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and Technology Special Project Subjects of Jilin Province and Changchun City","award":["61934003"],"award-info":[{"award-number":["61934003"]}]},{"name":"Major Science and Technology Special Project Subjects of Jilin Province and Changchun City","award":["20210301014GX"],"award-info":[{"award-number":["20210301014GX"]}]},{"name":"Major Science and Technology Special Project Subjects of Jilin Province and Changchun City","award":["20200501007GX"],"award-info":[{"award-number":["20200501007GX"]}]},{"name":"Major Science and Technology Special Project Subjects of Jilin Province and Changchun City","award":["2021TD-39"],"award-info":[{"award-number":["2021TD-39"]}]},{"name":"Major Scientific and Technological Program of Jilin Province","award":["2022YFB2804503"],"award-info":[{"award-number":["2022YFB2804503"]}]},{"name":"Major Scientific and Technological Program of Jilin Province","award":["62090053"],"award-info":[{"award-number":["62090053"]}]},{"name":"Major Scientific and 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University","award":["62090053"],"award-info":[{"award-number":["62090053"]}]},{"name":"Program for Science and Technology Innovative Research Team of Jilin University","award":["62090054"],"award-info":[{"award-number":["62090054"]}]},{"name":"Program for Science and Technology Innovative Research Team of Jilin University","award":["61934003"],"award-info":[{"award-number":["61934003"]}]},{"name":"Program for Science and Technology Innovative Research Team of Jilin University","award":["20210301014GX"],"award-info":[{"award-number":["20210301014GX"]}]},{"name":"Program for Science and Technology Innovative Research Team of Jilin University","award":["20200501007GX"],"award-info":[{"award-number":["20200501007GX"]}]},{"name":"Program for Science and Technology Innovative Research Team of Jilin University","award":["2021TD-39"],"award-info":[{"award-number":["2021TD-39"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>It has become routine to directly process point clouds using a combination of shared multilayer perceptrons and aggregate functions. However, this practice has difficulty capturing the local information of point clouds, leading to information loss. Nevertheless, several recent works have proposed models that establish point-to-point relationships based on this procedure. However, to address the information loss, in this study we use self-supervised methods to enhance the network\u2019s understanding of point clouds. Our proposed multigrid autoencoder (MA) constrains the encoder part of the classification network so that it gains an understanding of the point cloud as it reconstructs it. With the help of self-supervised learning, we find the original network improves performance. We validate our model on PointNet++, and the experimental results show that our method improves overall classification accuracy by 2.0% and 4.7% with ModelNet40 and ScanObjectNN datasets, respectively.<\/jats:p>","DOI":"10.3390\/s22218115","type":"journal-article","created":{"date-parts":[[2022,10,24]],"date-time":"2022-10-24T10:09:23Z","timestamp":1666606163000},"page":"8115","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Self-Supervised Learning for Point-Cloud Classification by a Multigrid Autoencoder"],"prefix":"10.3390","volume":"22","author":[{"given":"Ruifeng","family":"Zhai","sequence":"first","affiliation":[{"name":"State Key Laboratory of Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun 130012, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junfeng","family":"Song","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun 130012, China"},{"name":"Peng Cheng Laboratory, Shenzhen 518000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuzhao","family":"Hou","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun 130012, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fengli","family":"Gao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun 130012, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xueyan","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun 130012, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3145","DOI":"10.1109\/LRA.2018.2850061","article-title":"3DmFV: Three-Dimensional Point Cloud Classification in Real-Time Using Convolutional Neural Networks","volume":"3","author":"Lindenbaum","year":"2018","journal-title":"IEEE Robot. 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