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Although significant progress has been made in point cloud processing in recent years, most of it has been achieved by designing more complex networks to attain better performance. This paper proposes a novel lightweight point cloud recognition network by introducing a new local neighborhood optimization layer (LNOL), which improves traditional sampling methods by correlation learning in local area. The LNOL is embedded within a single\u2010layer local transformer architecture, significantly reducing computational complexity and parameters while maintaining the model's expressive power. Experimental results on the ModelNet40 benchmark dataset demonstrate that our method achieves a classification accuracy of 93.3% and an average precision of 92.0% without using a voting strategy. Compared to the mainstream local transformer model point transformer, our network requires only 9.95G FLOPs and 2.33M parameters, reducing computational cost by 94.7% and parameter count by 75.7%, with only a 0.4% drop in accuracy. This study provides an efficient solution for real\u2010time 3D recognition applications, significantly lowering computational resource requirements while maintaining\u00a0performance.<\/jats:p>","DOI":"10.1049\/ipr2.70226","type":"journal-article","created":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T15:09:53Z","timestamp":1760022593000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An Efficient and Lightweight Point Cloud Recognition Network Based on Neighborhood Learning"],"prefix":"10.1049","volume":"19","author":[{"given":"Yanxia","family":"Bao","sequence":"first","affiliation":[{"name":"School of Mathematics and Computer Science LiShui University Zhejiang China"}]},{"given":"Zilong","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering Zhejiang Sci\u2010Tech University Zhejiang China"}]},{"given":"Yahong","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Mathematics and Computer Science LiShui University Zhejiang China"}]},{"given":"Yang","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Mathematics and Computer Science LiShui University Zhejiang China"},{"name":"Information Department Zhejiang Tianrun Electric Co., Ltd Zhejiang China"}]}],"member":"265","published-online":{"date-parts":[[2025,10,9]]},"reference":[{"key":"e_1_2_10_2_1","first-page":"881","volume-title":"IEEE Intelligent Vehicles Symposium","author":"Gao X.","year":"2021"},{"key":"e_1_2_10_3_1","doi-asserted-by":"crossref","unstructured":"Z.Wang C.Nguyen P.Asente andJ.Dorsey \u201cPointshopar: Supporting Environmental Design Prototyping Using Point Cloud in Augmented Reality \u201d inCHI '23: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems(ACM 2023) 1\u201315.","DOI":"10.1145\/3544548.3580776"},{"key":"e_1_2_10_4_1","first-page":"77799","volume-title":"Advances in Neural Information Processing Systems","author":"Zhu H.","year":"2024"},{"key":"e_1_2_10_5_1","unstructured":"S.MehtaandM.Rastegari \u201cMobilevit: Light\u2010Weight General\u2010Purpose and Mobile\u2010Friendly Vision Transformer \u201darXiv preprint arXiv:2110.02178(2021)."},{"key":"e_1_2_10_6_1","doi-asserted-by":"crossref","unstructured":"C.Park Y.Jeong M.Cho andJ.Park \u201cFast Point Transformer \u201d in2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)(IEEE 2022) 16928\u201316937.","DOI":"10.1109\/CVPR52688.2022.01644"},{"key":"e_1_2_10_7_1","doi-asserted-by":"crossref","unstructured":"O.Dovrat I.Lang andS.Avidan \u201cLearning to Sample \u201d in2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)(IEEE 2019) 2755\u20132764.","DOI":"10.1109\/CVPR.2019.00287"},{"key":"e_1_2_10_8_1","doi-asserted-by":"crossref","unstructured":"I.Lang A.Manor andS.Avidan \u201cSamplenet: Differentiable Point Cloud Sampling \u201d in2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)(IEEE 2020) 7575\u20137585.","DOI":"10.1109\/CVPR42600.2020.00760"},{"key":"e_1_2_10_9_1","doi-asserted-by":"crossref","unstructured":"Y.Lin Y.Huang S.Zhou M.Jiang T.Wang andY.Lei \u201cDa\u2010Net: Density\u2010Adaptive Downsampling Network for Point Cloud Classification Via End\u2010to\u2010End Learning \u201d in2021 4th International Conference on Pattern Recognition and Artificial Intelligence (PRAI)(IEEE 2021) 13\u201318.","DOI":"10.1109\/PRAI53619.2021.9551070"},{"key":"e_1_2_10_10_1","doi-asserted-by":"crossref","unstructured":"H.Su S.Maji E.Kalogerakis andE.Learned\u2010Miller \u201cMulti\u2010View Convolutional Neural Networks for 3D Shape Recognition \u201d in2015 IEEE International Conference on Computer Vision (ICCV)(IEEE 2015) 945\u2013953.","DOI":"10.1109\/ICCV.2015.114"},{"key":"e_1_2_10_11_1","doi-asserted-by":"crossref","unstructured":"S.Bai X.Bai Z.Zhou Z.Zhang andL.Jan Latecki \u201cGift: A Real\u2010Time and Scalable 3D Shape Search Engine \u201d in2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)(IEEE 2016) 5023\u20135032.","DOI":"10.1109\/CVPR.2016.543"},{"key":"e_1_2_10_12_1","unstructured":"C. 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