A method of generating depth images for view-based shape retrieval of 3D CAD models from partial point clouds
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Hyungki | - |
dc.contributor.author | Yeo, Changmo | - |
dc.contributor.author | Cha, Moohyun | - |
dc.contributor.author | Mun, Duhwan | - |
dc.date.accessioned | 2021-08-30T02:54:11Z | - |
dc.date.available | 2021-08-30T02:54:11Z | - |
dc.date.created | 2021-06-18 | - |
dc.date.issued | 2021-03 | - |
dc.identifier.issn | 1380-7501 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/49523 | - |
dc.description.abstract | Laser scanners can easily acquire the geometric data of physical environments in the form of point clouds. Industrial 3D reconstruction processes generally recognize objects from point clouds, which should include both geometric and semantic data. However, the recognition process is often a bottleneck in 3D reconstruction because it is labor intensive and requires expertise in domain knowledge. To address this problem, various methods have been developed to recognize objects by retrieving their corresponding models from a database via input geometric queries. In recent years, geometric data conversion to images and view-based 3D shape retrieval applications have demonstrated high accuracies. Depth images that encode the depth values as pixel intensities are frequently used for view-based 3D shape retrieval. However, geometric data collected from objects are often incomplete owing to occlusions and line-of-sight limitations. Images generated by occluded point clouds lower the view-based 3D object retrieval performance owing to loss of information. In this paper, we propose a viewpoint and image-resolution estimation method for view-based 3D shape retrieval from point cloud queries. Further, automatic selection of viewpoint and image resolution are proposed using the data acquisition rate and density calculations from sampled viewpoints and image resolutions. The retrieval performances for images generated by the proposed method are investigated via experiments and compared for various datasets. Additionally, view-based 3D shape retrieval performance with a deep convolutional neural network was investigated using the proposed method. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER | - |
dc.subject | SEGMENTATION | - |
dc.title | A method of generating depth images for view-based shape retrieval of 3D CAD models from partial point clouds | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Mun, Duhwan | - |
dc.identifier.doi | 10.1007/s11042-020-10283-z | - |
dc.identifier.scopusid | 2-s2.0-85098655305 | - |
dc.identifier.wosid | 000604479100012 | - |
dc.identifier.bibliographicCitation | MULTIMEDIA TOOLS AND APPLICATIONS, v.80, no.7, pp.10859 - 10880 | - |
dc.relation.isPartOf | MULTIMEDIA TOOLS AND APPLICATIONS | - |
dc.citation.title | MULTIMEDIA TOOLS AND APPLICATIONS | - |
dc.citation.volume | 80 | - |
dc.citation.number | 7 | - |
dc.citation.startPage | 10859 | - |
dc.citation.endPage | 10880 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Software Engineering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Theory & Methods | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.subject.keywordPlus | SEGMENTATION | - |
dc.subject.keywordAuthor | Depth image | - |
dc.subject.keywordAuthor | Image resolution selection | - |
dc.subject.keywordAuthor | Point cloud | - |
dc.subject.keywordAuthor | View-based 3D shape retrieval | - |
dc.subject.keywordAuthor | Viewpoint selection | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.