mlsanityData to 7 Oct 2026

A-CNN: Annularly Convolutional Neural Networks on Point Clouds

arXiv 1904.08017 ↗First posted 16 Apr 2019cs.CVcited by 66

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Abstract (arXiv)

Analyzing the geometric and semantic properties of 3D point clouds through the deep networks is still challenging due to the irregularity and sparsity of samplings of their geometric structures. This paper presents a new method to define and compute convolution directly on 3D point clouds by the proposed annular convolution. This new convolution operator can better capture the local neighborhood geometry of each point by specifying the (regular and dilated) ring-shaped structures and directions in the computation. It can adapt to the geometric variability and scalability at the signal processing level. We apply it to the developed hierarchical neural networks for object classification, part segmentation, and semantic segmentation in large-scale scenes. The extensive experiments and comparisons demonstrate that our approach outperforms the state-of-the-art methods on a variety of standard benchmark datasets (e.g., ModelNet10, ModelNet40, ShapeNet-part, S3DIS, and ScanNet).

Key facts. Cited by 66 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Apr 2019.

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Citing papers per year: 2021: 29; 2022: 9; 2023: 14; 2024: 9; 2025: 2; 2026: 3.

66 papers.

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  1. 2021PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Cloudscited by 211
  2. 2021Walk in the Cloud: Learning Curves for Point Clouds Shape Analysiscited by 172
  3. 2021Revisiting Point Cloud Shape Classification with a Simple and Effective Baselinecited by 145
  4. 2021Beyond Self-attention: External Attention using Two Linear Layers for Visual Taskscited by 109
  5. 2021PointMixer: MLP-Mixer for Point Cloud Understandingcited by 71
  6. 2024Point Cloud Mamba: Point Cloud Learning via State Space Modelcited by 66
  7. 20214D Panoptic LiDAR Segmentationcited by 64
  8. 2021PRA-Net: Point Relation-Aware Network for 3D Point Cloud Analysiscited by 58
  9. 2023Diffusion 3D Features (Diff3F): Decorating Untextured Shapes with Distilled Semantic Featurescited by 55
  10. 2021Learning Semantic Segmentation of Large-Scale Point Clouds with Random Samplingcited by 51
  11. 2021Neural Points: Point Cloud Representation with Neural Fields for Arbitrary Upsamplingcited by 48
  12. 2021Image2Point: 3D Point-Cloud Understanding with 2D Image Pretrained Modelscited by 41
38 more
  1. 2021Deep Learning Based 3D Segmentation: A Surveycited by 30
  2. 2023Deep Learning-based 3D Point Cloud Classification: A Systematic Survey and Outlookcited by 24
  3. 2023Clustering based Point Cloud Representation Learning for 3D Analysiscited by 23
  4. 2021Not All Voxels Are Equal: Semantic Scene Completion from the Point-Voxel Perspectivecited by 22
  5. 2022LCPFormer: Towards Effective 3D Point Cloud Analysis via Local Context Propagation in Transformerscited by 21
  6. 2024PointRWKV: Efficient RWKV-Like Model for Hierarchical Point Cloud Learningcited by 21
  7. 2024Multimodality Helps Few-shot 3D Point Cloud Semantic Segmentationcited by 20
  8. 2022Flattening-Net: Deep Regular 2D Representation for 3D Point Cloud Analysiscited by 19
  9. 2024Rethinking Few-shot 3D Point Cloud Semantic Segmentationcited by 18
  10. 2023Invariant Training 2D-3D Joint Hard Samples for Few-Shot Point Cloud Recognitioncited by 13
  11. 2024GPSFormer: A Global Perception and Local Structure Fitting-based Transformer for Point Cloud Understandingcited by 12
  12. 2021Learning without Forgetting for 3D Point Cloud Objectscited by 7
  13. 2021Language-Level Semantics Conditioned 3D Point Cloud Segmentationcited by 7
  14. 2022PSNet: Fast Data Structuring for Hierarchical Deep Learning on Point Cloudcited by 7
  15. 2021Investigate Indistinguishable Points in Semantic Segmentation of 3D Point Cloudcited by 6
  16. 2021diffConv: Analyzing Irregular Point Clouds with an Irregular Viewcited by 6
  17. 2022QuadConv: Quadrature-Based Convolutions with Applications to Non-Uniform PDE Data Compressioncited by 6
  18. 2022Active Self-Training for Weakly Supervised 3D Scene Semantic Segmentationcited by 5
  19. 2023CurveCloudNet: Processing Point Clouds with 1D Structurecited by 5
  20. 2023D-Net: Learning for Distinctive Point Clouds by Self-Attentive Point Searching and Learnable Feature Fusioncited by 5
  21. 2021Anomaly Detection in Radar Data Using PointNetscited by 4
  22. 2021Background-Aware 3D Point Cloud Segmentationwith Dynamic Point Feature Aggregationcited by 4
  23. 2021PAPooling: Graph-based Position Adaptive Aggregation of Local Geometry in Point Cloudscited by 4
  24. 2022PointSCNet: Point Cloud Structure and Correlation Learning Based on Space Filling Curve-Guided Samplingcited by 4
  25. 2023Collect-and-Distribute Transformer for 3D Point Cloud Analysiscited by 4
  26. 2021Differentiable Convolution Search for Point Cloud Processingcited by 3
  27. 2023GAM : Gradient Attention Module of Optimization for Point Clouds Analysiscited by 3
  28. 2024T3DNet: Compressing Point Cloud Models for Lightweight 3D Recognitioncited by 3
  29. 2024On-the-fly Point Feature Representation for Point Clouds Analysiscited by 3
  30. 2024PointMT: Efficient Point Cloud Analysis with Hybrid MLP-Transformer Architecturecited by 3
  31. 2021The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutionscited by 2
  32. 2021VMNet: Voxel-Mesh Network for Geodesic-Aware 3D Semantic Segmentationcited by 2
  33. 2022Completing Partial Point Clouds with Outliers by Collaborative Completion and Segmentationcited by 2
  34. 2023Slice Transformer and Self-supervised Learning for 6DoF Localization in 3D Point Cloud Mapscited by 2
  35. 2023DANet: Density Adaptive Convolutional Network with Interactive Attention for 3D Point Cloudscited by 2
  36. 2026GaussFusion: Towards Multimodal 3D Gaussian Pretrainingcited by 2
  37. 2021EllipsoidNet: Ellipsoid Representation for Point Cloud Classification and Segmentationcited by 1
  38. 2021Revisiting 2D Convolutional Neural Networks for Graph-based Applicationscited by 1

50 most cited of 66 All 66 (JSON)