{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:05:08Z","timestamp":1777705508413,"version":"3.51.4"},"reference-count":7,"publisher":"SAGE Publications","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2024,3,5]]},"abstract":"<jats:p>3D point cloud has irregularity and disorder, which pose challenges for point cloud analysis. In the past, the projection or point cloud voxelization methods often used were insufficient in accuracy and speed. In recent years, the methods using Transformer in the NLP field or ResNet in the deep learning field have shown promising results. This article expands these ideas and introduces a novel approach. This paper designs a model AaDR-PointCloud that combines self-attention blocks and deep residual point blocks and operates iteratively to extract point cloud information. The self-attention blocks used in the model are particularly suitable for point cloud processing because of their order independence. The deep residual point blocks used provide the expression of depth features. The model performs point cloud classification and segmentation tests on two shape classification datasets and an object part segmentation dataset, achieving higher accuracy on these benchmarks.<\/jats:p>","DOI":"10.3233\/jifs-231997","type":"journal-article","created":{"date-parts":[[2024,1,16]],"date-time":"2024-01-16T11:05:44Z","timestamp":1705403144000},"page":"6265-6277","source":"Crossref","is-referenced-by-count":0,"title":["AaDR-PointCloud: An integrated point cloud processing network using attention and deep residual"],"prefix":"10.1177","volume":"46","author":[{"given":"Bo","family":"Li","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"TongWei","family":"Lu","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Intelligent Robot (Wuhan Institute of Technology), China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feng","family":"Min","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Intelligent Robot (Wuhan Institute of Technology), China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"4CD","key":"10.3233\/JIFS-231997_ref6","first-page":"71.1","article-title":"Point convolutional neural networks by extension operators","volume":"37","author":"Atzmon","year":"2018","journal-title":"ACM Transactions on Graphics"},{"issue":"6cd","key":"10.3233\/JIFS-231997_ref15","first-page":"210.1","article-title":"A scalable active framework for region annotation in 3d shape collections","volume":"35","author":"Yi","year":"2016","journal-title":"ACM Transactions on Graphics (TOG)"},{"issue":"99","key":"10.3233\/JIFS-231997_ref28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TPAMI.2020.3043745","article-title":"Deep learning for 3d point clouds: A survey","volume":"PP","author":"Guo","year":"2020","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"5","key":"10.3233\/JIFS-231997_ref30","first-page":"2018","article-title":"Dynamic graph cnn for learning on point clouds","volume":"38","author":"Wang","journal-title":"ACM Transactions on Graphics"},{"key":"10.3233\/JIFS-231997_ref41","doi-asserted-by":"crossref","first-page":"8778","DOI":"10.1609\/aaai.v33i01.33018778","article-title":"Point2sequence: Learning the shape representation of 3d point clouds with an attention-based sequence to sequence network,","volume":"33","author":"Liu","year":"2019","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"10.3233\/JIFS-231997_ref48","doi-asserted-by":"crossref","first-page":"1943","DOI":"10.1109\/TMM.2021.3074240","article-title":"Geometric back-projection network for point cloud classification,","volume":"24","author":"Qiu","year":"2021","journal-title":"IEEE Transactions on Multimedia"},{"key":"10.3233\/JIFS-231997_ref49","doi-asserted-by":"crossref","unstructured":"Cheng S. , Chen X. , He X. , Liu Z. and Bai X. , Pra-net: Point relation-aware network for 3d point cloud analysis, IEEE Transactions on Image Processing PP(99) (2021).","DOI":"10.1109\/TIP.2021.3072214"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-231997","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:44:04Z","timestamp":1777455844000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-231997"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,5]]},"references-count":7,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.3233\/jifs-231997","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,5]]}}}