Depth Estimation From a Single RGB Image Using Fine-Tuned Generative Adversarial Network
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Islam, Naeem Ul | - |
dc.contributor.author | Park, Jaebyung | - |
dc.date.available | 2021-04-16T03:40:11Z | - |
dc.date.created | 2021-04-16 | - |
dc.date.issued | 2021-02 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/80763 | - |
dc.description.abstract | Estimating the depth map from a single RGB image is important to understand the nature of the terrain in robot navigation and has attracted considerable attention in the past decade. The existing approaches can accurately estimate the depth from a single RGB image, considering a highly structured environment. The problem becomes more challenging when the terrain is highly dynamic. We propose a fine-tuned generative adversarial network to estimate the depth map effectively for a given single RGB image. The proposed network is composed of a fine-tuned generator and a global discriminator. The encoder part of the generator takes input RGB images and depth maps and generates their joint distribution in the latent space. Subsequently, the decoder part of the generator decodes the depth map from the joint distribution. The discriminator takes real and fake pairs in three different configurations and then guides the generator to estimate the depth map from the given RGB image accordingly. Finally, we conducted extensive experiments with a highly dynamic environment dataset for verifying the effectiveness and feasibility of the proposed approach. The proposed approach could decode the depth map from the joint distribution more effectively and accurately than the existing approaches. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.relation.isPartOf | IEEE ACCESS | - |
dc.title | Depth Estimation From a Single RGB Image Using Fine-Tuned Generative Adversarial Network | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000623408300001 | - |
dc.identifier.doi | 10.1109/ACCESS.2021.3060435 | - |
dc.identifier.bibliographicCitation | IEEE ACCESS, v.9, pp.32781 - 32794 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85101899929 | - |
dc.citation.endPage | 32794 | - |
dc.citation.startPage | 32781 | - |
dc.citation.title | IEEE ACCESS | - |
dc.citation.volume | 9 | - |
dc.contributor.affiliatedAuthor | Islam, Naeem Ul | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | Estimation | - |
dc.subject.keywordAuthor | Generators | - |
dc.subject.keywordAuthor | Training | - |
dc.subject.keywordAuthor | Shape | - |
dc.subject.keywordAuthor | Robots | - |
dc.subject.keywordAuthor | Generative adversarial networks | - |
dc.subject.keywordAuthor | Three-dimensional displays | - |
dc.subject.keywordAuthor | Generative adversarial network | - |
dc.subject.keywordAuthor | convolutional neural network | - |
dc.subject.keywordAuthor | image translation | - |
dc.subject.keywordAuthor | auto-encoders | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
1342, Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Republic of Korea(13120)031-750-5114
COPYRIGHT 2020 Gachon 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.