Segmentation via Gradual Receptive Field Component Reasoning](https://mlsanity.com/paper/2105.10203/) (2021)
- [Point Cloud Learning with Transformer](https://mlsanity.com/paper/2104.13636/) (2021)
- [GA-NET: Global Attention Network for Point Cloud Semantic Segmentation](https://mlsanity.com/paper/2107.03101/) (2021)
- [GeoSegNet: Point Cloud Semantic Segmentation via Geometric Encoder-Decoder Modeling](https://mlsanity.com/paper/2207.06766/) (2022)
- [DLA-Net: Learning Dual Local Attention Features for Semantic Segmentation of Large-Scale Building Facade Point Clouds](https://mlsanity.com/paper/2106.00376/) (2021)
- [Language-Level Semantics Conditioned 3D Point Cloud Segmentation](https://mlsanity.com/paper/2107.00430/) (2021)
- [Investigate Indistinguishable Points in Semantic Segmentation of 3D Point Cloud](https://mlsanity.com/paper/2103.10339/) (2021)
- [BFANet: Revisiting 3D Semantic Segmentation with Boundary Feature Analysis](https://mlsanity.com/paper/2503.12539/) (2025)
- [PAPooling: Graph-based Position Adaptive Aggregation of Local Geometry in Point Clouds](https://mlsanity.com/paper/2111.14067/) (2021)
- [SPNet: Multi-Shell Kernel Convolution for Point Cloud Semantic Segmentation](https://mlsanity.com/paper/2109.11610/) (2021)
- [PointMT: Efficient Point Cloud Analysis with Hybrid MLP-Transformer Architecture](https://mlsanity.com/paper/2408.05508/) (2024)
- [PointViG: A Lightweight GNN-based Model for Efficient Point Cloud Analysis](https://mlsanity.com/paper/2407.00921/) (2024)
- [ActivationNet: Representation learning to predict contact quality of interacting 3-D surfaces in engineering designs](https://mlsanity.com/paper/2103.11288/) (2021)
- [Cross-Level Cross-Scale Cross-Attention Network for Point Cloud Representation](https://mlsanity.com/paper/2104.13053/) (2021)
- [Dynamic Local Feature Aggregation for Learning on Point Clouds](https://mlsanity.com/paper/2301.02836/) (2023)
- [Learning task-specific features for 3D pointcloud graph creation](https://mlsanity.com/paper/2209.00949/) (2022)
- [Shrinking unit: a Graph Convolution-Based Unit for CNN-like 3D Point Cloud Feature Extractors](https://mlsanity.com/paper/2209.12770/) (2022)
- [Adaptive Edge-to-Edge Interaction Learning for Point Cloud Analysis](https://mlsanity.com/paper/2211.10888/) (2022)
- [Global Attention-Guided Dual-Domain Point Cloud Feature Learning for Classification and Segmentation](https://mlsanity.com/paper/2407.08994/) (2024)
- [SegGraph: Leveraging Graphs of SAM Segments for Few-Shot 3D Part Segmentation](https://mlsanity.com/paper/2512.16143/) (2025)
- [PT-WNO: Point Transformer with Wavelet Neural Operator for 3D Point Cloud Semantic Segmentation](https://mlsanity.com/paper/2606.11466/) (2026)

## 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.