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.