Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Frisky, Aufaclav Zatu Kusuma, Harjoko, Agus, Awaludin, Lukman, Dharmawan, Andi, Augoestien, Nia Gella, Candradewi, Ika, Hujja, Roghib Muhammad, Putranto, Andi, Hartono, Tri, Suhartono, Yudi, Zambanini, Sebastian, Sablatnig, Robert
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7900221/
https://www.ncbi.nlm.nih.gov/pubmed/33665250
http://dx.doi.org/10.1016/j.dib.2021.106853
_version_ 1783654179285237760
author Frisky, Aufaclav Zatu Kusuma
Harjoko, Agus
Awaludin, Lukman
Dharmawan, Andi
Augoestien, Nia Gella
Candradewi, Ika
Hujja, Roghib Muhammad
Putranto, Andi
Hartono, Tri
Suhartono, Yudi
Zambanini, Sebastian
Sablatnig, Robert
author_facet Frisky, Aufaclav Zatu Kusuma
Harjoko, Agus
Awaludin, Lukman
Dharmawan, Andi
Augoestien, Nia Gella
Candradewi, Ika
Hujja, Roghib Muhammad
Putranto, Andi
Hartono, Tri
Suhartono, Yudi
Zambanini, Sebastian
Sablatnig, Robert
author_sort Frisky, Aufaclav Zatu Kusuma
collection PubMed
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.
format Online
Article
Text
id pubmed-7900221
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Elsevier
record_format MEDLINE/PubMed
spelling pubmed-79002212021-03-03 Registered Relief Depth (RRD) borobudur dataset for single-frame depth prediction on one-side artifacts Frisky, Aufaclav Zatu Kusuma Harjoko, Agus Awaludin, Lukman Dharmawan, Andi Augoestien, Nia Gella Candradewi, Ika Hujja, Roghib Muhammad Putranto, Andi Hartono, Tri Suhartono, Yudi Zambanini, Sebastian Sablatnig, Robert Data Brief Data Article 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. Elsevier 2021-02-06 /pmc/articles/PMC7900221/ /pubmed/33665250 http://dx.doi.org/10.1016/j.dib.2021.106853 Text en © 2021 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Data Article
Frisky, Aufaclav Zatu Kusuma
Harjoko, Agus
Awaludin, Lukman
Dharmawan, Andi
Augoestien, Nia Gella
Candradewi, Ika
Hujja, Roghib Muhammad
Putranto, Andi
Hartono, Tri
Suhartono, Yudi
Zambanini, Sebastian
Sablatnig, Robert
Registered Relief Depth (RRD) borobudur dataset for single-frame depth prediction on one-side artifacts
title 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_short 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
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7900221/
https://www.ncbi.nlm.nih.gov/pubmed/33665250
http://dx.doi.org/10.1016/j.dib.2021.106853
work_keys_str_mv AT friskyaufaclavzatukusuma registeredreliefdepthrrdborobudurdatasetforsingleframedepthpredictionononesideartifacts
AT harjokoagus registeredreliefdepthrrdborobudurdatasetforsingleframedepthpredictionononesideartifacts
AT awaludinlukman registeredreliefdepthrrdborobudurdatasetforsingleframedepthpredictionononesideartifacts
AT dharmawanandi registeredreliefdepthrrdborobudurdatasetforsingleframedepthpredictionononesideartifacts
AT augoestienniagella registeredreliefdepthrrdborobudurdatasetforsingleframedepthpredictionononesideartifacts
AT candradewiika registeredreliefdepthrrdborobudurdatasetforsingleframedepthpredictionononesideartifacts
AT hujjaroghibmuhammad registeredreliefdepthrrdborobudurdatasetforsingleframedepthpredictionononesideartifacts
AT putrantoandi registeredreliefdepthrrdborobudurdatasetforsingleframedepthpredictionononesideartifacts
AT hartonotri registeredreliefdepthrrdborobudurdatasetforsingleframedepthpredictionononesideartifacts
AT suhartonoyudi registeredreliefdepthrrdborobudurdatasetforsingleframedepthpredictionononesideartifacts
AT zambaninisebastian registeredreliefdepthrrdborobudurdatasetforsingleframedepthpredictionononesideartifacts
AT sablatnigrobert registeredreliefdepthrrdborobudurdatasetforsingleframedepthpredictionononesideartifacts