Gorschlüter, Felix ; Rojtberg, Pavel ; Pöllabauer, Thomas (2022)
A Survey of 6D Object Detection Based on 3D Models for Industrial Applications.
In: Journal of Imaging, 2022, 8 (3)
doi: 10.26083/tuprints-00021027
Artikel, Zweitveröffentlichung, Verlagsversion
Kurzbeschreibung (Abstract)
Six-dimensional object detection of rigid objects is a problem especially relevant for quality control and robotic manipulation in industrial contexts. This work is a survey of the state of the art of 6D object detection with these use cases in mind, specifically focusing on algorithms trained only with 3D models or renderings thereof. Our first contribution is a listing of requirements typically encountered in industrial applications. The second contribution is a collection of quantitative evaluation results for several different 6D object detection methods trained with synthetic data and the comparison and analysis thereof. We identify the top methods for individual requirements that industrial applications have for object detectors, but find that a lack of comparable data prevents large-scale comparison over multiple aspects.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Gorschlüter, Felix ; Rojtberg, Pavel ; Pöllabauer, Thomas |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | A Survey of 6D Object Detection Based on 3D Models for Industrial Applications |
| Sprache: | Englisch |
| Publikationsjahr: | 2022 |
| Publikationsdatum der Erstveröffentlichung: | 2022 |
| Verlag: | MDPI |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Journal of Imaging |
| Jahrgang/Volume einer Zeitschrift: | 8 |
| (Heft-)Nummer: | 3 |
| Kollation: | 18 Seiten |
| DOI: | 10.26083/tuprints-00021027 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/21027 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichung DeepGreen |
| Kurzbeschreibung (Abstract): | Six-dimensional object detection of rigid objects is a problem especially relevant for quality control and robotic manipulation in industrial contexts. This work is a survey of the state of the art of 6D object detection with these use cases in mind, specifically focusing on algorithms trained only with 3D models or renderings thereof. Our first contribution is a listing of requirements typically encountered in industrial applications. The second contribution is a collection of quantitative evaluation results for several different 6D object detection methods trained with synthetic data and the comparison and analysis thereof. We identify the top methods for individual requirements that industrial applications have for object detectors, but find that a lack of comparable data prevents large-scale comparison over multiple aspects. |
| Freie Schlagworte: | object detection, pose estimation, machine learning, neural networks, synthetic training, RGBD |
| Status: | Verlagsversion |
| URN: | urn:nbn:de:tuda-tuprints-210272 |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften und Maschinenbau |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme 20 Fachbereich Informatik > Fraunhofer IGD |
| Hinterlegungsdatum: | 11 Apr 2022 11:29 |
| Letzte Änderung: | 12 Apr 2022 09:43 |
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| Zugehörige Links: | |
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| Suche nach Titel in: | TUfind oder in Google |
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