mlsanityData to 7 Oct 2026

Adaptive Graph Convolution for Point Cloud Analysis

arXiv 2108.08035 ↗First posted 18 Aug 2021cs.CVcited by 60

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Abstract (arXiv)

Convolution on 3D point clouds that generalized from 2D grid-like domains is widely researched yet far from perfect. The standard convolution characterises feature correspondences indistinguishably among 3D points, presenting an intrinsic limitation of poor distinctive feature learning. In this paper, we propose Adaptive Graph Convolution (AdaptConv) which generates adaptive kernels for points according to their dynamically learned features. Compared with using a fixed/isotropic kernel, AdaptConv improves the flexibility of point cloud convolutions, effectively and precisely capturing the diverse relations between points from different semantic parts. Unlike popular attentional weight schemes, the proposed AdaptConv implements the adaptiveness inside the convolution operation instead of simply assigning different weights to the neighboring points. Extensive qualitative and quantitative evaluations show that our method outperforms state-of-the-art point cloud classification and segmentation approaches on several benchmark datasets. Our code is available at https://github.com/hrzhou2/AdaptConv-master.

Key facts. Cited by 60 papers in the map (data through 7 Oct 2026). Builds on 39 of its 52 references that are in the map. First posted on arXiv 18 Aug 2021.

Builds on

39 of 52 references.

References · 39

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27 more
  1. 2020PCT: Point cloud transformercited by 730
  2. 2018PointConv: Deep Convolutional Networks on 3D Point Cloudscited by 656
  3. 2015Multi-view Convolutional Neural Networks for 3D Shape Recognitioncited by 564
  4. 2016Geometric deep learning on graphs and manifolds using mixture model CNNscited by 468
  5. 2017Large-scale Point Cloud Semantic Segmentation with Superpoint Graphscited by 384
  6. 2019Relation-Shape Convolutional Neural Network for Point Cloud Analysiscited by 346
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  11. 2018SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filterscited by 261
  12. 2016Volumetric and Multi-View CNNs for Object Classification on 3D Datacited by 240
  13. 2020PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Samplingcited by 238
  14. 2019Pay Less Attention with Lightweight and Dynamic Convolutionscited by 211
  15. 2018SO-Net: Self-Organizing Network for Point Cloud Analysiscited by 210
  16. 2017SEGCloud: Semantic Segmentation of 3D Point Cloudscited by 178
  17. 2021Deep Parametric Continuous Convolutional Neural Networkscited by 161
  18. 2018Point Convolutional Neural Networks by Extension Operatorscited by 154
  19. 2017Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Modelscited by 142
  20. 2017Pointwise Convolutional Neural Networkscited by 126
  21. 2019ShellNet: Efficient Point Cloud Convolutional Neural Networks using Concentric Shells Statisticscited by 104
  22. 2018Local Spectral Graph Convolution for Point Set Feature Learningcited by 84
  23. 2018Recurrent Slice Networks for 3D Segmentation of Point Cloudscited by 82
  24. 2019Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentationcited by 68
  25. 20163D Shape Segmentation with Projective Convolutional Networkscited by 63
  26. 2019Spherical Kernel for Efficient Graph Convolution on 3D Point Cloudscited by 57
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 5; 2022: 18; 2023: 12; 2024: 14; 2025: 10; 2026: 1.

60 papers.

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  1. 2022Surface Representation for Point Cloudscited by 84
  2. 2022SeedFormer: Patch Seeds based Point Cloud Completion with Upsample Transformercited by 83
  3. 2022A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspectivecited by 56
  4. 2023Self-positioning Point-based Transformer for Point Cloud Understandingcited by 40
  5. 2022PointDistiller: Structured Knowledge Distillation Towards Efficient and Compact 3D Detectioncited by 33
  6. 20213D Medical Point Transformer: Introducing Convolution to Attention Networks for Medical Point Cloud Analysiscited by 23
  7. 2022CSDN: Cross-modal Shape-transfer Dual-refinement Network for Point Cloud Completioncited by 22
  8. 2022GPA-Net:No-Reference Point Cloud Quality Assessment with Multi-task Graph Convolutional Networkcited by 22
  9. 2022PointVector: A Vector Representation In Point Cloud Analysiscited by 21
  10. 2022FBNet: Feedback Network for Point Cloud Completioncited by 20
  11. 2024Multimodality Helps Few-shot 3D Point Cloud Semantic Segmentationcited by 20
  12. 2023SE-ORNet: Self-Ensembling Orientation-aware Network for Unsupervised Point Cloud Shape Correspondencecited by 19
38 more
  1. 2022The Devil is in the Pose: Ambiguity-free 3D Rotation-invariant Learning via Pose-aware Convolutioncited by 18
  2. 2024Rethinking Few-shot 3D Point Cloud Semantic Segmentationcited by 18
  3. 2025Edge Prompt Tuning for Graph Neural Networkscited by 18
  4. 2023ConDaFormer: Disassembled Transformer with Local Structure Enhancement for 3D Point Cloud Understandingcited by 16
  5. 2022Geometry-Complete Perceptron Networks for 3D Molecular Graphscited by 15
  6. 2024LSK3DNet: Towards Effective and Efficient 3D Perception with Large Sparse Kernelscited by 15
  7. 2022AGConv: Adaptive Graph Convolution on 3D Point Cloudscited by 13
  8. 2023Invariant Training 2D-3D Joint Hard Samples for Few-Shot Point Cloud Recognitioncited by 13
  9. 2024GPSFormer: A Global Perception and Local Structure Fitting-based Transformer for Point Cloud Understandingcited by 12
  10. 2022PointCaM: Cut-and-Mix for Open-Set Point Cloud Learningcited by 11
  11. 2021diffConv: Analyzing Irregular Point Clouds with an Irregular Viewcited by 6
  12. 2022UTOPIC: Uncertainty-aware Overlap Prediction Network for Partial Point Cloud Registrationcited by 6
  13. 2023Point Deformable Network with Enhanced Normal Embedding for Point Cloud Analysiscited by 6
  14. 2024DHGCN: Dynamic Hop Graph Convolution Network for Self-Supervised Point Cloud Learningcited by 6
  15. 2024Local-consistent Transformation Learning for Rotation-invariant Point Cloud Analysiscited by 6
  16. 2023Group Multi-View Transformer for 3D Shape Analysis with Spatial Encodingcited by 4
  17. 2025CORENet: Cross-Modal 4D Radar Denoising Network with LiDAR Supervision for Autonomous Drivingcited by 4
  18. 2022SelectionConv: Convolutional Neural Networks for Non-rectilinear Image Datacited by 3
  19. 2024PointMT: Efficient Point Cloud Analysis with Hybrid MLP-Transformer Architecturecited by 3
  20. 2024Virtual Nodes Can Help: Tackling Distribution Shifts in Federated Graph Learningcited by 3
  21. 2022HDNet: Hierarchical Dynamic Network for Gait Recognition using Millimeter-Wave Radarcited by 2
  22. 2023PointSmile: Point Self-supervised Learning via Curriculum Mutual Informationcited by 2
  23. 2024PointViG: A Lightweight GNN-based Model for Efficient Point Cloud Analysiscited by 2
  24. 2025HydraMamba: Multi-Head State Space Model for Global Point Cloud Learningcited by 2
  25. 2021Adaptive Channel Encoding Transformer for Point Cloud Analysiscited by 1
  26. 2022SPCNet: Stepwise Point Cloud Completion Networkcited by 1
  27. 2023Dynamic Local Feature Aggregation for Learning on Point Cloudscited by 1
  28. 20243D Learnable Supertoken Transformer for LiDAR Point Cloud Scene Segmentationcited by 1
  29. 2025Channel-adaptive Cross-modal Generative Semantic Communication for Point Cloud Transmissioncited by 1
  30. 2025Enhancing point cloud analysis via neighbor aggregation correction based on cross-stage structure correlationcited by 1
  31. 2026UnderOneFacade: Worldwide Facade Semantic Segmentation Benchmark Datasetcited by 1
  32. 2021Dual-Neighborhood Deep Fusion Network for Point Cloud Analysiscited by 0
  33. 2021Adaptive Channel Encoding for Point Cloud Analysiscited by 0
  34. 2022Shrinking unit: a Graph Convolution-Based Unit for CNN-like 3D Point Cloud Feature Extractorscited by 0
  35. 2022Adaptive Edge-to-Edge Interaction Learning for Point Cloud Analysiscited by 0
  36. 2023Hierarchical Point Attention for Indoor 3D Object Detectioncited by 0
  37. 2023A Task-driven Network for Mesh Classification and Semantic Part Segmentationcited by 0
  38. 2023Bidirectional Knowledge Reconfiguration for Lightweight Point Cloud Analysiscited by 0

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