mlsanityData to 7 Oct 2026

DensePoint: Learning Densely Contextual Representation for Efficient Point Cloud Processing

arXiv 1909.03669 ↗First posted 9 Sep 2019cs.CV cs.AI cs.GRcited by 104

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Abstract (arXiv)

Point cloud processing is very challenging, as the diverse shapes formed by irregular points are often indistinguishable. A thorough grasp of the elusive shape requires sufficiently contextual semantic information, yet few works devote to this. Here we propose DensePoint, a general architecture to learn densely contextual representation for point cloud processing. Technically, it extends regular grid CNN to irregular point configuration by generalizing a convolution operator, which holds the permutation invariance of points, and achieves efficient inductive learning of local patterns. Architecturally, it finds inspiration from dense connection mode, to repeatedly aggregate multi-level and multi-scale semantics in a deep hierarchy. As a result, densely contextual information along with rich semantics, can be acquired by DensePoint in an organic manner, making it highly effective. Extensive experiments on challenging benchmarks across four tasks, as well as thorough model analysis, verify DensePoint achieves the state of the arts.

Key facts. Cited by 104 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Sep 2019.

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Citing papers per year: 2021: 33; 2022: 34; 2023: 18; 2024: 10; 2025: 8; 2026: 1.

104 papers.

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  1. 2021Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modelingcited by 647
  2. 2022Point Transformer V2: Grouped Vector Attention and Partition-based Poolingcited by 384
  3. 2022Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Frameworkcited by 376
  4. 2022Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-trainingcited by 263
  5. 2022ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understandingcited by 262
  6. 2021PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Cloudscited by 211
  7. 2023ULIP-2: Towards Scalable Multimodal Pre-training for 3D Understandingcited by 199
  8. 2022Stratified Transformer for 3D Point Cloud Segmentationcited by 166
  9. 2022Masked Discrimination for Self-Supervised Learning on Point Cloudscited by 155
  10. 2021Revisiting Point Cloud Shape Classification with a Simple and Effective Baselinecited by 145
  11. 2022Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning?cited by 121
  12. 2022Surface Representation for Point Cloudscited by 84
38 more
  1. 2021Score-Based Point Cloud Denoisingcited by 83
  2. 2021PointMixer: MLP-Mixer for Point Cloud Understandingcited by 71
  3. 2021PointCutMix: Regularization Strategy for Point Cloud Classificationcited by 69
  4. 2021PRA-Net: Point Relation-Aware Network for 3D Point Cloud Analysiscited by 58
  5. 2024OA-CNNs: Omni-Adaptive Sparse CNNs for 3D Semantic Segmentationcited by 43
  6. 2021Image2Point: 3D Point-Cloud Understanding with 2D Image Pretrained Modelscited by 41
  7. 2021PointBA: Towards Backdoor Attacks in 3D Point Cloudcited by 34
  8. 2023Parameter is Not All You Need: Starting from Non-Parametric Networks for 3D Point Cloud Analysiscited by 34
  9. 2022A Unified Query-based Paradigm for Point Cloud Understandingcited by 32
  10. 2022CADOps-Net: Jointly Learning CAD Operation Types and Steps from Boundary-Representationscited by 30
  11. 2021Learning Inner-Group Relations on Point Cloudscited by 29
  12. 2022Isometric 3D Adversarial Examples in the Physical Worldcited by 25
  13. 2021Deep Point Set Resampling via Gradient Fieldscited by 24
  14. 2023Advancements in Point Cloud Data Augmentation for Deep Learning: A Surveycited by 24
  15. 20213D Medical Point Transformer: Introducing Convolution to Attention Networks for Medical Point Cloud Analysiscited by 23
  16. 2022Bidirectional Feature Globalization for Few-shot Semantic Segmentation of 3D Point Cloud Scenescited by 23
  17. 2022Exploring the Devil in Graph Spectral Domain for 3D Point Cloud Attackscited by 19
  18. 2024PartSTAD: 2D-to-3D Part Segmentation Task Adaptationcited by 18
  19. 2022POS-BERT: Point Cloud One-Stage BERT Pre-Trainingcited by 16
  20. 2022Beyond single receptive field: A receptive field fusion-and-stratification network for airborne laser scanning point cloud classificationcited by 16
  21. 2022Point Cloud Attacks in Graph Spectral Domain: When 3D Geometry Meets Graph Signal Processingcited by 16
  22. 2023Few-Shot 3D Point Cloud Semantic Segmentation via Stratified Class-Specific Attention Based Transformer Networkcited by 16
  23. 2023ConDaFormer: Disassembled Transformer with Local Structure Enhancement for 3D Point Cloud Understandingcited by 16
  24. 2024Bringing Masked Autoencoders Explicit Contrastive Properties for Point Cloud Self-Supervised Learningcited by 16
  25. 2023Invariant Training 2D-3D Joint Hard Samples for Few-Shot Point Cloud Recognitioncited by 13
  26. 2023CPCM: Contextual Point Cloud Modeling for Weakly-supervised Point Cloud Semantic Segmentationcited by 12
  27. 2023Beyond First Impressions: Integrating Joint Multi-modal Cues for Comprehensive 3D Representationcited by 12
  28. 2024LCM: Locally Constrained Compact Point Cloud Model for Masked Point Modelingcited by 12
  29. 2021DeltaConv: Anisotropic Operators for Geometric Deep Learning on Point Cloudscited by 10
  30. 2023A Closer Look at Few-Shot 3D Point Cloud Classificationcited by 10
  31. 2022What's the Situation with Intelligent Mesh Generation: A Survey and Perspectivescited by 9
  32. 2023General Point Model with Autoencoding and Autoregressivecited by 9
  33. 2021RPSRNet: End-to-End Trainable Rigid Point Set Registration Network using Barnes-Hut 2D-Tree Representationcited by 8
  34. 2022PSNet: Fast Data Structuring for Hierarchical Deep Learning on Point Cloudcited by 7
  35. 2023PointWavelet: Learning in Spectral Domain for 3D Point Cloud Analysiscited by 7
  36. 2023What Makes for Effective Few-shot Point Cloud Classification?cited by 7
  37. 2021Attention Models for Point Clouds in Deep Learning: A Surveycited by 6
  38. 2021MKConv: Multidimensional Feature Representation for Point Cloud Analysiscited by 6

50 most cited of 104 All 104 (JSON)