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

Autor:	Mohammadi, Marzieh, Salarpour, Amir
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Artificial Intelligence Computer Science - Machine Learning Computer Science - Robotics
Druh dokumentu:	Working Paper
Popis:	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. Comment: This paper has been accepted for presentation at the IEEE Winter Conference on Applications of Computer Vision (WACV) 2025
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2412.03056 Zobrazit plný text záznamu View this record from Arxiv