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Intelligent Reconstruction and Assembling of Pipeline from Point Cloud Data in Smart Plant 3D

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dc.contributor.authorHoli, Pavitra-
dc.contributor.authorPark, Seong Sill-
dc.contributor.authorPatil, Ashok Kumar-
dc.contributor.authorKumar, G. Ajay-
dc.contributor.authorChai, Young Ho-
dc.date.accessioned2021-08-18T02:40:19Z-
dc.date.available2021-08-18T02:40:19Z-
dc.date.issued2015-09-
dc.identifier.issn0302-9743-
dc.identifier.issn1611-3349-
dc.identifier.urihttps://scholarworks.bwise.kr/cau/handle/2019.sw.cau/48613-
dc.description.abstractThe laser-scanned data of subsisting industrial pipeline plants are not only astronomically immense, but are withal intricately entwined like a net. The users must identify 3D points corresponding to each pipeline to be modelled in immensely colossal laser-scanned data sets. To accurately identify the 3D points corresponding to each pipeline, the users need to have some cognizance of direction and design of the pipelines. In addition, manually identifying each pipeline from gigantic and intricate scanned data is proximately infeasible, time-consuming and cumbersome process. In order to simplify and make the process more facile for reconstruction process an intelligent way of reconstruction and assembling of pipeline from point cloud data in Smart Plant 3D (SP3D) is proposed. The presented results shows that the proposed method indeed contribute automation of 3D pipeline model.-
dc.format.extent11-
dc.language영어-
dc.language.isoENG-
dc.publisherSPRINGER INTERNATIONAL PUBLISHING AG-
dc.titleIntelligent Reconstruction and Assembling of Pipeline from Point Cloud Data in Smart Plant 3D-
dc.typeArticle-
dc.identifier.doi10.1007/978-3-319-24078-7_36-
dc.identifier.bibliographicCitationADVANCES IN MULTIMEDIA INFORMATION PROCESSING - PCM 2015, PT II, v.9315, pp 360 - 370-
dc.description.isOpenAccessN-
dc.identifier.wosid000366085100036-
dc.identifier.scopusid2-s2.0-84951875250-
dc.citation.endPage370-
dc.citation.startPage360-
dc.citation.titleADVANCES IN MULTIMEDIA INFORMATION PROCESSING - PCM 2015, PT II-
dc.citation.volume9315-
dc.type.docTypeProceedings Paper-
dc.publisher.location독일-
dc.subject.keywordAuthorPoint cloud-
dc.subject.keywordAuthorSegmentation-
dc.subject.keywordAuthorCylinder detection-
dc.subject.keywordAuthorSP3D automation-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalWebOfScienceCategoryComputer Science, Artificial Intelligence-
dc.relation.journalWebOfScienceCategoryComputer Science, Information Systems-
dc.relation.journalWebOfScienceCategoryComputer Science, Theory & Methods-
dc.description.journalRegisteredClassscopus-
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첨단영상대학원 (영상학과)
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