{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T01:41:42Z","timestamp":1772761302410,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2021,9,2]],"date-time":"2021-09-02T00:00:00Z","timestamp":1630540800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42001340 U1711267"],"award-info":[{"award-number":["42001340 U1711267"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41671400"],"award-info":[{"award-number":["41671400"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation,Ministry of Natural Resources","award":["KF-2020-05-068"],"award-info":[{"award-number":["KF-2020-05-068"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Feature extraction on point clouds is an essential task when analyzing and processing point clouds of 3D scenes. However, there still remains a challenge to adequately exploit local fine-grained features on point cloud data due to its irregular and unordered structure in a 3D space. To alleviate this problem, a Dilated Graph Attention-based Network (DGANet) with a certain feature for learning ability is proposed. Specifically, we first build a local dilated graph-like region for each input point to establish the long-range spatial correlation towards its corresponding neighbors, which allows the proposed network to access a wider range of geometric information of local points with their long-range dependencies. Moreover, by integrating the dilated graph attention module (DGAM) implemented by a novel offset\u2013attention mechanism, the proposed network promises to highlight the differing importance on each edge of the constructed local graph to uniquely learn the discrepancy feature of geometric attributes between the connected point pairs. Finally, all the learned edge attention features are further aggregated, allowing the most significant geometric feature representation of local regions by the graph\u2013attention pooling to fully extract local detailed features for each point. The validation experiments using two challenging benchmark datasets demonstrate the effectiveness and powerful generation ability of our proposed DGANet in both 3D object classification and segmentation tasks.<\/jats:p>","DOI":"10.3390\/rs13173484","type":"journal-article","created":{"date-parts":[[2021,9,2]],"date-time":"2021-09-02T23:05:12Z","timestamp":1630623912000},"page":"3484","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["DGANet: A Dilated Graph Attention-Based Network for Local Feature Extraction on 3D Point Clouds"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3048-1496","authenticated-orcid":false,"given":"Jie","family":"Wan","sequence":"first","affiliation":[{"name":"Key Laboratory of Geological and Evaluation of Ministry of Education, China University of Geosciences, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhong","family":"Xie","sequence":"additional","affiliation":[{"name":"Department of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"},{"name":"National Engineering Research Center of Geographic Information System, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7421-4915","authenticated-orcid":false,"given":"Yongyang","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"},{"name":"National Engineering Research Center of Geographic Information System, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziyin","family":"Zeng","sequence":"additional","affiliation":[{"name":"Department of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ding","family":"Yuan","sequence":"additional","affiliation":[{"name":"Department of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qinjun","family":"Qiu","sequence":"additional","affiliation":[{"name":"National Engineering Research Center of Geographic Information System, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.isprsjprs.2021.03.001","article-title":"A Point-Based Deep Learning Network for Semantic Segmentation of MLS Point Clouds","volume":"175","author":"Han","year":"2021","journal-title":"ISPRS J. 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