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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPEW34M/45DPE5L
Repositorysid.inpe.br/sibgrapi/2021/09.11.20.09
Last Update2021:09.11.20.09.14 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2021/09.11.20.09.14
Metadata Last Update2022:09.10.00.16.17 (UTC) administrator
DOI10.1109/SIBGRAPI54419.2021.00012
Citation KeySchirmerSSNYMPVL:2021:NeNeIm
TitleNeural Networks for Implicit Representations of 3D Scenes
FormatOn-line
Year2021
Access Date2024, May 06
Number of Files1
Size5299 KiB
2. Context
Author1 Schirmer, Luiz
2 Schardong, Guilherme
3 Silva, Vinícius da
4 Novello, Tiago
5 Yukimura, Daniel
6 Magalhães, Thales
7 Paz, Hallison
8 Velho, Luiz
9 Lopes, Hélio
Affiliation1 PUC-Rio 
2 PUC-Rio 
3 PUC-Rio 
4 IMPA 
5 IMPA 
6 IMPA 
7 IMPA 
8 IMPA 
9 PUC-Rio
EditorPaiva, Afonso
Menotti, David
Baranoski, Gladimir V. G.
Proença, Hugo Pedro
Junior, Antonio Lopes Apolinario
Papa, João Paulo
Pagliosa, Paulo
dos Santos, Thiago Oliveira
e Sá, Asla Medeiros
da Silveira, Thiago Lopes Trugillo
Brazil, Emilio Vital
Ponti, Moacir A.
Fernandes, Leandro A. F.
Avila, Sandra
e-Mail Addressschirmer.luizj@gmail.com
Conference NameConference on Graphics, Patterns and Images, 34 (SIBGRAPI)
Conference LocationGramado, RS, Brazil (virtual)
Date18-22 Oct. 2021
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeTutorial
History (UTC)2021-10-05 00:47:12 :: schirmer.luizj@gmail.com -> administrator :: 2021
2022-03-03 04:41:59 :: administrator -> menottid@gmail.com :: 2021
2022-03-03 12:30:42 :: menottid@gmail.com -> administrator :: 2021
2022-09-10 00:16:17 :: administrator -> :: 2021
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
KeywordsNeural Networks
Implicit Functions
Signal Distance Functions
AbstractThis survey presents methods that use neural networks for implicit representations of 3D geometry --- neural implicit functions. We explore the different aspects of neural implicit functions for shape modeling and synthesis. We aim to provide a theoretical analysis of 3D shape reconstruction using deep neural networks and introduce a discussion between researchers interested in this research field.
Arrangementurlib.net > SDLA > Fonds > SIBGRAPI 2021 > Neural Networks for...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
agreement.html 11/09/2021 17:09 1.3 KiB 
4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/45DPE5L
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/45DPE5L
Languageen
Target FileTutorial_Sibgrapi_2021 (2).pdf
User Groupschirmer.luizj@gmail.com
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/45PQ3RS
Citing Item Listsid.inpe.br/sibgrapi/2021/11.12.11.46 4
sid.inpe.br/banon/2001/03.30.15.38.24 2
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


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