Gorschlüter, Felix ; Rojtberg, Pavel ; Pöllabauer, Thomas (2022):
A Survey of 6D Object Detection Based on 3D Models for Industrial Applications. (Publisher's Version)
In: Journal of Imaging, 8 (3), MDPI, e-ISSN 2313-433X,
DOI: 10.26083/tuprints-00021027,
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Item Type: | Article |
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Origin: | Secondary publication DeepGreen |
Status: | Publisher's Version |
Title: | A Survey of 6D Object Detection Based on 3D Models for Industrial Applications |
Language: | English |
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. |
Journal or Publication Title: | Journal of Imaging |
Volume of the journal: | 8 |
Issue Number: | 3 |
Place of Publication: | Darmstadt |
Publisher: | MDPI |
Collation: | 18 Seiten |
Uncontrolled Keywords: | object detection, pose estimation, machine learning, neural networks, synthetic training, RGBD |
Classification DDC: | 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften und Maschinenbau |
Divisions: | 20 Department of Computer Science > Interactive Graphics Systems 20 Department of Computer Science > Fraunhofer IGD |
Date Deposited: | 11 Apr 2022 11:29 |
Last Modified: | 27 Oct 2022 05:50 |
DOI: | 10.26083/tuprints-00021027 |
Corresponding Links: | |
URN: | urn:nbn:de:tuda-tuprints-210272 |
SWORD Depositor: | Deep Green |
URI: | https://tuprints.ulb.tu-darmstadt.de/id/eprint/21027 |
PPN: | 50077210X |
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