Papers › Point-GN: A Non-Parametric Network Using Gaussian Positional Encoding for Point Cloud...
Point-GN: A Non-Parametric Network Using Gaussian Positional Encoding for Point Cloud Classification
Marzieh Mohammadi, Amir Salarpour
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.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Training-free 3D Point Cloud Classification | ModelNet40 | Point-GN | Accuracy (%) | 85.3 | #1 of 7 | Archive leaderboard | report |
| Training-free 3D Point Cloud Classification | ModelNet40 | Point-GN | Need 3D Data? | Yes | #1 of 7 | Archive leaderboard | report |
| Training-free 3D Point Cloud Classification | ModelNet40 | Point-GN | Parameters | 0M | #1 of 7 | Archive leaderboard | report |
| Training-free 3D Point Cloud Classification | ScanObjectNN | Point-GN | Accuracy (%) | 86.4 | #1 of 6 | Archive leaderboard | report |
| Training-free 3D Point Cloud Classification | ScanObjectNN | Point-GN | Need 3D Data? | Yes | #1 of 6 | Archive leaderboard | report |
| Training-free 3D Point Cloud Classification | ScanObjectNN | Point-GN | Parameters | 0M | #1 of 6 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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