Registered Relief Depth (RRD) borobudur dataset for single-frame depth prediction on one-side artifacts

Single-frame depth prediction is an efficient 3D reconstruction method for one-side artifacts. However, for this purpose, ground truth images, where the pixels are associated with the actual depth, are needed. The small number of publicly accessible datasets is an issue with the restoration of cultu...

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Main Authors: Aufaclav Zatu Kusuma Frisky, Agus Harjoko, Lukman Awaludin, Andi Dharmawan, Nia Gella Augoestien, Ika Candradewi, Roghib Muhammad Hujja, Andi Putranto, Tri Hartono, Yudi Suhartono, Sebastian Zambanini, Robert Sablatnig
Format: Article
Language:English
Published: Elsevier 2021-04-01
Series:Data in Brief
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2352340921001372
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spelling doaj-7e8e2b580ee9434f968706b2af3084f12021-04-26T05:56:23ZengElsevierData in Brief2352-34092021-04-0135106853Registered Relief Depth (RRD) borobudur dataset for single-frame depth prediction on one-side artifactsAufaclav Zatu Kusuma Frisky0Agus Harjoko1Lukman Awaludin2Andi Dharmawan3Nia Gella Augoestien4Ika Candradewi5Roghib Muhammad Hujja6Andi Putranto7Tri Hartono8Yudi Suhartono9Sebastian Zambanini10Robert Sablatnig11Electronics and Instrumentations Lab, Department of Computer Science and Electronics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Yogyakarta, Indonesia; Computer Vision Lab, Institute of Visual Computing and Human-Centered Technology, Faculty of Informatics, Technische Universität Wien, Vienna, AustriaElectronics and Instrumentations Lab, Department of Computer Science and Electronics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Yogyakarta, Indonesia; Corresponding author.Electronics and Instrumentations Lab, Department of Computer Science and Electronics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Yogyakarta, IndonesiaElectronics and Instrumentations Lab, Department of Computer Science and Electronics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Yogyakarta, IndonesiaElectronics and Instrumentations Lab, Department of Computer Science and Electronics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Yogyakarta, IndonesiaElectronics and Instrumentations Lab, Department of Computer Science and Electronics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Yogyakarta, IndonesiaElectronics and Instrumentations Lab, Department of Computer Science and Electronics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Yogyakarta, IndonesiaDepartment of Archaeology, Faculty of Cultural Sciences, Universitas Gadjah Mada, Yogyakarta, IndonesiaBorobudur Conservation Center, Directorate General of Education and Culture of the Republic of Indonesia, Magelang, IndonesiaBorobudur Conservation Center, Directorate General of Education and Culture of the Republic of Indonesia, Magelang, IndonesiaComputer Vision Lab, Institute of Visual Computing and Human-Centered Technology, Faculty of Informatics, Technische Universität Wien, Vienna, AustriaComputer Vision Lab, Institute of Visual Computing and Human-Centered Technology, Faculty of Informatics, Technische Universität Wien, Vienna, AustriaSingle-frame depth prediction is an efficient 3D reconstruction method for one-side artifacts. However, for this purpose, ground truth images, where the pixels are associated with the actual depth, are needed. The small number of publicly accessible datasets is an issue with the restoration of cultural heritage objects. In addition, relief data with irregular characteristics due to nature and human treatment, such as decolorization caused by moss and chemical reaction is still not available. We therefore created a dataset of Borobudur temple reliefs registered with their depth for data availability to solve these problems. This data collection consists of 4608 × 3456 (4K) resolution and profound RGB frames and we call this dataset the Registered Relief Depth (RRD) Borobudur Dataset. The RGB images have been taken using an Olympus EM10 II Camera with a 14 mm f/3.5 lens and the depth images were obtained directly using an ASUS XTION scanner, acquired on the temple's reliefs at 15000–25000 lux day time. The registration process of RGB data and depth information was manually performed via control points and was directly supervised by the archaeologist. Apart of enriching the data availability, this dataset can become an opportunity for International researchers to understand more about Indonesian Cultural Heritages.http://www.sciencedirect.com/science/article/pii/S2352340921001372Single-frameDepth predictionReliefTempleRegistered imageOne-side artifact
collection DOAJ
language English
format Article
sources DOAJ
author Aufaclav Zatu Kusuma Frisky
Agus Harjoko
Lukman Awaludin
Andi Dharmawan
Nia Gella Augoestien
Ika Candradewi
Roghib Muhammad Hujja
Andi Putranto
Tri Hartono
Yudi Suhartono
Sebastian Zambanini
Robert Sablatnig
spellingShingle Aufaclav Zatu Kusuma Frisky
Agus Harjoko
Lukman Awaludin
Andi Dharmawan
Nia Gella Augoestien
Ika Candradewi
Roghib Muhammad Hujja
Andi Putranto
Tri Hartono
Yudi Suhartono
Sebastian Zambanini
Robert Sablatnig
Registered Relief Depth (RRD) borobudur dataset for single-frame depth prediction on one-side artifacts
Data in Brief
Single-frame
Depth prediction
Relief
Temple
Registered image
One-side artifact
author_facet Aufaclav Zatu Kusuma Frisky
Agus Harjoko
Lukman Awaludin
Andi Dharmawan
Nia Gella Augoestien
Ika Candradewi
Roghib Muhammad Hujja
Andi Putranto
Tri Hartono
Yudi Suhartono
Sebastian Zambanini
Robert Sablatnig
author_sort Aufaclav Zatu Kusuma Frisky
title Registered Relief Depth (RRD) borobudur dataset for single-frame depth prediction on one-side artifacts
title_short Registered Relief Depth (RRD) borobudur dataset for single-frame depth prediction on one-side artifacts
title_full Registered Relief Depth (RRD) borobudur dataset for single-frame depth prediction on one-side artifacts
title_fullStr Registered Relief Depth (RRD) borobudur dataset for single-frame depth prediction on one-side artifacts
title_full_unstemmed Registered Relief Depth (RRD) borobudur dataset for single-frame depth prediction on one-side artifacts
title_sort registered relief depth (rrd) borobudur dataset for single-frame depth prediction on one-side artifacts
publisher Elsevier
series Data in Brief
issn 2352-3409
publishDate 2021-04-01
description Single-frame depth prediction is an efficient 3D reconstruction method for one-side artifacts. However, for this purpose, ground truth images, where the pixels are associated with the actual depth, are needed. The small number of publicly accessible datasets is an issue with the restoration of cultural heritage objects. In addition, relief data with irregular characteristics due to nature and human treatment, such as decolorization caused by moss and chemical reaction is still not available. We therefore created a dataset of Borobudur temple reliefs registered with their depth for data availability to solve these problems. This data collection consists of 4608 × 3456 (4K) resolution and profound RGB frames and we call this dataset the Registered Relief Depth (RRD) Borobudur Dataset. The RGB images have been taken using an Olympus EM10 II Camera with a 14 mm f/3.5 lens and the depth images were obtained directly using an ASUS XTION scanner, acquired on the temple's reliefs at 15000–25000 lux day time. The registration process of RGB data and depth information was manually performed via control points and was directly supervised by the archaeologist. Apart of enriching the data availability, this dataset can become an opportunity for International researchers to understand more about Indonesian Cultural Heritages.
topic Single-frame
Depth prediction
Relief
Temple
Registered image
One-side artifact
url http://www.sciencedirect.com/science/article/pii/S2352340921001372
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