{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T13:21:23Z","timestamp":1767705683509,"version":"build-2065373602"},"reference-count":48,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2022,11,28]],"date-time":"2022-11-28T00:00:00Z","timestamp":1669593600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003787","name":"Hebei Natural Science Foundation","doi-asserted-by":"publisher","award":["F2020202045","U21A20515","62271074","61972459","61971418","U2003109","62171321","62071157","62162044","32271983","LSU-KFJJ-2021-05"],"award-info":[{"award-number":["F2020202045","U21A20515","62271074","61972459","61971418","U2003109","62171321","62071157","62162044","32271983","LSU-KFJJ-2021-05"]}],"id":[{"id":"10.13039\/501100003787","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Natural Science Foundation of China","award":["F2020202045","U21A20515","62271074","61972459","61971418","U2003109","62171321","62071157","62162044","32271983","LSU-KFJJ-2021-05"],"award-info":[{"award-number":["F2020202045","U21A20515","62271074","61972459","61971418","U2003109","62171321","62071157","62162044","32271983","LSU-KFJJ-2021-05"]}]},{"name":"Open Research Fund of Key Laboratory of Space Utilization, Chinese Academy of Sciences","award":["F2020202045","U21A20515","62271074","61972459","61971418","U2003109","62171321","62071157","62162044","32271983","LSU-KFJJ-2021-05"],"award-info":[{"award-number":["F2020202045","U21A20515","62271074","61972459","61971418","U2003109","62171321","62071157","62162044","32271983","LSU-KFJJ-2021-05"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Real-time large-scale point cloud segmentation is an important but challenging task for practical applications such as remote sensing and robotics. Existing real-time methods have achieved acceptable performance by aggregating local information. However, most of them only exploit local spatial geometric or semantic information dependently, few considering the complementarity of both. In this paper, we propose a model named Spatial\u2013Semantic Incorporation Network (SSI-Net) for real-time large-scale point cloud segmentation. A Spatial-Semantic Cross-correction (SSC) module is introduced in SSI-Net as a basic unit. High-quality contextual features can be learned through SSC by correcting and updating high-level semantic information using spatial geometric cues and vice versa. Adopting the plug-and-play SSC module, we design SSI-Net as an encoder\u2013decoder architecture. To ensure efficiency, it also adopts a random sample-based hierarchical network structure. Extensive experiments on several prevalent indoor and outdoor datasets for point cloud semantic segmentation demonstrate that the proposed approach can achieve state-of-the-art performance.<\/jats:p>","DOI":"10.3390\/rs14236022","type":"journal-article","created":{"date-parts":[[2022,11,28]],"date-time":"2022-11-28T07:01:30Z","timestamp":1669618890000},"page":"6022","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Large-Scale Semantic Scene Understanding with Cross-Correction Representation"],"prefix":"10.3390","volume":"14","author":[{"given":"Yuehua","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8212-1361","authenticated-orcid":false,"given":"Jiguang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Automation of Chinese Academy of Sciences, Beijing 100090, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shibiao","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5805","DOI":"10.1007\/s00500-019-04355-y","article-title":"RobNet: Real-time road-object 3D point cloud segmentation based on SqueezeNet and cyclic CRF","volume":"24","author":"Sun","year":"2020","journal-title":"Soft Comput."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Hao, F., Li, J., Song, R., Li, Y., and Cao, K. 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