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DC poleHodnotaJazyk
dc.contributor.authorMüller, Simone
dc.contributor.authorKranzlmüller, Dieter
dc.contributor.editorSkala, Václav
dc.date.accessioned2021-08-31T05:38:35Z
dc.date.available2021-08-31T05:38:35Z
dc.date.issued2021
dc.identifier.citationWSCG 2021: full papers proceedings: 29. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 21-30.en
dc.identifier.isbn978-80-86943-34-3
dc.identifier.issn2464-4617
dc.identifier.issn2464–4625(CD/DVD)
dc.identifier.urihttp://hdl.handle.net/11025/45006
dc.format10 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencycs
dc.rights© Václav Skala - UNION Agencycs
dc.subjectvícesenzorovýcs
dc.subjectdynamické párovánícs
dc.subjectstereoskopiecs
dc.subjectmračno bodůcs
dc.subjectreálný čascs
dc.subjectzískávání datcs
dc.subjectpočítačové viděnícs
dc.titleDynamic Sensor Matching for Parallel Point Cloud Data Acquisitionen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedBased on depth perception of individual stereo cameras, spatial structures can be derived as point clouds. Thequality of such three-dimensional data is technically restricted by sensor limitations, latency of recording, andinsufficient object reconstructions caused by surface illustration. Additionally external physical effects likelighting conditions, material properties, and reflections can lead to deviations between real and virtual objectperception. Such physical influences can be seen in rendered point clouds as geometrical imaging errors onsurfaces and edges. We propose the simultaneous use of multiple and dynamically arranged cameras. Theincreased information density leads to more details in surrounding detection and object illustration. During apre-processing phase the collected data are merged and prepared. Subsequently, a logical analysis part examinesand allocates the captured images to three-dimensional space. For this purpose, it is necessary to create a newmetadata set consisting of image and localisation data. The post-processing reworks and matches the locallyassigned images. As a result, the dynamic moving images become comparable so that a more accurate point cloudcan be generated. For evaluation and better comparability we decided to use synthetically generated data sets. Ourapproach builds the foundation for dynamic and real-time based generation of digital twins with the aid of realsensor data.en
dc.subject.translatedmulti-sensoren
dc.subject.translateddynamic matchingen
dc.subject.translatedstereoscopyen
dc.subject.translatedpoint clouden
dc.subject.translatedreal-timeen
dc.subject.translateddata acquisitionen
dc.subject.translatedcomputer visionen
dc.identifier.doihttps://doi.org/10.24132/CSRN.2021.3101.3
dc.type.statusPeer-revieweden
Vyskytuje se v kolekcích:WSCG 2021: Full Papers Proceedings

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