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However, the inherent propagation characteristics of mmWave signals often result in highly sparse point clouds with limited semantic information and the entanglement of temporal-topological features, making human semantic segmentation a challenging task. To address these challenges, mmSeg (i) first introduces a radar cross-section (RCS) calculation method suitable for commercial millimeter-wave radar to enhance the semantic information of radar point clouds at a coarse granularity; (ii) further designs a temporal-topological decoupling network to obtain the fine-grained human semantic segmentation results; (iii) constructs an efficient loss function for end-to-end training, based on an adjacency matrix graph to improve the segmentation performance. We evaluate mmSeg on our self-built millimeter-wave dataset HSS and a public dataset MM-Fi. mmSeg achieves an average point cloud segmentation accuracy of 87.74% on the HSS dataset and 84.18% on the MM-Fi dataset, outperforming the existing methods in both cases.<\/jats:p>","DOI":"10.1145\/3786769","type":"journal-article","created":{"date-parts":[[2025,12,25]],"date-time":"2025-12-25T12:07:34Z","timestamp":1766664454000},"page":"1-32","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["mmSeg: Leveraging mmWave Radar for Fine-grained Human Semantic Segmentation"],"prefix":"10.1145","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-0637-195X","authenticated-orcid":false,"given":"Ruili","family":"Shi","sequence":"first","affiliation":[{"name":"Southeast University","place":["Nanjing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2766-1135","authenticated-orcid":false,"given":"Shuai","family":"Wang","sequence":"additional","affiliation":[{"name":"Southeast University","place":["Nanjing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2338-0256","authenticated-orcid":false,"given":"Luoyu","family":"Mei","sequence":"additional","affiliation":[{"name":"Southeast University","place":["Nanjing, China"]},{"name":"City University of Hong Kong","place":["Nanjing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-8776-0777","authenticated-orcid":false,"given":"Xuehan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Southeast University","place":["Nanjing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0544-8253","authenticated-orcid":false,"given":"Zhao-Dong","family":"Xu","sequence":"additional","affiliation":[{"name":"Southeast University","place":["Nanjing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3609-2205","authenticated-orcid":false,"given":"Shuai","family":"Wang","sequence":"additional","affiliation":[{"name":"Southeast University","place":["Nanjing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,2,5]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"crossref","unstructured":"Aakriti Adhikari and Sanjib Sur. 2024. 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