mlsanityData to 7 Oct 2026

Attention-based Point Cloud Edge Sampling

arXiv 2302.14673 ↗First posted 28 Feb 2023cs.CVcited by 37

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Abstract (arXiv)

Point cloud sampling is a less explored research topic for this data representation. The most commonly used sampling methods are still classical random sampling and farthest point sampling. With the development of neural networks, various methods have been proposed to sample point clouds in a task-based learning manner. However, these methods are mostly generative-based, rather than selecting points directly using mathematical statistics. Inspired by the Canny edge detection algorithm for images and with the help of the attention mechanism, this paper proposes a non-generative Attention-based Point cloud Edge Sampling method (APES), which captures salient points in the point cloud outline. Both qualitative and quantitative experimental results show the superior performance of our sampling method on common benchmark tasks.

Key facts. Cited by 37 papers in the map (data through 7 Oct 2026). Builds on 40 of its 60 references that are in the map. First posted on arXiv 28 Feb 2023.

Builds on

40 of 60 references.

References · 40

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  4. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
  5. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,220
  6. 2020Point Transformercited by 1,462
  7. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
  8. 2019KPConv: Flexible and Deformable Convolution for Point Cloudscited by 1,202
  9. 2018PointCNN: Convolution On X-Transformed Pointscited by 764
  10. 2020PCT: Point cloud transformercited by 730
  11. 2018PointConv: Deep Convolutional Networks on 3D Point Cloudscited by 656
  12. 2019RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Cloudscited by 584
28 more
  1. 2019Relation-Shape Convolutional Neural Network for Point Cloud Analysiscited by 346
  2. 2019Point-Voxel CNN for Efficient 3D Deep Learningcited by 343
  3. 2015Understanding Neural Networks Through Deep Visualizationcited by 332
  4. 2017Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphscited by 307
  5. 2018SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filterscited by 261
  6. 2020PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Samplingcited by 238
  7. 2021PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Cloudscited by 211
  8. 2018PU-Net: Point Cloud Upsampling Networkcited by 208
  9. 20203D Object Detection with Pointformercited by 194
  10. 2018Tangent Convolutions for Dense Prediction in 3Dcited by 173
  11. 2022Stratified Transformer for 3D Point Cloud Segmentationcited by 166
  12. 2018SPLATNet: Sparse Lattice Networks for Point Cloud Processingcited by 158
  13. 2019Modeling Point Clouds with Self-Attention and Gumbel Subset Samplingcited by 151
  14. 2019Grid-GCN for Fast and Scalable Point Cloud Learningcited by 103
  15. 2020P2B: Point-to-Box Network for 3D Object Tracking in Point Cloudscited by 84
  16. 2017Deep Projective 3D Semantic Segmentationcited by 81
  17. 2019SampleNet: Differentiable Point Cloud Samplingcited by 74
  18. 2018PointSIFT: A SIFT-like Network Module for 3D Point Cloud Semantic Segmentationcited by 58
  19. 2021PRA-Net: Point Relation-Aware Network for 3D Point Cloud Analysiscited by 58
  20. 2020FPConv: Learning Local Flattening for Point Convolutioncited by 55
  21. 2019Adaptive Hierarchical Down-Sampling for Point Cloud Classificationcited by 40
  22. 2016Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-scale Deep Networkscited by 25
  23. 20223DCTN: 3D Convolution-Transformer Network for Point Cloud Classificationcited by 25
  24. 2018Flex-Convolution (Million-Scale Point-Cloud Learning Beyond Grid-Worlds)cited by 23
  25. 2019GAPNet: Graph Attention based Point Neural Network for Exploiting Local Feature of Point Cloudcited by 15
  26. 2021SA-Det3D: Self-Attention Based Context-Aware 3D Object Detectioncited by 13
  27. 2020MOPS-Net: A Matrix Optimization-driven Network forTask-Oriented 3D Point Cloud Downsamplingcited by 11
  28. 2022LighTN: Light-weight Transformer Network for Performance-overhead Tradeoff in Point Cloud Downsamplingcited by 8

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 14; 2025: 16; 2026: 4.

37 papers.

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  1. 2024PointMamba: A Simple State Space Model for Point Cloud Analysiscited by 166
  2. 2024Dora: Sampling and Benchmarking for 3D Shape Variational Auto-Encoderscited by 93
  3. 2024Dynamic Adapter Meets Prompt Tuning: Parameter-Efficient Transfer Learning for Point Cloud Analysiscited by 52
  4. 2024PoinTramba: A Hybrid Transformer-Mamba Framework for Point Cloud Analysiscited by 26
  5. 2024PointRWKV: Efficient RWKV-Like Model for Hierarchical Point Cloud Learningcited by 21
  6. 2024Open Panoramic Segmentationcited by 17
  7. 2025PointLoRA: Low-Rank Adaptation with Token Selection for Point Cloud Learningcited by 13
  8. 2024DV-Matcher: Deformation-based Non-Rigid Point Cloud Matching Guided by Pre-trained Visual Featurescited by 10
  9. 2025Spectral Informed Mamba for Robust Point Cloud Processingcited by 10
  10. 2024MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processingcited by 5
  11. 2023GTNet: Graph Transformer Network for 3D Point Cloud Classification and Semantic Segmentationcited by 3
  12. 2023Edge Aware Learning for 3D Point Cloudcited by 3
25 more
  1. 2025Large Language Models for Knowledge Graph Embedding: A Surveycited by 3
  2. 2025Efficient Spiking Point Mamba for Point Cloud Analysiscited by 3
  3. 2025Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNetcited by 3
  4. 2025CloudMamba: Grouped Selective State Spaces for Point Cloud Analysiscited by 3
  5. 2024Rethinking Attention Module Design for Point Cloud Analysiscited by 2
  6. 2025HydraMamba: Multi-Head State Space Model for Global Point Cloud Learningcited by 2
  7. 2024AVS-Net: Point Sampling with Adaptive Voxel Size for 3D Scene Understandingcited by 1
  8. 2024GSTran: Joint Geometric and Semantic Coherence for Point Cloud Segmentationcited by 1
  9. 2024Curvature Informed Furthest Point Samplingcited by 1
  10. 2025SAMBLE: Shape-Specific Point Cloud Sampling for an Optimal Trade-Off Between Local Detail and Global Uniformitycited by 1
  11. 2025A Cross Branch Fusion-Based Contrastive Learning Framework for Point Cloud Self-supervised Learningcited by 1
  12. 2025GFT: Graph Feature Tuning for Efficient Point Cloud Analysiscited by 1
  13. 2026NPNet: A Non-Parametric Network with Adaptive Gaussian-Fourier Positional Encoding for 3D Classification and Segmentationcited by 1
  14. 2026SLNet: A Super-Lightweight Geometry-Adaptive Network for 3D Point Cloud Recognitioncited by 1
  15. 2023GP-PCS: One-shot Feature-Preserving Point Cloud Simplification with Gaussian Processes on Riemannian Manifoldscited by 0
  16. 2024REPS: Reconstruction-based Point Cloud Samplingcited by 0
  17. 2024Efficient Point Transformer with Dynamic Token Aggregating for LiDAR Point Cloud Processingcited by 0
  18. 2025SM3D: Mitigating Spectral Bias and Semantic Dilution in Point Cloud State Space Modelscited by 0
  19. 2025Approximate Borderline Sampling using Granular-Ball for Classification Taskscited by 0
  20. 2025Preserving Topological and Geometric Embeddings for Point Cloud Recoverycited by 0
  21. 2025Deep Learning For Point Cloud Denoising: A Surveycited by 0
  22. 2025Unlocking Hidden Potential in Point Cloud Networks with Attention-Guided Grouping-Feature Coordinationcited by 0
  23. 2025Desensitizing for Improving Corruption Robustness in Point Cloud Classification through Adversarial Trainingcited by 0
  24. 2026CLLAP: Contrastive Learning-based LiDAR-Augmented Pretraining for Enhanced Radar-Camera Fusioncited by 0
  25. 2026Test-Time Instance Selection for Improved Whole Slide Image Analysiscited by 0