mlsanityData to 13 Oct 2026

Point-LN: A Lightweight Framework for Efficient Point Cloud Classification Using Non-Parametric Positional Encoding

arXiv 2501.14238 ↗First posted 24 Jan 2025cs.CV cs.AI cs.LGcited by 3

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Abstract (arXiv)

We introduce Point-LN, a novel lightweight framework engineered for efficient 3D point cloud classification. Point-LN integrates essential non-parametric components-such as Farthest Point Sampling (FPS), k-Nearest Neighbors (k-NN), and non-learnable positional encoding-with a streamlined learnable classifier that significantly enhances classification accuracy while maintaining a minimal parameter footprint. This hybrid architecture ensures low computational costs and rapid inference speeds, making Point-LN ideal for real-time and resource-constrained applications. Comprehensive evaluations on benchmark datasets, including ModelNet40 and ScanObjectNN, demonstrate that Point-LN achieves competitive performance compared to state-of-the-art methods, all while offering exceptional efficiency. These results establish Point-LN as a robust and scalable solution for diverse point cloud classification tasks, highlighting its potential for widespread adoption in various computer vision applications.

Key facts. Cited by 3 papers in the map (data through 13 Oct 2026). Builds on 20 of its 35 references that are in the map. First posted on arXiv 24 Jan 2025.

Builds on

20 of 35 references.

References · 20

  1. 2017Attention Is All You Needcited by 60,106
  2. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,231
  3. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,389
  4. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,222
  5. 2020Point Transformercited by 1,465
  6. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
  7. 2019KPConv: Flexible and Deformable Convolution for Point Cloudscited by 1,203
  8. 2018PointCNN: Convolution On X-Transformed Pointscited by 764
  9. 2018PointConv: Deep Convolutional Networks on 3D Point Cloudscited by 656
  10. 2015Multi-view Convolutional Neural Networks for 3D Shape Recognitioncited by 565
  11. 2019Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Datacited by 532
  12. 2022Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Frameworkcited by 376
8 more
  1. 2021Walk in the Cloud: Learning Curves for Point Clouds Shape Analysiscited by 172
  2. 2019Modeling Point Clouds with Self-Attention and Gumbel Subset Samplingcited by 151
  3. 2020MVTN: Multi-View Transformation Network for 3D Shape Recognitioncited by 140
  4. 2023Parameter is Not All You Need: Starting from Non-Parametric Networks for 3D Point Cloud Analysiscited by 34
  5. 2020Beyond Triplet Loss: Person Re-identification with Fine-grained Difference-aware Pairwise Losscited by 32
  6. 2016GIFT: A Real-time and Scalable 3D Shape Search Enginecited by 31
  7. 2016FPNN: Field Probing Neural Networks for 3D Datacited by 25
  8. 2024Point-GN: A Non-Parametric Network Using Gaussian Positional Encoding for Point Cloud Classificationcited by 8

Built on by

Cited by year (2026 to 13 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNetcited by 3
  2. 2026NPNet: A Non-Parametric Network with Adaptive Gaussian-Fourier Positional Encoding for 3D Classification and Segmentationcited by 1
  3. 2026SLNet: A Super-Lightweight Geometry-Adaptive Network for 3D Point Cloud Recognitioncited by 1