mlsanityData to 14 Oct 2026

PnP-3D: A Plug-and-Play for 3D Point Clouds

arXiv 2108.07378 ↗First posted 16 Aug 2021cs.CVcited by 10

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Abstract (arXiv)

With the help of the deep learning paradigm, many point cloud networks have been invented for visual analysis. However, there is great potential for development of these networks since the given information of point cloud data has not been fully exploited. To improve the effectiveness of existing networks in analyzing point cloud data, we propose a plug-and-play module, PnP-3D, aiming to refine the fundamental point cloud feature representations by involving more local context and global bilinear response from explicit 3D space and implicit feature space. To thoroughly evaluate our approach, we conduct experiments on three standard point cloud analysis tasks, including classification, semantic segmentation, and object detection, where we select three state-of-the-art networks from each task for evaluation. Serving as a plug-and-play module, PnP-3D can significantly boost the performances of established networks. In addition to achieving state-of-the-art results on four widely used point cloud benchmarks, we present comprehensive ablation studies and visualizations to demonstrate our approach's advantages. The code will be available at https://github.com/ShiQiu0419/pnp-3d.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 47 of its 67 references that are in the map. First posted on arXiv 16 Aug 2021.

Builds on

47 of 67 references.

References · 47

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35 more
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  13. 2017Large-scale Point Cloud Semantic Segmentation with Superpoint Graphscited by 385
  14. 2019Relation-Shape Convolutional Neural Network for Point Cloud Analysiscited by 346
  15. 2020PointGroup: Dual-Set Point Grouping for 3D Instance Segmentationcited by 321
  16. 2018SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filterscited by 261
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  18. 2021PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Cloudscited by 211
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 1; 2022: 2; 2023: 3; 2024: 1; 2025: 2; 2026: 1.

10 papers.

  1. 2023Parameter is Not All You Need: Starting from Non-Parametric Networks for 3D Point Cloud Analysiscited by 34
  2. 2021PU-Transformer: Point Cloud Upsampling Transformercited by 32
  3. 2023P2C: Self-Supervised Point Cloud Completion from Single Partial Cloudscited by 20
  4. 2022PointCaM: Cut-and-Mix for Open-Set Point Cloud Learningcited by 11
  5. 2023Retro-FPN: Retrospective Feature Pyramid Network for Point Cloud Semantic Segmentationcited by 6
  6. 2025CS-Net:Contribution-based Sampling Network for Point Cloud Simplificationcited by 3
  7. 2024NumGrad-Pull: Numerical Gradient Guided Tri-plane Representation for Surface Reconstruction from Point Cloudscited by 2
  8. 2022Adaptive Edge-to-Edge Interaction Learning for Point Cloud Analysiscited by 0
  9. 2025LDRFusion: A LiDAR-Dominant multimodal refinement framework for 3D object detectioncited by 0
  10. 2026PCT-Prompt: A Prompt-Guided Transformer Framework for Dense Prediction Tasks in Point Cloudscited by 0