mlsanityData to 14 Oct 2026

PRA-Net: Point Relation-Aware Network for 3D Point Cloud Analysis

arXiv 2112.04903 ↗First posted 9 Dec 2021cs.CVcited by 58

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Abstract (arXiv)

Learning intra-region contexts and inter-region relations are two effective strategies to strengthen feature representations for point cloud analysis. However, unifying the two strategies for point cloud representation is not fully emphasized in existing methods. To this end, we propose a novel framework named Point Relation-Aware Network (PRA-Net), which is composed of an Intra-region Structure Learning (ISL) module and an Inter-region Relation Learning (IRL) module. The ISL module can dynamically integrate the local structural information into the point features, while the IRL module captures inter-region relations adaptively and efficiently via a differentiable region partition scheme and a representative point-based strategy. Extensive experiments on several 3D benchmarks covering shape classification, keypoint estimation, and part segmentation have verified the effectiveness and the generalization ability of PRA-Net. Code will be available at https://github.com/XiwuChen/PRA-Net .

Key facts. Cited by 58 papers in the map (data through 14 Oct 2026). Builds on 52 of its 69 references that are in the map. First posted on arXiv 9 Dec 2021.

Builds on

52 of 69 references.

References · 52

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  3. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,475
  4. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  5. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,401
  6. 2017ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenescited by 3,117
  7. 2018CBAM: Convolutional Block Attention Modulecited by 2,477
  8. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,223
  9. 2020Point Transformercited by 1,470
  10. 2019KPConv: Flexible and Deformable Convolution for Point Cloudscited by 1,205
  11. 2018PointCNN: Convolution On X-Transformed Pointscited by 764
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38 more
  1. 2020PCT: Point cloud transformercited by 732
  2. 2018PointConv: Deep Convolutional Networks on 3D Point Cloudscited by 657
  3. 2015Multi-view Convolutional Neural Networks for 3D Shape Recognitioncited by 565
  4. 2019Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Datacited by 532
  5. 2018Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Drivingcited by 457
  6. 2017Large-scale Point Cloud Semantic Segmentation with Superpoint Graphscited by 385
  7. 2019Relation-Shape Convolutional Neural Network for Point Cloud Analysiscited by 346
  8. 2016OctNet: Learning Deep 3D Representations at High Resolutionscited by 342
  9. 2017Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphscited by 310
  10. 2018SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filterscited by 261
  11. 2016Volumetric and Multi-View CNNs for Object Classification on 3D Datacited by 240
  12. 2020PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Samplingcited by 239
  13. 2018SO-Net: Self-Organizing Network for Point Cloud Analysiscited by 210
  14. 2018Tangent Convolutions for Dense Prediction in 3Dcited by 173
  15. 2017SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentationcited by 169
  16. 2021Deep Parametric Continuous Convolutional Neural Networkscited by 161
  17. 2019Modeling Point Clouds with Self-Attention and Gumbel Subset Samplingcited by 151
  18. 2019ShellNet: Efficient Point Cloud Convolutional Neural Networks using Concentric Shells Statisticscited by 104
  19. 2019DensePoint: Learning Densely Contextual Representation for Efficient Point Cloud Processingcited by 104
  20. 2019Grid-GCN for Fast and Scalable Point Cloud Learningcited by 103
  21. 2018Point2Sequence: Learning the Shape Representation of 3D Point Clouds with an Attention-based Sequence to Sequence Networkcited by 100
  22. 2018Multiresolution Tree Networks for 3D Point Cloud Processingcited by 91
  23. 2020A Closer Look at Local Aggregation Operators in Point Cloud Analysiscited by 87
  24. 2019Interpolated Convolutional Networks for 3D Point Cloud Understandingcited by 86
  25. 2018Recurrent Slice Networks for 3D Segmentation of Point Cloudscited by 82
  26. 2016SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentationcited by 70
  27. 2019A-CNN: Annularly Convolutional Neural Networks on Point Cloudscited by 66
  28. 2019Multi-Angle Point Cloud-VAE: Unsupervised Feature Learning for 3D Point Clouds from Multiple Angles by Joint Self-Reconstruction and Half-to-Half Predictioncited by 66
  29. 2018VV-Net: Voxel VAE Net with Group Convolutions for Point Cloud Segmentationcited by 64
  30. 2020FPConv: Learning Local Flattening for Point Convolutioncited by 55
  31. 2020KeypointNet: A Large-scale 3D Keypoint Dataset Aggregated from Numerous Human Annotationscited by 51
  32. 2019Octree guided CNN with Spherical Kernels for 3D Point Cloudscited by 50
  33. 2019Geometry Sharing Network for 3D Point Cloud Classification and Segmentationcited by 33
  34. 2016GIFT: A Real-time and Scalable 3D Shape Search Enginecited by 31
  35. 2019Point2Node: Correlation Learning of Dynamic-Node for Point Cloud Feature Modelingcited by 25
  36. 2019View N-gram Network for 3D Object Retrievalcited by 18
  37. 2018Edge and Corner Detection for Unorganized 3D Point Clouds with Application to Robotic Weldingcited by 14
  38. 2019Point2SpatialCapsule: Aggregating Features and Spatial Relationships of Local Regions on Point Clouds using Spatial-aware Capsulescited by 14

50 most cited of 52 All 52 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 16; 2023: 19; 2024: 13; 2025: 6; 2026: 4.

58 papers.

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  1. 2022Masked Autoencoders for Point Cloud Self-supervised Learningcited by 459
  2. 2022PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategiescited by 436
  3. 2022Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Frameworkcited by 379
  4. 2024PointMamba: A Simple State Space Model for Point Cloud Analysiscited by 167
  5. 2023Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretrainingcited by 164
  6. 2022Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning?cited by 122
  7. 2022P2P: Tuning Pre-trained Image Models for Point Cloud Analysis with Point-to-Pixel Promptingcited by 74
  8. 2023Instance-aware Dynamic Prompt Tuning for Pre-trained Point Cloud Modelscited by 68
  9. 2024Mamba3D: Enhancing Local Features for 3D Point Cloud Analysis via State Space Modelcited by 57
  10. 2024Dynamic Adapter Meets Prompt Tuning: Parameter-Efficient Transfer Learning for Point Cloud Analysiscited by 53
  11. 2022Implicit Autoencoder for Point-Cloud Self-Supervised Representation Learningcited by 46
  12. 2023Self-positioning Point-based Transformer for Point Cloud Understandingcited by 40
38 more
  1. 2023Attention-based Point Cloud Edge Samplingcited by 37
  2. 2023Take-A-Photo: 3D-to-2D Generative Pre-training of Point Cloud Modelscited by 25
  3. 2022Let Images Give You More:Point Cloud Cross-Modal Training for Shape Analysiscited by 24
  4. 2022PointVector: A Vector Representation In Point Cloud Analysiscited by 21
  5. 2023Point Cloud Classification Using Content-based Transformer via Clustering in Feature Spacecited by 19
  6. 2022Rethinking the compositionality of point clouds through regularization in the hyperbolic spacecited by 18
  7. 2024Bringing Masked Autoencoders Explicit Contrastive Properties for Point Cloud Self-Supervised Learningcited by 16
  8. 2022Masked Surfel Prediction for Self-Supervised Point Cloud Learningcited by 15
  9. 2024A Unified Framework for 3D Scene Understandingcited by 15
  10. 2024MAP: Unleashing Hybrid Mamba-Transformer Vision Backbone's Potential with Masked Autoregressive Pretrainingcited by 12
  11. 2022PointCAT: Contrastive Adversarial Training for Robust Point Cloud Recognitioncited by 10
  12. 2022Advanced Feature Learning on Point Clouds using Multi-resolution Features and Learnable Poolingcited by 9
  13. 2023PointWavelet: Learning in Spectral Domain for 3D Point Cloud Analysiscited by 7
  14. 2023Regress Before Construct: Regress Autoencoder for Point Cloud Self-supervised Learningcited by 6
  15. 2023Point Deformable Network with Enhanced Normal Embedding for Point Cloud Analysiscited by 6
  16. 2023Full Point Encoding for Local Feature Aggregation in 3D Point Cloudscited by 5
  17. 2024MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processingcited by 5
  18. 2025Describe, Adapt and Combine: Empowering CLIP Encoders for Open-set 3D Object Retrievalcited by 5
  19. 2022APP-Net: Auxiliary-point-based Push and Pull Operations for Efficient Point Cloud Classificationcited by 4
  20. 2023PointPatchMix: Point Cloud Mixing with Patch Scoringcited by 4
  21. 2023PointGame: Geometrically and Adaptively Masked Auto-Encoder on Point Cloudscited by 4
  22. 2023Collect-and-Distribute Transformer for 3D Point Cloud Analysiscited by 4
  23. 2024Adapt PointFormer: 3D Point Cloud Analysis via Adapting 2D Visual Transformerscited by 4
  24. 2022PointNorm: Dual Normalization is All You Need for Point Cloud Analysiscited by 3
  25. 2023PointCLIMB: An Exemplar-Free Point Cloud Class Incremental Benchmarkcited by 3
  26. 2023Edge Aware Learning for 3D Point Cloudcited by 3
  27. 2024GeoMask3D: Geometrically Informed Mask Selection for Self-Supervised Point Cloud Learning in 3Dcited by 3
  28. 2025KAN or MLP? Point Cloud Shows the Way Forwardcited by 3
  29. 2023Exploiting Inductive Bias in Transformer for Point Cloud Classification and Segmentationcited by 2
  30. 2025TeDA: Boosting Vision-Lanuage Models for Zero-Shot 3D Object Retrieval via Testing-time Distribution Alignmentcited by 2
  31. 2022Hypergraph Convolutional Network based Weakly Supervised Point Cloud Semantic Segmentation with Scene-Level Annotationscited by 1
  32. 2023Dynamic Local Feature Aggregation for Learning on Point Cloudscited by 1
  33. 2023EipFormer: Emphasizing Instance Positions in 3D Instance Segmentationcited by 1
  34. 2024Curvature Informed Furthest Point Samplingcited by 1
  35. 2025InfoGNN: End-to-end deep learning on mesh via graph neural networkscited by 1
  36. 2025SAMBLE: Shape-Specific Point Cloud Sampling for an Optimal Trade-Off Between Local Detail and Global Uniformitycited by 1
  37. 20223DGTN: 3D Dual-Attention GLocal Transformer Network for Point Cloud Classification and Segmentationcited by 0
  38. 2023Joint Learning for Scattered Point Cloud Understanding with Hierarchical Self-Distillationcited by 0

50 most cited of 58 All 58 (JSON)