Real-Time Variational Range Image Fusion and Visualization for Large-Scale Scenes Using GPU Hash Tables

Lade...
Vorschaubild
Dateien
Zu diesem Dokument gibt es keine Dateien.
Datum
2018
Herausgeber:innen
Kontakt
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
URI (zitierfähiger Link)
ArXiv-ID
Internationale Patentnummer
EU-Projektnummer
336978
DFG-Projektnummer
Forschungsförderung
Projekt
LIA - Light Field Imaging and Analysis
Open Access-Veröffentlichung
Gesperrt bis
Titel in einer weiteren Sprache
Forschungsvorhaben
Organisationseinheiten
Zeitschriftenheft
Publikationstyp
Beitrag zu einem Konferenzband
Publikationsstatus
Published
Erschienen in
2018 IEEE Winter Conference on Applications of Computer Vision (WACV). Piscataway, New Jersey: IEEE, 2018, pp. 912-920. ISBN 978-1-5386-5189-6. Available under: doi: 10.1109/WACV.2018.00105
Zusammenfassung

We present a real-time pipeline for large-scale 3D scene reconstruction from a single moving RGB-D camera together with interactive visualization. Our approach combines a time and space efficient data structure capable of representing large scenes, a local variational update algorithm and a visualization system. The environment's structure is reconstructed by integrating the depth image of each camera view into a sparse volume representation using a truncated signed distance function, which is organized via a hash table. Noise from real-world data is efficiently eliminated by immediately performing local variational refinements on newly integrated data. The whole pipeline is able to perform in real-time on consumer-available hardware and allows for simultaneous inspection of the currently reconstructed scene.

Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
004 Informatik
Schlagwörter
Image reconstruction, Real-time systems, Graphics processing units, Three-dimensional displays, Pipelines, Octrees, Data visualization
Konferenz
2018 IEEE Winter Conference on Applications of Computer Vision (WACV), 12. März 2018 - 15. März 2018, Lake Tahoe, USA
Rezension
undefined / . - undefined, undefined
Zitieren
ISO 690MARNIOK, Nico, Bastian GOLDLÜCKE, 2018. Real-Time Variational Range Image Fusion and Visualization for Large-Scale Scenes Using GPU Hash Tables. 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). Lake Tahoe, USA, 12. März 2018 - 15. März 2018. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). Piscataway, New Jersey: IEEE, 2018, pp. 912-920. ISBN 978-1-5386-5189-6. Available under: doi: 10.1109/WACV.2018.00105
BibTex
@inproceedings{Marniok2018RealT-42779,
  year={2018},
  doi={10.1109/WACV.2018.00105},
  title={Real-Time Variational Range Image Fusion and Visualization for Large-Scale Scenes Using GPU Hash Tables},
  isbn={978-1-5386-5189-6},
  publisher={IEEE},
  address={Piscataway, New Jersey},
  booktitle={2018 IEEE Winter Conference on Applications of Computer Vision (WACV)},
  pages={912--920},
  author={Marniok, Nico and Goldlücke, Bastian}
}
RDF
<rdf:RDF
    xmlns:dcterms="http://purl.org/dc/terms/"
    xmlns:dc="http://purl.org/dc/elements/1.1/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:bibo="http://purl.org/ontology/bibo/"
    xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
    xmlns:foaf="http://xmlns.com/foaf/0.1/"
    xmlns:void="http://rdfs.org/ns/void#"
    xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > 
  <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/42779">
    <dc:creator>Goldlücke, Bastian</dc:creator>
    <dc:language>eng</dc:language>
    <dc:creator>Marniok, Nico</dc:creator>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2018-07-04T16:35:29Z</dc:date>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dcterms:issued>2018</dcterms:issued>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:contributor>Marniok, Nico</dc:contributor>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dcterms:abstract xml:lang="eng">We present a real-time pipeline for large-scale 3D scene reconstruction from a single moving RGB-D camera together with interactive visualization. Our approach combines a time and space efficient data structure capable of representing large scenes, a local variational update algorithm and a visualization system. The environment's structure is reconstructed by integrating the depth image of each camera view into a sparse volume representation using a truncated signed distance function, which is organized via a hash table. Noise from real-world data is efficiently eliminated by immediately performing local variational refinements on newly integrated data. The whole pipeline is able to perform in real-time on consumer-available hardware and allows for simultaneous inspection of the currently reconstructed scene.</dcterms:abstract>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/42779"/>
    <dc:contributor>Goldlücke, Bastian</dc:contributor>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2018-07-04T16:35:29Z</dcterms:available>
    <dcterms:title>Real-Time Variational Range Image Fusion and Visualization for Large-Scale Scenes Using GPU Hash Tables</dcterms:title>
  </rdf:Description>
</rdf:RDF>
Interner Vermerk
xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter
Kontakt
URL der Originalveröffentl.
Prüfdatum der URL
Prüfungsdatum der Dissertation
Finanzierungsart
Kommentar zur Publikation
Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Ja
Begutachtet