Full metadata record
DC poleHodnotaJazyk
dc.contributor.authorKomar, Alexander
dc.contributor.authorKammerer, Michael
dc.contributor.authorBarzegar Khalilsaraei, Saeedeh
dc.contributor.authorAugsdoerfer, Ursula
dc.contributor.editorSkala, Václav
dc.date.accessioned2024-07-31T18:36:16Z-
dc.date.available2024-07-31T18:36:16Z-
dc.date.issued2024
dc.identifier.citationWSCG 2024: full papers proceedings: 32. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p.325-330.en
dc.identifier.issn2464–4625 (online)
dc.identifier.issn2464–4617 (print)
dc.identifier.urihttp://hdl.handle.net/11025/57406
dc.format6 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencyen
dc.rights© Václav Skala - UNION Agencyen
dc.subjectneuronové sítěcs
dc.subjectGANcs
dc.subjectsigned distance fieldscs
dc.titleLatEd: A Geometric Latent Vector Editoren
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedUsing a neural network approach, a shape may be compressed to a one-dimensional vector, the so-called latent dimension or latent vector. This latent shape dimension is examined in this paper. This latent vector of a shape is used to identify the corresponding shape in a database. Two types of networks are evaluated in terms of lookup accuracy and reconstruction quality using a database of Lego pieces. Even with small training set a reasonable robustness to rotation and translation of the shapes was achieved. While a human can interpret uncompressed data just fine, the compressed values of the network might be cryptic and thus offer no insight regarding the uncompressed input. Therefore, we introduce a latent dimension editor which allows the user to examine the geometry content of the latent vector and its influence on the decoded shape. The latent vector editor enables the visual exploration of the latent vector, by making changes to the latent vector visible in real-time via a 3D visualization of the reconstructed object.en
dc.subject.translatedneural networksen
dc.subject.translatedGANen
dc.subject.translatedsigned distance fieldsen
dc.identifier.doihttps://doi.org/10.24132/10.24132/CSRN.3401.35
dc.type.statusPeer revieweden
Vyskytuje se v kolekcích:WSCG 2024: Full Papers Proceedings

Soubory připojené k záznamu:
Soubor Popis VelikostFormát 
D13-2024.pdfPlný text5,05 MBAdobe PDFZobrazit/otevřít
Zobrazit minimální záznam Zobrazit statistiky


Použijte tento identifikátor k citaci nebo jako odkaz na tento záznam: http://hdl.handle.net/11025/57406

Všechny záznamy v DSpace jsou chráněny autorskými právy, všechna práva vyhrazena.

hledání
navigace
  1. DSpace at University of West Bohemia
  2. Publikace ZČU / UWB Publications
  3. Sborníky ZČU / UWB Proceedings
  4. WSCG: Full Papers
  5. WSCG 2024: Full Papers Proceedings