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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPEW34M/49SP3UH
Repositorysid.inpe.br/sibgrapi/2023/09.26.00.25
Last Update2024:01.31.12.51.34 (UTC) gmnetto@inf.ufrgs.br
Metadata Repositorysid.inpe.br/sibgrapi/2023/09.26.00.25.49
Metadata Last Update2024:01.31.12.51.34 (UTC) gmnetto@inf.ufrgs.br
Citation KeyNetto:2023:RoPoRe
TitleRobust Point-Cloud Registration based on Dense Point Matching and Probabilistic Modeling
FormatOn-line
Year2023
Access Date2024, May 02
Number of Files1
Size3816 KiB
2. Context
AuthorNetto, Gustavo Marques
AffiliationUFRGS
EditorClua, Esteban Walter Gonzalez
Körting, Thales Sehn
Paulovich, Fernando Vieira
Feris, Rogerio
e-Mail Addressgmnetto@inf.ufrgs.br
Conference NameConference on Graphics, Patterns and Images, 36 (SIBGRAPI)
Conference LocationRio Grande, RS
DateNov. 06-09, 2023
Book TitleProceedings
Tertiary TypeMaster's or Doctoral Work
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
KeywordsPoint-cloud registration
rigid registration
non-rigid registration
dense point matching
AbstractThis thesis presents techniques for 3D point-cloud registration that are robust to outliers and missing regions. They tackle non-rigid and rigid registration and exploit the advantages of deep learning for dense point matching. This is done by proposing a single new neural network to solve both registration types. Our network uses a recently proposed attention mechanism and explicitly accounts for missing correspondences, which is key to its performance. Additionally, we use recent advances in probabilistic modeling to further refine the correspondences created by our network during non-rigid registration. Such a combination of deep learning and probabilistic modeling produces context awareness and enforces motion coherence, which makes our approach resilient to outliers and missing information. We demonstrate the effectiveness of our techniques by comparing them to state-of-the-art methods. Our comparisons use datasets containing noise, partial point clouds, and irregular sampling. The experiments show that our techniques obtain superior results in general. For example, our approaches achieve a registration error up to 45% smaller than other techniques in partial point clouds for non-rigid registration, and up to 49% smaller on rigid registration. We also discuss additional aspects of our techniques such as robustness to different levels of noise and to different numbers of samples in the point clouds. Finally, we tackle the lack of datasets with ground truth for supervised training of non-rigid registration models by presenting a self-supervised strategy based on random deformations.
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/49SP3UH
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/49SP3UH
Languageen
Target FileSIBGRAPI2023_Netto-1-1.pdf
User Groupgmnetto@inf.ufrgs.br
Visibilityshown
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage doi edition electronicmailaddress group holdercode isbn issn label lineage mark nextedition nexthigherunit notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project publisher publisheraddress readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume
7. Description control
e-Mail (login)gmnetto@inf.ufrgs.br
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