mlsanityData to 7 Oct 2026

Point-GN: A Non-Parametric Network Using Gaussian Positional Encoding for Point Cloud Classification

arXiv 2412.03056 ↗First posted 4 Dec 2024cs.CV cs.AI cs.LGcited by 8

Not on the map. Why?

Abstract (arXiv)

This paper introduces Point-GN, a novel non-parametric network for efficient and accurate 3D point cloud classification. Unlike conventional deep learning models that rely on a large number of trainable parameters, Point-GN leverages non-learnable components-specifically, Farthest Point Sampling (FPS), k-Nearest Neighbors (k-NN), and Gaussian Positional Encoding (GPE)-to extract both local and global geometric features. This design eliminates the need for additional training while maintaining high performance, making Point-GN particularly suited for real-time, resource-constrained applications. We evaluate Point-GN on two benchmark datasets, ModelNet40 and ScanObjectNN, achieving classification accuracies of 85.29% and 85.89%, respectively, while significantly reducing computational complexity. Point-GN outperforms existing non-parametric methods and matches the performance of fully trained models, all with zero learnable parameters. Our results demonstrate that Point-GN is a promising solution for 3D point cloud classification in practical, real-time environments.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 30 of its 50 references that are in the map. First posted on arXiv 4 Dec 2024.

Builds on

30 of 50 references.

References · 30

  1. 2017Attention Is All You Needcited by 59,932
  2. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  3. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
  4. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,220
  5. 2020Point Transformercited by 1,462
  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 1,202
  8. 2014Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Imagescited by 774
  9. 2018PointCNN: Convolution On X-Transformed Pointscited by 764
  10. 2019Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervisioncited by 699
  11. 2021Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modelingcited by 647
  12. 2017Frustum PointNets for 3D Object Detection from RGB-D Datacited by 640
18 more
  1. 2019RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Cloudscited by 584
  2. 2016Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modelingcited by 558
  3. 2019Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Datacited by 532
  4. 2021PointCLIP: Point Cloud Understanding by CLIPcited by 461
  5. 2022PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategiescited by 432
  6. 2021Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modelingcited by 429
  7. 2022Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Frameworkcited by 376
  8. 2019Point-Voxel CNN for Efficient 3D Deep Learningcited by 343
  9. 2021Walk in the Cloud: Learning Curves for Point Clouds Shape Analysiscited by 172
  10. 2019Modeling Point Clouds with Self-Attention and Gumbel Subset Samplingcited by 151
  11. 2021Revisiting Point Cloud Shape Classification with a Simple and Effective Baselinecited by 145
  12. 2019ShellNet: Efficient Point Cloud Convolutional Neural Networks using Concentric Shells Statisticscited by 104
  13. 2016Shape Completion Enabled Robotic Graspingcited by 79
  14. 2020Self-Supervised Few-Shot Learning on Point Cloudscited by 66
  15. 2022Meta Architecture for Point Cloud Analysiscited by 39
  16. 2023Parameter is Not All You Need: Starting from Non-Parametric Networks for 3D Point Cloud Analysiscited by 34
  17. 20213D Medical Point Transformer: Introducing Convolution to Attention Networks for Medical Point Cloud Analysiscited by 23
  18. 2024Discovering New Shadow Patterns for Black-Box Attacks on Lane Detection of Autonomous Vehiclescited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 6.

8 papers.

  1. 2025Point-LN: A Lightweight Framework for Efficient Point Cloud Classification Using Non-Parametric Positional Encodingcited by 3
  2. 2025Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNetcited by 3
  3. 2026NPNet: A Non-Parametric Network with Adaptive Gaussian-Fourier Positional Encoding for 3D Classification and Segmentationcited by 1
  4. 2026SLNet: A Super-Lightweight Geometry-Adaptive Network for 3D Point Cloud Recognitioncited by 1
  5. 2026A Unified Non-Parametric and Interpretable Point Cloud Analysis via t-FCW Graph Representationcited by 0
  6. 2026RadiusFPS: Efficient Farthest Point Sampling on CPUs and GPUs via Spherical Voxel Pruningcited by 0
  7. 2026A Robust Point Cloud Analysis Framework Inspired By Primary Visual Cortexcited by 0
  8. 2026Synthetic LiDAR Data Generation and Deterministic Downsampling for Point Cloud Classification on the Edgecited by 0