{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T09:00:53Z","timestamp":1765357253169,"version":"build-2065373602"},"reference-count":30,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2022,11,29]],"date-time":"2022-11-29T00:00:00Z","timestamp":1669680000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Korea Institute of Police Technology","award":["092021C26S03000","5199990814084","2022R1F1A1072626"],"award-info":[{"award-number":["092021C26S03000","5199990814084","2022R1F1A1072626"]}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea (NRF)","doi-asserted-by":"publisher","award":["092021C26S03000","5199990814084","2022R1F1A1072626"],"award-info":[{"award-number":["092021C26S03000","5199990814084","2022R1F1A1072626"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Recently, research using point clouds has been increasing with the development of 3D scanner technology. According to this trend, the demand for high-quality point clouds is increasing, but there is still a problem with the high cost of obtaining high-quality point clouds. Therefore, with the recent remarkable development of deep learning, point cloud up-sampling research, which uses deep learning to generate high-quality point clouds from low-quality point clouds, is one of the fields attracting considerable attention. This paper proposes a new point cloud up-sampling method called Point cloud Up-sampling via Multi-scale Features Attention (PU-MFA). Inspired by prior studies that reported good performance at generating high-quality dense point set using the multi-scale features or attention mechanisms, PU-MFA merges the two through a U-Net structure. In addition, PU-MFA adaptively uses multi-scale features to refine the global features effectively. The PU-MFA was compared with other state-of-the-art methods in various evaluation metrics through various experiments using the PU-GAN dataset, which is a synthetic point cloud dataset, and the KITTI dataset, which is the real-scanned point cloud dataset. In various experimental results, PU-MFA showed superior performance of generating high-quality dense point set in quantitative and qualitative evaluation compared to other state-of-the-art methods, proving the effectiveness of the proposed method. The attention map of PU-MFA was also visualized to show the effect of multi-scale features.<\/jats:p>","DOI":"10.3390\/s22239308","type":"journal-article","created":{"date-parts":[[2022,11,30]],"date-time":"2022-11-30T08:46:41Z","timestamp":1669798001000},"page":"9308","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["PU-MFA: Point Cloud Up-Sampling via Multi-Scale Features Attention"],"prefix":"10.3390","volume":"22","author":[{"given":"Hyungjun","family":"Lee","sequence":"first","affiliation":[{"name":"Graduate School of Automotive Engineering, Kookmin University, Seoul 02707, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1917-699X","authenticated-orcid":false,"given":"Sejoon","family":"Lim","sequence":"additional","affiliation":[{"name":"Department of Automobile and IT Convergence, Kookmin University, Seoul 02707, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Koide, K., Miura, J., and Menegatti, E. 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