| DC Element | Wert | Sprache |
|---|---|---|
| dc.contributor.advisor | Frintrop, Simone | - |
| dc.contributor.advisor | Zhang, Jianwei | - |
| dc.contributor.author | Gao, Ge | - |
| dc.date.accessioned | 2021-07-13T15:42:48Z | - |
| dc.date.available | 2021-07-13T15:42:48Z | - |
| dc.date.issued | 2021 | - |
| dc.identifier.uri | https://ediss.sub.uni-hamburg.de/handle/ediss/9098 | - |
| dc.description.abstract | Knowing the 6D pose of an object is a prerequisite for robotic applications such as grasping and manipulation. Majority of existing methods rely on color as the primary data modality. Since depth contains object surface information, we study what depth can contribute to estimating 6D object pose. | en |
| dc.language.iso | en | de_DE |
| dc.publisher | Staats- und Universitätsbibliothek Hamburg Carl von Ossietzky | de |
| dc.rights | http://purl.org/coar/access_right/c_abf2 | de_DE |
| dc.subject.ddc | 004: Informatik | de_DE |
| dc.title | Learning 6D Object Pose from Point Clouds | en |
| dc.type | doctoralThesis | en |
| dcterms.dateAccepted | 2021-06-16 | - |
| dc.rights.cc | https://creativecommons.org/licenses/by/4.0/ | de_DE |
| dc.rights.rs | http://rightsstatements.org/vocab/InC/1.0/ | - |
| dc.type.casrai | Dissertation | - |
| dc.type.dini | doctoralThesis | - |
| dc.type.driver | doctoralThesis | - |
| dc.type.status | info:eu-repo/semantics/publishedVersion | de_DE |
| dc.type.thesis | doctoralThesis | de_DE |
| tuhh.type.opus | Dissertation | - |
| thesis.grantor.department | Informatik | de_DE |
| thesis.grantor.place | Hamburg | - |
| thesis.grantor.universityOrInstitution | Universität Hamburg | de_DE |
| dcterms.DCMIType | Text | - |
| dc.identifier.urn | urn:nbn:de:gbv:18-ediss-93786 | - |
| item.advisorGND | Frintrop, Simone | - |
| item.advisorGND | Zhang, Jianwei | - |
| item.grantfulltext | open | - |
| item.languageiso639-1 | other | - |
| item.fulltext | With Fulltext | - |
| item.creatorOrcid | Gao, Ge | - |
| item.creatorGND | Gao, Ge | - |
| Enthalten in den Sammlungen: | Elektronische Dissertationen und Habilitationen | |
Dateien zu dieser Ressource:
| Datei | Beschreibung | Prüfsumme | Größe | Format | |
|---|---|---|---|---|---|
| Gao_PhD_thesis_final_02_07_2021_compressed.pdf | PhD dissertation Ge Gao | 964a1248a116abfdbdbe39c6c95e84da | 13.39 MB | Adobe PDF | Öffnen/Anzeigen |
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