SHREC 2021: Retrieval of cultural heritage objects

Descripción del Articulo

This work has been partially supported by Proyecto de Mejoramiento y Ampliación de los Servicios del Sistema Nacional de Ciencia, Tecnología e Innovación Tecnológica (Banco Mundial, Concytec), Nr. Grant 062-2018-FONDECYT-BM-IADT-AV. This work was supported by the European Commission (Bigmedilytics 7...

Descripción completa

Detalles Bibliográficos
Autores: Sipiran I., Lazo P., Lopez C., Jimenez M., Bagewadi N., Bustos B., Dao H., Gangisetty S., Hanik M., Ho-Thi N.-P., Holenderski M., Jarnikov D., Labrada A., Lengauer S., Licandro R., Nguyen D.-H., Nguyen-Ho T.-L., Perez Rey L.A., Pham B.-D., Pham M.-K., Preiner R., Schreck T., Trinh Q.-H., Tonnaer L., von Tycowicz C., Vu-Le T.-A.
Formato: artículo
Fecha de Publicación:2021
Institución:Consejo Nacional de Ciencia Tecnología e Innovación
Repositorio:CONCYTEC-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.concytec.gob.pe:20.500.12390/3053
Enlace del recurso:https://hdl.handle.net/20.500.12390/3053
https://doi.org/10.1016/j.cag.2021.07.010
Nivel de acceso:acceso abierto
Materia:Cultural heritage
3D model retrieval
Benchmarking
https://purl.org/pe-repo/ocde/ford#5.08.04
id CONC_be56d4eb86d36d41d4ccdfa2d57aa297
oai_identifier_str oai:repositorio.concytec.gob.pe:20.500.12390/3053
network_acronym_str CONC
network_name_str CONCYTEC-Institucional
repository_id_str 4689
dc.title.none.fl_str_mv SHREC 2021: Retrieval of cultural heritage objects
title SHREC 2021: Retrieval of cultural heritage objects
spellingShingle SHREC 2021: Retrieval of cultural heritage objects
Sipiran I.
Cultural heritage
3D model retrieval
Benchmarking
https://purl.org/pe-repo/ocde/ford#5.08.04
title_short SHREC 2021: Retrieval of cultural heritage objects
title_full SHREC 2021: Retrieval of cultural heritage objects
title_fullStr SHREC 2021: Retrieval of cultural heritage objects
title_full_unstemmed SHREC 2021: Retrieval of cultural heritage objects
title_sort SHREC 2021: Retrieval of cultural heritage objects
author Sipiran I.
author_facet Sipiran I.
Lazo P.
Lopez C.
Jimenez M.
Bagewadi N.
Bustos B.
Dao H.
Gangisetty S.
Hanik M.
Ho-Thi N.-P.
Holenderski M.
Jarnikov D.
Labrada A.
Lengauer S.
Licandro R.
Nguyen D.-H.
Nguyen-Ho T.-L.
Perez Rey L.A.
Pham B.-D.
Pham M.-K.
Preiner R.
Schreck T.
Trinh Q.-H.
Tonnaer L.
von Tycowicz C.
Vu-Le T.-A.
author_role author
author2 Lazo P.
Lopez C.
Jimenez M.
Bagewadi N.
Bustos B.
Dao H.
Gangisetty S.
Hanik M.
Ho-Thi N.-P.
Holenderski M.
Jarnikov D.
Labrada A.
Lengauer S.
Licandro R.
Nguyen D.-H.
Nguyen-Ho T.-L.
Perez Rey L.A.
Pham B.-D.
Pham M.-K.
Preiner R.
Schreck T.
Trinh Q.-H.
Tonnaer L.
von Tycowicz C.
Vu-Le T.-A.
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
dc.contributor.author.fl_str_mv Sipiran I.
Lazo P.
Lopez C.
Jimenez M.
Bagewadi N.
Bustos B.
Dao H.
Gangisetty S.
Hanik M.
Ho-Thi N.-P.
Holenderski M.
Jarnikov D.
Labrada A.
Lengauer S.
Licandro R.
Nguyen D.-H.
Nguyen-Ho T.-L.
Perez Rey L.A.
Pham B.-D.
Pham M.-K.
Preiner R.
Schreck T.
Trinh Q.-H.
Tonnaer L.
von Tycowicz C.
Vu-Le T.-A.
dc.subject.none.fl_str_mv Cultural heritage
topic Cultural heritage
3D model retrieval
Benchmarking
https://purl.org/pe-repo/ocde/ford#5.08.04
dc.subject.es_PE.fl_str_mv 3D model retrieval
Benchmarking
dc.subject.ocde.none.fl_str_mv https://purl.org/pe-repo/ocde/ford#5.08.04
description This work has been partially supported by Proyecto de Mejoramiento y Ampliación de los Servicios del Sistema Nacional de Ciencia, Tecnología e Innovación Tecnológica (Banco Mundial, Concytec), Nr. Grant 062-2018-FONDECYT-BM-IADT-AV. This work was supported by the European Commission (Bigmedilytics 780495, TRABIT 765148), the Austrian Research Promotion Agency (FFG) - BRIDGE (grant number: 878730), the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy The Berlin Mathematics Research Center MATH+ (EXC-2046/1, project ID: 390685689), the Bundesministerium fuer Bildung und Forschung (BMBF) through BIFOLD - The Berlin Institute for the Foundations of Learning and Data (ref. 01IS18025A and ref 01IS18037A). This work has also received funding from the NWO-TTW Programme “Efficient Deep Learning”(EDL) P16-25. The-Anh Vu-Le was funded by Vingroup Joint Stock Company and supported by the Domestic Master/ PhD Scholarship Programme of Vingroup Innovation Foundation (VINIF), Vingroup Big Data Institute (VINBIGDATA), code VINIF.2020.ThS.JVN.01. The HCMUS team was funded by Gia Lam Urban Development and Investment Company Limited, Vingroup and supported by Vingroup Innovation Foundation (VINIF) under project code VINIF.2019.DA19. This work was also co-funded by the Austrian Science Fund FWF and the State of Styria, Austria within the project Crossmodal Search and Visual Exploration of 3D Cultural Heritage Objects (P31317-NBL).
publishDate 2021
dc.date.accessioned.none.fl_str_mv 2024-05-30T23:13:38Z
dc.date.available.none.fl_str_mv 2024-05-30T23:13:38Z
dc.date.issued.fl_str_mv 2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12390/3053
dc.identifier.doi.none.fl_str_mv https://doi.org/10.1016/j.cag.2021.07.010
dc.identifier.scopus.none.fl_str_mv 2-s2.0-85111653912
url https://hdl.handle.net/20.500.12390/3053
https://doi.org/10.1016/j.cag.2021.07.010
identifier_str_mv 2-s2.0-85111653912
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartof.none.fl_str_mv Computers and Graphics (Pergamon)
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Elsevier Ltd
publisher.none.fl_str_mv Elsevier Ltd
dc.source.none.fl_str_mv reponame:CONCYTEC-Institucional
instname:Consejo Nacional de Ciencia Tecnología e Innovación
instacron:CONCYTEC
instname_str Consejo Nacional de Ciencia Tecnología e Innovación
instacron_str CONCYTEC
institution CONCYTEC
reponame_str CONCYTEC-Institucional
collection CONCYTEC-Institucional
repository.name.fl_str_mv Repositorio Institucional CONCYTEC
repository.mail.fl_str_mv repositorio@concytec.gob.pe
_version_ 1839175780687413248
spelling Publicationrp06322600rp08762600rp08765600rp08774600rp08768600rp06301600rp08776600rp08769600rp08773600rp08770600rp08772600rp08767600rp08763600rp08764600rp08758600rp08771600rp08760600rp08756600rp08766600rp08757600rp08777600rp08754600rp08759600rp08755600rp08761600rp08775600Sipiran I.Lazo P.Lopez C.Jimenez M.Bagewadi N.Bustos B.Dao H.Gangisetty S.Hanik M.Ho-Thi N.-P.Holenderski M.Jarnikov D.Labrada A.Lengauer S.Licandro R.Nguyen D.-H.Nguyen-Ho T.-L.Perez Rey L.A.Pham B.-D.Pham M.-K.Preiner R.Schreck T.Trinh Q.-H.Tonnaer L.von Tycowicz C.Vu-Le T.-A.2024-05-30T23:13:38Z2024-05-30T23:13:38Z2021https://hdl.handle.net/20.500.12390/3053https://doi.org/10.1016/j.cag.2021.07.0102-s2.0-85111653912This work has been partially supported by Proyecto de Mejoramiento y Ampliación de los Servicios del Sistema Nacional de Ciencia, Tecnología e Innovación Tecnológica (Banco Mundial, Concytec), Nr. Grant 062-2018-FONDECYT-BM-IADT-AV. This work was supported by the European Commission (Bigmedilytics 780495, TRABIT 765148), the Austrian Research Promotion Agency (FFG) - BRIDGE (grant number: 878730), the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy The Berlin Mathematics Research Center MATH+ (EXC-2046/1, project ID: 390685689), the Bundesministerium fuer Bildung und Forschung (BMBF) through BIFOLD - The Berlin Institute for the Foundations of Learning and Data (ref. 01IS18025A and ref 01IS18037A). This work has also received funding from the NWO-TTW Programme “Efficient Deep Learning”(EDL) P16-25. The-Anh Vu-Le was funded by Vingroup Joint Stock Company and supported by the Domestic Master/ PhD Scholarship Programme of Vingroup Innovation Foundation (VINIF), Vingroup Big Data Institute (VINBIGDATA), code VINIF.2020.ThS.JVN.01. The HCMUS team was funded by Gia Lam Urban Development and Investment Company Limited, Vingroup and supported by Vingroup Innovation Foundation (VINIF) under project code VINIF.2019.DA19. This work was also co-funded by the Austrian Science Fund FWF and the State of Styria, Austria within the project Crossmodal Search and Visual Exploration of 3D Cultural Heritage Objects (P31317-NBL).This paper presents the methods and results of the SHREC’21 track on a dataset of cultural heritage (CH) objects. We present a dataset of 938 scanned models that have varied geometry and artistic styles. For the competition, we propose two challenges: the retrieval-by-shape challenge and the retrieval-by-culture challenge. The former aims at evaluating the ability of retrieval methods to discriminate cultural heritage objects by overall shape. The latter focuses on assessing the effectiveness of retrieving objects from the same culture. Both challenges constitute a suitable scenario to evaluate modern shape retrieval methods in a CH domain. Ten groups participated in the challenges: thirty runs were submitted for the retrieval-by-shape task, and twenty-six runs were submitted for the retrieval-by-culture task. The results show a predominance of learning methods on image-based multi-view representations to characterize 3D objects. Nevertheless, the problem presented in our challenges is far from being solved. We also identify the potential paths for further improvements and give insights into the future directions of research. © 2021 Elsevier LtdConsejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - ConcytecengElsevier LtdComputers and Graphics (Pergamon)info:eu-repo/semantics/openAccessCultural heritage3D model retrieval-1Benchmarking-1https://purl.org/pe-repo/ocde/ford#5.08.04-1SHREC 2021: Retrieval of cultural heritage objectsinfo:eu-repo/semantics/articlereponame:CONCYTEC-Institucionalinstname:Consejo Nacional de Ciencia Tecnología e Innovacióninstacron:CONCYTEC20.500.12390/3053oai:repositorio.concytec.gob.pe:20.500.12390/30532024-05-30 16:13:32.91http://purl.org/coar/access_right/c_14cbinfo:eu-repo/semantics/closedAccessmetadata only accesshttps://repositorio.concytec.gob.peRepositorio Institucional CONCYTECrepositorio@concytec.gob.pe#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="dedc56ea-e1e6-431d-bbea-f353cfe43667"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>SHREC 2021: Retrieval of cultural heritage objects</Title> <PublishedIn> <Publication> <Title>Computers and Graphics (Pergamon)</Title> </Publication> </PublishedIn> <PublicationDate>2021</PublicationDate> <DOI>https://doi.org/10.1016/j.cag.2021.07.010</DOI> <SCP-Number>2-s2.0-85111653912</SCP-Number> <Authors> <Author> <DisplayName>Sipiran I.</DisplayName> <Person id="rp06322" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Lazo P.</DisplayName> <Person id="rp08762" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Lopez C.</DisplayName> <Person id="rp08765" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Jimenez M.</DisplayName> <Person id="rp08774" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Bagewadi N.</DisplayName> <Person id="rp08768" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Bustos B.</DisplayName> <Person id="rp06301" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Dao H.</DisplayName> <Person id="rp08776" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Gangisetty S.</DisplayName> <Person id="rp08769" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Hanik M.</DisplayName> <Person id="rp08773" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Ho-Thi N.-P.</DisplayName> <Person id="rp08770" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Holenderski M.</DisplayName> <Person id="rp08772" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Jarnikov D.</DisplayName> <Person id="rp08767" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Labrada A.</DisplayName> <Person id="rp08763" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Lengauer S.</DisplayName> <Person id="rp08764" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Licandro R.</DisplayName> <Person id="rp08758" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Nguyen D.-H.</DisplayName> <Person id="rp08771" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Nguyen-Ho T.-L.</DisplayName> <Person id="rp08760" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Perez Rey L.A.</DisplayName> <Person id="rp08756" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Pham B.-D.</DisplayName> <Person id="rp08766" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Pham M.-K.</DisplayName> <Person id="rp08757" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Preiner R.</DisplayName> <Person id="rp08777" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Schreck T.</DisplayName> <Person id="rp08754" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Trinh Q.-H.</DisplayName> <Person id="rp08759" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Tonnaer L.</DisplayName> <Person id="rp08755" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>von Tycowicz C.</DisplayName> <Person id="rp08761" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Vu-Le T.-A.</DisplayName> <Person id="rp08775" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Elsevier Ltd</DisplayName> <OrgUnit /> </Publisher> </Publishers> <Keyword>Cultural heritage</Keyword> <Keyword>3D model retrieval</Keyword> <Keyword>Benchmarking</Keyword> <Abstract>This paper presents the methods and results of the SHREC’21 track on a dataset of cultural heritage (CH) objects. We present a dataset of 938 scanned models that have varied geometry and artistic styles. For the competition, we propose two challenges: the retrieval-by-shape challenge and the retrieval-by-culture challenge. The former aims at evaluating the ability of retrieval methods to discriminate cultural heritage objects by overall shape. The latter focuses on assessing the effectiveness of retrieving objects from the same culture. Both challenges constitute a suitable scenario to evaluate modern shape retrieval methods in a CH domain. Ten groups participated in the challenges: thirty runs were submitted for the retrieval-by-shape task, and twenty-six runs were submitted for the retrieval-by-culture task. The results show a predominance of learning methods on image-based multi-view representations to characterize 3D objects. Nevertheless, the problem presented in our challenges is far from being solved. We also identify the potential paths for further improvements and give insights into the future directions of research. © 2021 Elsevier Ltd</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication> -1
score 13.448654
Nota importante:
La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).