{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/point-gn-a-non-parametric-network-using","title":"Point-GN: A Non-Parametric Network Using Gaussian Positional Encoding for Point Cloud Classification","arxiv_id":"2412.03056","date":"2024-12-04","proceeding":null,"authors":["Marzieh Mohammadi","Amir Salarpour"],"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.","url_abs":"https://arxiv.org/abs/2412.03056v2","url_pdf":"https://arxiv.org/pdf/2412.03056v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"point-gn-a-non-parametric-network-using","repo_url":"https://github.com/asalarpour/Point_GN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"3d-point-cloud-classification","task_name":"3D Point Cloud Classification"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"point-cloud-classification","task_name":"Point Cloud Classification"},{"task_slug":"training-free-3d-point-cloud-classification","task_name":"Training-free 3D Point Cloud Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/training-free-3d-point-cloud-classification","task":"Training-free 3D Point Cloud Classification","dataset":"ModelNet40","model":"Point-GN","rank_in_archive_order":1,"of":7,"metrics":{"Accuracy (%)":"85.3","Need 3D Data?":"Yes","Parameters":"0M"},"uses_additional_data":false},{"leaderboard":"/sota/training-free-3d-point-cloud-classification-1","task":"Training-free 3D Point Cloud Classification","dataset":"ScanObjectNN","model":"Point-GN","rank_in_archive_order":1,"of":6,"metrics":{"Accuracy (%)":"86.4","Need 3D Data?":"Yes","Parameters":"0M"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}