Deep Learning Based 6D Pose Estimation of Unknown Objects
- 6D pose estimation of unknown objects is an unsolved research problem in computer vision. This contribution proposes a novel method for estimating an object's pose using a monocular camera and an artificially created point cloud from the geometric object data. Point cloud generation and pose estimation are done with deep learning methods, combining several neural network-based approaches6D pose estimation of unknown objects is an unsolved research problem in computer vision. This contribution proposes a novel method for estimating an object's pose using a monocular camera and an artificially created point cloud from the geometric object data. Point cloud generation and pose estimation are done with deep learning methods, combining several neural network-based approaches in one system. This paper describes the architecture and methods and compares and evaluates several ideas.…
| Dokumentart: | Preprint |
|---|---|
| Zitierlink: | https://opus.hs-offenburg.de/12038 | Bibliografische Angaben |
| Titel (Englisch): | Deep Learning Based 6D Pose Estimation of Unknown Objects |
| Verfasserangaben: | Stefan HenselStaff MemberORCiDGND, Marin B. MarinovORCiD, Jeremy Fischer |
| Erscheinungsjahr: | 2025 |
| Verlag: | MDPI AG |
| Seitenanzahl: | 20 |
| Erste Seite: | 1 |
| Letzte Seite: | 20 |
| Titel des übergeordneten Werkes (Englisch): | Preprints.org |
| DOI: | https://doi.org/10.20944/preprints202502.1483.v1 |
| Sprache: | Englisch | Inhaltliche Informationen |
| Fakultäten / Einrichtungen: | Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019) |
| Sammlungen der Hochschule Offenburg: | Bibliografie |
| Freies Schlagwort / Tag: | 3D object detection | Formale Angaben |
| Relevanz für "Jahresbericht über Forschungsleistungen": | Keine Relevanz |
| Open-Access-Status: | Open Access |
| Diamond | |
| Lizenz (Deutsch): |  Creative Commons - CC BY - Namensnennung 4.0 International |