1. Identity statement | |
Reference Type | Other (Misc) |
Site | sibgrapi.sid.inpe.br |
Identifier | 8JMKD3MGPEW34M/49U8AAP |
Repository | sid.inpe.br/sibgrapi/2023/10.04.22.56 |
Last Update | 2023:10.04.22.56.27 (UTC) gmnetto@inf.ufrgs.br |
Metadata Repository | sid.inpe.br/sibgrapi/2023/10.04.22.56.27 |
Metadata Last Update | 2023:10.04.22.56.27 (UTC) gmnetto@inf.ufrgs.br |
Citation Key | Netto:2023:RoPoRe |
Title | Robust Point-Cloud Registration based on Dense Point Matching and Probabilistic Modeling |
Short Title | Authorization |
Format | On-line |
Year | 2023 |
Date | Nov. 06-09, 2023 |
Access Date | 2024, May 02 |
Number of Files | 1 |
Size | 225 KiB |
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2. Context | |
Author | Netto, Gustavo Marques |
Affiliation | UFRGS |
e-Mail Address | gmnetto@inf.ufrgs.br |
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3. Content and structure | |
Is the master or a copy? | is the master |
Content Stage | completed |
Transferable | 1 |
Keywords | Point-cloud registration rigid registration non-rigid registration dense point matching |
Abstract | This 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. |
Arrangement | Robust Point-Cloud Registration... > Authorization |
doc Directory Content | access |
source Directory Content | there are no files |
agreement Directory Content | |
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4. Conditions of access and use | |
data URL | http://urlib.net/ibi/8JMKD3MGPEW34M/49U8AAP |
zipped data URL | http://urlib.net/zip/8JMKD3MGPEW34M/49U8AAP |
Language | en |
Target File | autorizacao_publicacao_assinada.pdf |
User Group | gmnetto@inf.ufrgs.br |
Visibility | shown |
Update Permission | not transferred |
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5. Allied materials | |
Mirror Repository | sid.inpe.br/banon/2001/03.30.15.38.24 |
Next Higher Units | 8JMKD3MGPEW34M/49SP3UH |
Host Collection | sid.inpe.br/banon/2001/03.30.15.38 |
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6. Notes | |
Empty Fields | archivingpolicy archivist area callnumber city contenttype copyholder copyright creatorhistory descriptionlevel dissemination doi edition electronicmailaddress group holdercode howpublished isbn issn label lineage mark nextedition notes number numberofpages orcid parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype session sponsor subject tertiarymark tertiarytype type url versiontype |
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7. Description control | |
e-Mail (login) | gmnetto@inf.ufrgs.br |
update | |
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