{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T15:17:38Z","timestamp":1783005458067,"version":"3.54.5"},"reference-count":43,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation for Colleges and Universities in Jiangsu Province","award":["21KJB520015"],"award-info":[{"award-number":["21KJB520015"]}]},{"name":"Natural Science Foundation for Colleges and Universities in Jiangsu Province","award":["2021K398C"],"award-info":[{"award-number":["2021K398C"]}]},{"name":"Jiangsu Postdoctoral Fund","award":["21KJB520015"],"award-info":[{"award-number":["21KJB520015"]}]},{"name":"Jiangsu Postdoctoral Fund","award":["2021K398C"],"award-info":[{"award-number":["2021K398C"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Similar to convolutional neural networks for image processing, existing analysis methods for 3D point clouds often require the designation of a local neighborhood to describe the local features of the point cloud. This local neighborhood is typically manually specified, which makes it impossible for the network to dynamically adjust the receptive field\u2019s range. If the range is too large, it tends to overlook local details, and if it is too small, it cannot establish global dependencies. To address this issue, we introduce in this paper a new concept: receptive field space (RFS). With a minor computational cost, we extract features from multiple consecutive receptive field ranges to form this new receptive field space. On this basis, we further propose a receptive field space attention mechanism, enabling the network to adaptively select the most effective receptive field range from RFS, thus equipping the network with the ability to adjust granularity adaptively. Our approach achieved state-of-the-art performance in both point cloud classification, with an overall accuracy (OA) of 94.2%, and part segmentation, achieving an mIoU of 86.0%, demonstrating the effectiveness of our method.<\/jats:p>","DOI":"10.3390\/s24134274","type":"journal-article","created":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T10:14:46Z","timestamp":1719828886000},"page":"4274","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Receptive Field Space for Point Cloud Analysis"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-0247-4148","authenticated-orcid":false,"given":"Zhongbin","family":"Jiang","sequence":"first","affiliation":[{"name":"School of Automation and Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hai","family":"Tao","sequence":"additional","affiliation":[{"name":"School of Automation and Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2686-3002","authenticated-orcid":false,"given":"Ye","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Automation and Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,1]]},"reference":[{"key":"ref_1","unstructured":"Yigzaw, S. 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