mlsanityData to 14 Oct 2026

Dense-Resolution Network for Point Cloud Classification and Segmentation

arXiv 2005.06734 ↗First posted 14 May 2020cs.CVcited by 45

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Abstract (arXiv)

Point cloud analysis is attracting attention from Artificial Intelligence research since it can be widely used in applications such as robotics, Augmented Reality, self-driving. However, it is always challenging due to irregularities, unorderedness, and sparsity. In this article, we propose a novel network named Dense-Resolution Network (DRNet) for point cloud analysis. Our DRNet is designed to learn local point features from the point cloud in different resolutions. In order to learn local point groups more effectively, we present a novel grouping method for local neighborhood searching and an error-minimizing module for capturing local features. In addition to validating the network on widely used point cloud segmentation and classification benchmarks, we also test and visualize the performance of the components. Comparing with other state-of-the-art methods, our network shows superiority on ModelNet40, ShapeNet synthetic and ScanObjectNN real point cloud datasets.

Key facts. Cited by 45 papers in the map (data through 14 Oct 2026). First posted on arXiv 14 May 2020.

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Citing papers per year: 2021: 9; 2022: 12; 2023: 11; 2024: 6; 2025: 5; 2026: 2.

45 papers.

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  1. 2022PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategiescited by 436
  2. 2022Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Frameworkcited by 379
  3. 2023Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretrainingcited by 164
  4. 2022Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning?cited by 122
  5. 2021Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusioncited by 72
  6. 2023Instance-aware Dynamic Prompt Tuning for Pre-trained Point Cloud Modelscited by 68
  7. 2024Mamba3D: Enhancing Local Features for 3D Point Cloud Analysis via State Space Modelcited by 57
  8. 2021Image2Point: 3D Point-Cloud Understanding with 2D Image Pretrained Modelscited by 41
  9. 2023Self-positioning Point-based Transformer for Point Cloud Understandingcited by 40
  10. 2023Parameter is Not All You Need: Starting from Non-Parametric Networks for 3D Point Cloud Analysiscited by 34
  11. 2021PU-Transformer: Point Cloud Upsampling Transformercited by 32
  12. 2021Dual Transformer for Point Cloud Analysiscited by 26
33 more
  1. 20223DCTN: 3D Convolution-Transformer Network for Point Cloud Classificationcited by 25
  2. 2023Take-A-Photo: 3D-to-2D Generative Pre-training of Point Cloud Modelscited by 25
  3. 2022Let Images Give You More:Point Cloud Cross-Modal Training for Shape Analysiscited by 24
  4. 2022LCPFormer: Towards Effective 3D Point Cloud Analysis via Local Context Propagation in Transformerscited by 21
  5. 2023P2C: Self-Supervised Point Cloud Completion from Single Partial Cloudscited by 20
  6. 2024Bringing Masked Autoencoders Explicit Contrastive Properties for Point Cloud Self-Supervised Learningcited by 16
  7. 2021Point Cloud Learning with Transformercited by 15
  8. 2024GPSFormer: A Global Perception and Local Structure Fitting-based Transformer for Point Cloud Understandingcited by 12
  9. 2024MAP: Unleashing Hybrid Mamba-Transformer Vision Backbone's Potential with Masked Autoregressive Pretrainingcited by 12
  10. 2021Investigating Attention Mechanism in 3D Point Cloud Object Detectioncited by 10
  11. 2021PnP-3D: A Plug-and-Play for 3D Point Cloudscited by 10
  12. 2021DeltaConv: Anisotropic Operators for Geometric Deep Learning on Point Cloudscited by 10
  13. 2022Advanced Feature Learning on Point Clouds using Multi-resolution Features and Learnable Poolingcited by 9
  14. 2022Point-DAE: Denoising Autoencoders for Self-supervised Point Cloud Learningcited by 9
  15. 2023Full Point Encoding for Local Feature Aggregation in 3D Point Cloudscited by 5
  16. 2025DiffPCN: Latent Diffusion Model Based on Multi-view Depth Images for Point Cloud Completioncited by 5
  17. 2021CloudWalker: Random walks for 3D point cloud shape analysiscited by 4
  18. 2022APP-Net: Auxiliary-point-based Push and Pull Operations for Efficient Point Cloud Classificationcited by 4
  19. 2023Collect-and-Distribute Transformer for 3D Point Cloud Analysiscited by 4
  20. 2025SignX: Continuous Sign Recognition in Compact Pose-Rich Latent Spacecited by 4
  21. 2022PointNorm: Dual Normalization is All You Need for Point Cloud Analysiscited by 3
  22. 2025KAN or MLP? Point Cloud Shows the Way Forwardcited by 3
  23. 2023Exploiting Inductive Bias in Transformer for Point Cloud Classification and Segmentationcited by 2
  24. 2024SignLLM: Sign Language Production Large Language Modelscited by 2
  25. 2025Masked Clustering Prediction for Unsupervised Point Cloud Pre-trainingcited by 2
  26. 2022Isomorphic mesh generation from point clouds with multilayer perceptronscited by 1
  27. 2023Local region-learning modules for point cloud classificationcited by 1
  28. 20223DGTN: 3D Dual-Attention GLocal Transformer Network for Point Cloud Classification and Segmentationcited by 0
  29. 2023VTPNet for 3D deep learning on point cloudcited by 0
  30. 2024Global Attention-Guided Dual-Domain Point Cloud Feature Learning for Classification and Segmentationcited by 0
  31. 2025Point upsampling networks for single-photon sensingcited by 0
  32. 2026Mamba Learns in Context: Structure-Aware Domain Generalization for Multi-Task Point Cloud Understandingcited by 0
  33. 2026A Robust Point Cloud Analysis Framework Inspired By Primary Visual Cortexcited by 0