{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T21:33:34Z","timestamp":1770068014134,"version":"3.49.0"},"reference-count":39,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,6]],"date-time":"2021-08-06T00:00:00Z","timestamp":1628208000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"BK21 FOUR (Fostering Outstanding Universities for Research)","award":["No. :5199990914048"],"award-info":[{"award-number":["No. :5199990914048"]}]},{"name":"Soonchunhyang University Research Fund","award":["None"],"award-info":[{"award-number":["None"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Semantic segmentation of large-scale outdoor 3D LiDAR point clouds becomes essential to understand the scene environment in various applications, such as geometry mapping, autonomous driving, and more. With an advantage of being a 3D metric space, 3D LiDAR point clouds, on the other hand, pose a challenge for a deep learning approach, due to their unstructured, unorder, irregular, and large-scale characteristics. Therefore, this paper presents an encoder\u2013decoder shared multi-layer perceptron (MLP) with multiple losses, to address an issue of this semantic segmentation. The challenge rises a trade-off between efficiency and effectiveness in performance. To balance this trade-off, we proposed common mechanisms, which is simple and yet effective, by defining a random point sampling layer, an attention-based pooling layer, and a summation of multiple losses integrated with the encoder\u2013decoder shared MLPs method for the large-scale outdoor point clouds semantic segmentation. We conducted our experiments on the following two large-scale benchmark datasets: Toronto-3D and DALES dataset. Our experimental results achieved an overall accuracy (OA) and a mean intersection over union (mIoU) of both the Toronto-3D dataset, with 83.60% and 71.03%, and the DALES dataset, with 76.43% and 59.52%, respectively. Additionally, our proposed method performed a few numbers of parameters of the model, and faster than PointNet++ by about three times during inferencing.<\/jats:p>","DOI":"10.3390\/rs13163121","type":"journal-article","created":{"date-parts":[[2021,8,8]],"date-time":"2021-08-08T21:35:40Z","timestamp":1628458540000},"page":"3121","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Semantic Segmentation of Large-Scale Outdoor Point Clouds by Encoder\u2013Decoder Shared MLPs with Multiple Losses"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1232-0610","authenticated-orcid":false,"given":"Beanbonyka","family":"Rim","sequence":"first","affiliation":[{"name":"Department of Software Convergence, Soonchunhyang University, Asan 31538, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7467-3038","authenticated-orcid":false,"given":"Ahyoung","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Kennesaw State University, Marietta, GA 30144, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9963-5521","authenticated-orcid":false,"given":"Min","family":"Hong","sequence":"additional","affiliation":[{"name":"Department of Computer Software Engineering, Soonchunhyang University, Asan 31538, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bello, S.A., Yu, S., Wang, C., Adam, J.M., and Li, J. 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