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A Survey of 6D Object Detection Based on 3D Models for Industrial Applications

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
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
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
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: 11 Apr 2022 11:29
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
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