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Pointwise Convolutional Neural Networks

arXiv 1712.05245 ↗First posted 14 Dec 2017cs.CV cs.LGcited by 127

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Abstract (arXiv)

Deep learning with 3D data such as reconstructed point clouds and CAD models has received great research interests recently. However, the capability of using point clouds with convolutional neural network has been so far not fully explored. In this paper, we present a convolutional neural network for semantic segmentation and object recognition with 3D point clouds. At the core of our network is pointwise convolution, a new convolution operator that can be applied at each point of a point cloud. Our fully convolutional network design, while being surprisingly simple to implement, can yield competitive accuracy in both semantic segmentation and object recognition task.

Key facts. Cited by 127 papers in the map (data through 14 Oct 2026). First posted on arXiv 14 Dec 2017.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 42; 2022: 29; 2023: 23; 2024: 16; 2025: 11; 2026: 6.

127 papers.

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  1. 2024VMamba: Visual State Space Modelcited by 969
  2. 2021Self-Supervised Pretraining of 3D Features on any Point-Cloudcited by 217
  3. 2021PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Cloudscited by 211
  4. 2022SoftGroup for 3D Instance Segmentation on Point Cloudscited by 200
  5. 2021Spatio-temporal Self-Supervised Representation Learning for 3D Point Cloudscited by 180
  6. 20222DPASS: 2D Priors Assisted Semantic Segmentation on LiDAR Point Cloudscited by 158
  7. 2021InstanceRefer: Cooperative Holistic Understanding for Visual Grounding on Point Clouds through Instance Multi-level Contextual Referringcited by 141
  8. 2022POCO: Point Convolution for Surface Reconstructioncited by 92
  9. 2021Equivariant Point Network for 3D Point Cloud Analysiscited by 81
  10. 20214D Panoptic LiDAR Segmentationcited by 65
  11. 2023Deep Learning on Implicit Neural Representations of Shapescited by 63
  12. 2021Adaptive Graph Convolution for Point Cloud Analysiscited by 60
38 more
  1. 2021Regularization Strategy for Point Cloud via Rigidly Mixed Samplecited by 59
  2. 2021Semantics for Robotic Mapping, Perception and Interaction: A Surveycited by 56
  3. 2021Generative Zero-Shot Learning for Semantic Segmentation of 3D Point Cloudscited by 55
  4. 2021SSPC-Net: Semi-supervised Semantic 3D Point Cloud Segmentation Networkcited by 54
  5. 2021Learning Semantic Segmentation of Large-Scale Point Clouds with Random Samplingcited by 51
  6. 2021Point-set Distances for Learning Representations of 3D Point Cloudscited by 48
  7. 2021Style-based Point Generator with Adversarial Rendering for Point Cloud Completioncited by 45
  8. 20213D Photo Stylization: Learning to Generate Stylized Novel Views from a Single Imagecited by 45
  9. 2022Comprehensive Review of Deep Learning-Based 3D Point Cloud Completion Processing and Analysiscited by 44
  10. 2021Image2Point: 3D Point-Cloud Understanding with 2D Image Pretrained Modelscited by 41
  11. 2021Point Cloud Augmentation with Weighted Local Transformationscited by 39
  12. 2021Learning from 2D: Contrastive Pixel-to-Point Knowledge Transfer for 3D Pretrainingcited by 33
  13. 2021Deep Learning Based 3D Segmentation: A Surveycited by 30
  14. 2021Towards Real-world X-ray Security Inspection: A High-Quality Benchmark and Lateral Inhibition Module for Prohibited Items Detectioncited by 30
  15. 2021ASSANet: An Anisotropic Separable Set Abstraction for Efficient Point Cloud Representation Learningcited by 29
  16. 2022RIConv++: Effective Rotation Invariant Convolutions for 3D Point Clouds Deep Learningcited by 29
  17. 2022Let Images Give You More:Point Cloud Cross-Modal Training for Shape Analysiscited by 24
  18. 2023Clustering based Point Cloud Representation Learning for 3D Analysiscited by 23
  19. 2021Not All Voxels Are Equal: Semantic Scene Completion from the Point-Voxel Perspectivecited by 22
  20. 2023Deep Learning in Deterministic Computational Mechanicscited by 21
  21. 2022GraphFit: Learning Multi-scale Graph-Convolutional Representation for Point Cloud Normal Estimationcited by 20
  22. 2022Flattening-Net: Deep Regular 2D Representation for 3D Point Cloud Analysiscited by 19
  23. 2023A Survey of Robustness and Safety of 2D and 3D Deep Learning Models Against Adversarial Attackscited by 19
  24. 20223D-OAE: Occlusion Auto-Encoders for Self-Supervised Learning on Point Cloudscited by 18
  25. 2023Benchmarking the Robustness of LiDAR Semantic Segmentation Modelscited by 18
  26. 2023Modelling Long Range Dependencies in ND: From Task-Specific to a General Purpose CNNcited by 18
  27. 2022RangeUDF: Semantic Surface Reconstruction from 3D Point Cloudscited by 16
  28. 2023Sim2real Transfer Learning for Point Cloud Segmentation: An Industrial Application Case on Autonomous Disassemblycited by 16
  29. 2023Neural Vector Fields: Implicit Representation by Explicit Learningcited by 16
  30. 2022Scalable SoftGroup for 3D Instance Segmentation on Point Cloudscited by 15
  31. 2021FPS-Net: A Convolutional Fusion Network for Large-Scale LiDAR Point Cloud Segmentationcited by 14
  32. 2022PCSCNet: Fast 3D Semantic Segmentation of LiDAR Point Cloud for Autonomous Car using Point Convolution and Sparse Convolution Networkcited by 14
  33. 2021SA-Det3D: Self-Attention Based Context-Aware 3D Object Detectioncited by 13
  34. 2022AGConv: Adaptive Graph Convolution on 3D Point Cloudscited by 13
  35. 2024DTG : Diffusion-based Trajectory Generation for Mapless Global Navigationcited by 13
  36. 2022SageMix: Saliency-Guided Mixup for Point Cloudscited by 12
  37. 2022Self-Distillation for Unsupervised 3D Domain Adaptationcited by 12
  38. 2024Open-Vocabulary 3D Semantic Segmentation with Text-to-Image Diffusion Modelscited by 12

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