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ConvPoint: Continuous Convolutions for Point Cloud Processing

arXiv 1904.02375 ↗First posted 4 Apr 2019cs.CVcited by 62

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Abstract (arXiv)

Point clouds are unstructured and unordered data, as opposed to images. Thus, most machine learning approach developed for image cannot be directly transferred to point clouds. In this paper, we propose a generalization of discrete convolutional neural networks (CNNs) in order to deal with point clouds by replacing discrete kernels by continuous ones. This formulation is simple, allows arbitrary point cloud sizes and can easily be used for designing neural networks similarly to 2D CNNs. We present experimental results with various architectures, highlighting the flexibility of the proposed approach. We obtain competitive results compared to the state-of-the-art on shape classification, part segmentation and semantic segmentation for large-scale point clouds.

Key facts. Cited by 62 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Apr 2019.

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Citing papers per year: 2021: 17; 2022: 16; 2023: 11; 2024: 11; 2025: 5; 2026: 2.

62 papers.

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  1. 2021PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Cloudscited by 211
  2. 2022POCO: Point Convolution for Surface Reconstructioncited by 91
  3. 2021One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentationcited by 89
  4. 2021EfficientLPS: Efficient LiDAR Panoptic Segmentationcited by 76
  5. 2024Point Cloud Mamba: Point Cloud Learning via State Space Modelcited by 66
  6. 2021Regularization Strategy for Point Cloud via Rigidly Mixed Samplecited by 59
  7. 2022Contrastive Boundary Learning for Point Cloud Segmentationcited by 56
  8. 2023Efficient 3D Semantic Segmentation with Superpoint Transformercited by 56
  9. 2021Generative Zero-Shot Learning for Semantic Segmentation of 3D Point Cloudscited by 55
  10. 20213D-to-2D Distillation for Indoor Scene Parsingcited by 47
  11. 2021PointBA: Towards Backdoor Attacks in 3D Point Cloudcited by 34
  12. 2023LinK: Linear Kernel for LiDAR-based 3D Perceptioncited by 26
38 more
  1. 2022Bidirectional Feature Globalization for Few-shot Semantic Segmentation of 3D Point Cloud Scenescited by 23
  2. 2022MetaSets: Meta-Learning on Point Sets for Generalizable Representationscited by 22
  3. 20213D Compositional Zero-shot Learning with DeCompositional Consensuscited by 16
  4. 2024Scalable 3D Panoptic Segmentation As Superpoint Graph Clusteringcited by 14
  5. 2024A comprehensive overview of deep learning techniques for 3D point cloud classification and semantic segmentationcited by 14
  6. 2022AGConv: Adaptive Graph Convolution on 3D Point Cloudscited by 13
  7. 2021DeltaConv: Anisotropic Operators for Geometric Deep Learning on Point Cloudscited by 10
  8. 2022Data Efficient 3D Learner via Knowledge Transferred from 2D Modelcited by 10
  9. 2023A Survey and Benchmark of Automatic Surface Reconstruction from Point Cloudscited by 10
  10. 2021Exploiting Local Geometry for Feature and Graph Construction for Better 3D Point Cloud Processing with Graph Neural Networkscited by 9
  11. 2024Advancing 3D Point Cloud Understanding through Deep Transfer Learning: A Comprehensive Surveycited by 9
  12. 2022Deep Surface Reconstruction from Point Clouds with Visibility Informationcited by 8
  13. 2022GeoSegNet: Point Cloud Semantic Segmentation via Geometric Encoder-Decoder Modelingcited by 8
  14. 2022CUF: Continuous Upsampling Filterscited by 7
  15. 2023Foveation in the Era of Deep Learningcited by 7
  16. 2021Investigate Indistinguishable Points in Semantic Segmentation of 3D Point Cloudcited by 6
  17. 2021MKConv: Multidimensional Feature Representation for Point Cloud Analysiscited by 6
  18. 2022QuadConv: Quadrature-Based Convolutions with Applications to Non-Uniform PDE Data Compressioncited by 6
  19. 2022Gaussian Mixture Convolution Networkscited by 4
  20. 2022Primitive3D: 3D Object Dataset Synthesis from Randomly Assembled Primitivescited by 4
  21. 2022Composite Convolution: a Flexible Operator for Deep Learning on 3D Point Cloudscited by 4
  22. 2023Human Vision Based 3D Point Cloud Semantic Segmentation of Large-Scale Outdoor Scenecited by 4
  23. 2023Tinto: Multisensor Benchmark for 3D Hyperspectral Point Cloud Segmentation in the Geosciencescited by 4
  24. 2023GAM : Gradient Attention Module of Optimization for Point Clouds Analysiscited by 3
  25. 2024Advancements in Point Cloud-Based 3D Defect Detection and Classification for Industrial Systems: A Comprehensive Surveycited by 3
  26. 2024GeoAuxNet: Towards Universal 3D Representation Learning for Multi-sensor Point Cloudscited by 3
  27. 2024Object Dynamics Modeling with Hierarchical Point Cloud-based Representationscited by 3
  28. 2022Predicting Vegetation Stratum Occupancy from Airborne LiDAR Data with Deep Learningcited by 2
  29. 2025Efficient Continuous Group Convolutions for Local SE(3) Equivariance in 3D Point Cloudscited by 2
  30. 2025Generalized Zero-Shot Learning for Point Cloud Segmentation with Evidence-Based Dynamic Calibrationcited by 2
  31. 2021Structure-Aware Multi-Hop Graph Convolution for Graph Neural Networkscited by 1
  32. 2022Hypergraph Convolutional Network based Weakly Supervised Point Cloud Semantic Segmentation with Scene-Level Annotationscited by 1
  33. 2023GeoSpark: Sparking up Point Cloud Segmentation with Geometry Cluecited by 1
  34. 2024Training point-based deep learning networks for forest segmentation with synthetic datacited by 1
  35. 20243D Learnable Supertoken Transformer for LiDAR Point Cloud Scene Segmentationcited by 1
  36. 2025HyperPointFormer: Multimodal Fusion in 3D Space with Dual-Branch Cross-Attention Transformerscited by 1
  37. 2025Enhancing point cloud analysis via neighbor aggregation correction based on cross-stage structure correlationcited by 1
  38. 2021Potential Convolution: Embedding Point Clouds into Potential Fieldscited by 0

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