Semantic Segmentation of Large-Scale Outdoor Point Clouds by Encoder-Decoder Shared MLPs with Multiple Losses
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Rim, Beanbonyka | - |
dc.contributor.author | Lee, Ahyoung | - |
dc.contributor.author | Hong, Min | - |
dc.date.accessioned | 2021-10-05T04:42:41Z | - |
dc.date.available | 2021-10-05T04:42:41Z | - |
dc.date.issued | 2021-08 | - |
dc.identifier.issn | 2072-4292 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/19858 | - |
dc.description.abstract | 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-decoder 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-decoder 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. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Multidisciplinary Digital Publishing Institute (MDPI) | - |
dc.title | Semantic Segmentation of Large-Scale Outdoor Point Clouds by Encoder-Decoder Shared MLPs with Multiple Losses | - |
dc.type | Article | - |
dc.publisher.location | 스위스 | - |
dc.identifier.doi | 10.3390/rs13163121 | - |
dc.identifier.scopusid | 2-s2.0-85112564439 | - |
dc.identifier.wosid | 000689750800001 | - |
dc.identifier.bibliographicCitation | Remote Sensing, v.13, no.16 | - |
dc.citation.title | Remote Sensing | - |
dc.citation.volume | 13 | - |
dc.citation.number | 16 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Environmental Sciences & Ecology | - |
dc.relation.journalResearchArea | Geology | - |
dc.relation.journalResearchArea | Remote Sensing | - |
dc.relation.journalResearchArea | Imaging Science & Photographic Technology | - |
dc.relation.journalWebOfScienceCategory | Environmental Sciences | - |
dc.relation.journalWebOfScienceCategory | Geosciences, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Remote Sensing | - |
dc.relation.journalWebOfScienceCategory | Imaging Science & Photographic Technology | - |
dc.subject.keywordAuthor | semantic segmentation | - |
dc.subject.keywordAuthor | 3D LiDAR point clouds | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | remote sensing | - |
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