{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T16:23:54Z","timestamp":1775665434644,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,1,26]],"date-time":"2022-01-26T00:00:00Z","timestamp":1643155200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003696","name":"Electronics and Telecommunications Research Institute","doi-asserted-by":"publisher","award":["21ZH1200"],"award-info":[{"award-number":["21ZH1200"]}],"id":[{"id":"10.13039\/501100003696","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Point clouds acquired with LiDAR are widely adopted in various fields, such as three-dimensional (3D) reconstruction, autonomous driving, and robotics. However, the high-density point cloud of large scenes captured with Lidar usually contains a large number of virtual points generated by the specular reflections of reflective materials, such as glass. When applying such large-scale high-density point clouds, reflection noise may have a significant impact on 3D reconstruction and other related techniques. In this study, we propose a method that uses deep learning and multi-position sensor comparison method to remove noise due to reflections from high-density point clouds in large scenes. The proposed method converts large-scale high-density point clouds into a range image and subsequently uses a deep learning method and multi-position sensor comparison method for noise detection. This alleviates the limitation of the deep learning networks, specifically their inability to handle large-scale high-density point clouds. The experimental results show that the proposed algorithm can effectively detect and remove noise due to reflection.<\/jats:p>","DOI":"10.3390\/rs14030577","type":"journal-article","created":{"date-parts":[[2022,1,26]],"date-time":"2022-01-26T03:33:32Z","timestamp":1643168012000},"page":"577","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Reflective Noise Filtering of Large-Scale Point Cloud Using Transformer"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6456-9496","authenticated-orcid":false,"given":"Rui","family":"Gao","sequence":"first","affiliation":[{"name":"Department of Multimedia Engineering, Dongguk University-Seoul, 30 Pildongro-1-gil, Jung-gu, Seoul 04620, Korea"}]},{"given":"Mengyu","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Multimedia Engineering, Dongguk University-Seoul, 30 Pildongro-1-gil, Jung-gu, Seoul 04620, Korea"}]},{"given":"Seung-Jun","family":"Yang","sequence":"additional","affiliation":[{"name":"Electronics and Telecommunications Research Institute, 218 Gajeong-ro, Yuseong-gu, Daejeon 34129, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2219-0848","authenticated-orcid":false,"given":"Kyungeun","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Multimedia Engineering, Dongguk University-Seoul, 30 Pildongro-1-gil, Jung-gu, Seoul 04620, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1007\/s11263-013-0627-y","article-title":"Rotational Projection Statistics for 3D Local Surface Description and Object Recognition","volume":"105","author":"Guo","year":"2013","journal-title":"Int. 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