Publikation:

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

Angaben zur Forschungsförderung

European Union (EU): 336978

Projekt

LIA - Light Field Imaging and Analysis
Open Access-Veröffentlichung
Core Facility der Universität Konstanz

Gesperrt bis

Titel in einer weiteren Sprache

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

Forschungsvorhaben

Organisationseinheiten

Zeitschriftenheft

Zugehörige Datensätze in KOPS

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
Diese Publikation teilen