{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T15:42:11Z","timestamp":1766158931520,"version":"build-2065373602"},"reference-count":48,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,5,3]],"date-time":"2022-05-03T00:00:00Z","timestamp":1651536000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of China","award":["61720106009","2022GXNSFBA035661","2021RYJ06"],"award-info":[{"award-number":["61720106009","2022GXNSFBA035661","2021RYJ06"]}]},{"name":"Natural Science Foundation of Guangxi","award":["61720106009","2022GXNSFBA035661","2021RYJ06"],"award-info":[{"award-number":["61720106009","2022GXNSFBA035661","2021RYJ06"]}]},{"name":"Artificial Intelligence Key Laboratory of Sichuan Province","award":["61720106009","2022GXNSFBA035661","2021RYJ06"],"award-info":[{"award-number":["61720106009","2022GXNSFBA035661","2021RYJ06"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Large-scale 3D point clouds are rich in geometric shape and scale information but they are also scattered, disordered and unevenly distributed. These characteristics lead to difficulties in learning point cloud semantic segmentations. Although many works have performed well in this task, most of them lack research on spatial information, which limits the ability to learn and understand the complex geometric structure of point cloud scenes. To this end, we propose the multispatial information and dual adaptive (MSIDA) module, which consists of a multispatial information encoding (MSI) block and dual adaptive (DA) blocks. The MSI block transforms the information of the relative position of each centre point and its neighbouring points into a cylindrical coordinate system and spherical coordinate system. Then the spatial information among the points can be re-represented and encoded. The DA blocks include a Coordinate System Attention Pooling Fusion (CSAPF) block and a Local Aggregated Feature Attention (LAFA) block. The CSAPF block weights and fuses the local features in the three coordinate systems to further learn local features, while the LAFA block weights the local aggregated features in the three coordinate systems to better understand the scene in the local region. To test the performance of the proposed method, we conducted experiments on the S3DIS, Semantic3D and SemanticKITTI datasets and compared the proposed method with other networks. The proposed method achieved 73%, 77.8% and 59.8% mean Intersection over Union (mIoU) on the S3DIS, Semantic3D and SemanticKITTI datasets, respectively.<\/jats:p>","DOI":"10.3390\/rs14092187","type":"journal-article","created":{"date-parts":[[2022,5,4]],"date-time":"2022-05-04T08:21:25Z","timestamp":1651652485000},"page":"2187","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["MSIDA-Net: Point Cloud Semantic Segmentation via Multi-Spatial Information and Dual Adaptive Blocks"],"prefix":"10.3390","volume":"14","author":[{"given":"Feng","family":"Shuang","sequence":"first","affiliation":[{"name":"Guangxi Key Laboratory of Intelligent Control and Maintenance of Power Equipment, School of Electrical Engineering, Guangxi University, Nanning 530004, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4091-8513","authenticated-orcid":false,"given":"Pei","family":"Li","sequence":"additional","affiliation":[{"name":"Guangxi Key Laboratory of Intelligent Control and Maintenance of Power Equipment, School of Electrical Engineering, Guangxi University, Nanning 530004, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7230-3196","authenticated-orcid":false,"given":"Yong","family":"Li","sequence":"additional","affiliation":[{"name":"Guangxi Key Laboratory of Intelligent Control and Maintenance of Power Equipment, School of Electrical Engineering, Guangxi University, Nanning 530004, China"},{"name":"Artificial Intelligence Key Laboratory of Sichuan Province, Yi Bin 644000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0076-7311","authenticated-orcid":false,"given":"Zhenxin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing Advanced Innovation Center for Imaging Theory and Technology, Key Lab of 3D Information Acquisition and Application, College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1862-0569","authenticated-orcid":false,"given":"Xu","family":"Li","sequence":"additional","affiliation":[{"name":"Guangxi Key Laboratory of Intelligent Control and Maintenance of Power Equipment, School of Electrical Engineering, Guangxi University, Nanning 530004, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4338","DOI":"10.1109\/TPAMI.2020.3005434","article-title":"Deep Learning for 3D Point Clouds: A Survey","volume":"43","author":"Guo","year":"2020","journal-title":"IEEE Trans. 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