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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPEW34M/43BG2TB
Repositorysid.inpe.br/sibgrapi/2020/09.30.16.45
Last Update2020:09.30.16.45.08 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2020/09.30.16.45.08
Metadata Last Update2022:06.14.00.00.15 (UTC) administrator
DOI10.1109/SIBGRAPI51738.2020.00052
Citation KeyBatistadaCunhaSanValMagTei:2020:StImDo
TitleA Study on the Impact of Domain Randomization for Monocular Deep 6DoF Pose Estimation
FormatOn-line
Year2020
Access Date2024, Apr. 25
Number of Files1
Size9047 KiB
2. Context
Author1 Batista da Cunha, Kelvin
2 dos Santos Brito, Caio José
3 Valença da Rocha Martins Albuquerque, Lucas
4 Magalhães Simões, Francisco Paulo
5 Teichrieb, Veronica
Affiliation1 Voxar Labs, Centro de Informática, Universidade Federal de Pernambuco
2 Voxar Labs, Centro de Informática, Universidade Federal de Pernambuco
3 Voxar Labs, Centro de Informática, Universidade Federal de Pernambuco
4 Curso Técnico em Informática para Internet, Instituto Federal de Pernambuco, Campus Belo Jardim
5 Voxar Labs, Centro de Informática, Universidade Federal de Pernambuco
EditorMusse, Soraia Raupp
Cesar Junior, Roberto Marcondes
Pelechano, Nuria
Wang, Zhangyang (Atlas)
e-Mail Addresskbc@cin.ufpe.br
Conference NameConference on Graphics, Patterns and Images, 33 (SIBGRAPI)
Conference LocationPorto de Galinhas (virtual)
Date7-10 Nov. 2020
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2020-09-30 16:45:08 :: kbc@cin.ufpe.br -> administrator ::
2022-06-14 00:00:15 :: administrator -> kbc@cin.ufpe.br :: 2020
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
KeywordsPose Estimation
Deep Learning
Domain Randomization
AbstractIn this work, we apply domain randomization to synthetic images and train deep 6DoF monocular RGB pose estimation models to work on a real object. We compare 19 models trained with different combinations of synthetic and real data (fully synthetic, fully real, initially synthetic and supplemented with real, and a real-synthetic randomized mix). By gradually decreasing the amount of real data used, we show it is possible for deep 6DoF detection to obtain superior results while using less real data (which is harder to obtain) and suggest the best approach to train a model with synthetic data. Our method is validated using a textureless 3D printed object, as the textureless category is a challenging, common open problem in itself. A real and a synthetic dataset generated for this work, totalling over 24,800 annotated frames, are also made public. We also show that synthetic, randomized data can help generalize a model by training it to handle challenges such as illumination changes and fast motion. Finally, we also evaluate how a model trained for one camera sensor works with a different one, and show that synthetic simulations of real cameras can help overcoming this challenge.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2020 > A Study on...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > A Study on...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/43BG2TB
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/43BG2TB
Languageen
Target File108.pdf
User Groupkbc@cin.ufpe.br
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/43G4L9S
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2020/10.28.20.46 4
sid.inpe.br/sibgrapi/2022/06.10.21.49 1
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume
7. Description control
e-Mail (login)kbc@cin.ufpe.br
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