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[bibtex]@InProceedings{Mohammadi_2025_WACV, author = {Mohammadi, Marzieh and Salarpour, Amir}, title = {Point-GN: A Non-Parametric Network using Gaussian Positional Encoding for Point Cloud Classification}, booktitle = {Proceedings of the Winter Conference on Applications of Computer Vision (WACV)}, month = {February}, year = {2025}, pages = {3487-3496} }
Point-GN: A Non-Parametric Network using Gaussian Positional Encoding for Point Cloud Classification
Abstract
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.
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