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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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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