Durner, Maximilian und Boerdijk, Wout und Sundermeyer, Martin und Friedl, Werner und Marton, Zoltan-Csaba und Triebel, Rudolph (2021) Unknown Object Segmentation from Stereo Images. In: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021. International Conference on Intelligent Robots and Systems, 2021-09-27 - 2021-10-01, Prague (online). doi: 10.1109/IROS51168.2021.9636281. ISBN 978-166541714-3. ISSN 2153-0858.
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Kurzfassung
Although instance-aware perception is a key prerequisite for many autonomous robotic applications, most of the methods only partially solve the problem by focusing solely on known object categories. However, for robots interacting in dynamic and cluttered environments, this is not realistic and severely limits the range of potential applications. Therefore, we propose a novel object instance segmentation approach that does not require any semantic or geometric information of the objects beforehand. In contrast to existing works, we do not explicitly use depth data as input, but rely on the insight that slight viewpoint changes, which for example are provided by stereo image pairs, are often sufficient to determine object boundaries and thus to segment objects. Focusing on the versatility of stereo sensors, we employ a transformer-based architecture that maps directly from the pair of input images to the object instances. This has the major advantage that instead of a noisy, and potentially incomplete depth map as an input, on which the segmentation is computed, we use the original image pair to infer the object instances and a dense depth map. In experiments in several different application domains, we show that our Instance Stereo Transformer (INSTR) algorithm outperforms current state-of-the-art methods that are based on depth maps. Training code and pretrained models are available at https://github.com/DLR-RM/instr
elib-URL des Eintrags: | https://elib.dlr.de/145858/ | ||||||||||||||||||||||||||||
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Dokumentart: | Konferenzbeitrag (Vortrag) | ||||||||||||||||||||||||||||
Titel: | Unknown Object Segmentation from Stereo Images | ||||||||||||||||||||||||||||
Autoren: |
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Datum: | 2021 | ||||||||||||||||||||||||||||
Erschienen in: | 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021 | ||||||||||||||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||||||||||||||
Open Access: | Ja | ||||||||||||||||||||||||||||
Gold Open Access: | Nein | ||||||||||||||||||||||||||||
In SCOPUS: | Ja | ||||||||||||||||||||||||||||
In ISI Web of Science: | Ja | ||||||||||||||||||||||||||||
DOI: | 10.1109/IROS51168.2021.9636281 | ||||||||||||||||||||||||||||
ISSN: | 2153-0858 | ||||||||||||||||||||||||||||
ISBN: | 978-166541714-3 | ||||||||||||||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||||||||||||||
Stichwörter: | instance segmentation unknown object segmentation stereo-vision | ||||||||||||||||||||||||||||
Veranstaltungstitel: | International Conference on Intelligent Robots and Systems | ||||||||||||||||||||||||||||
Veranstaltungsort: | Prague (online) | ||||||||||||||||||||||||||||
Veranstaltungsart: | internationale Konferenz | ||||||||||||||||||||||||||||
Veranstaltungsbeginn: | 27 September 2021 | ||||||||||||||||||||||||||||
Veranstaltungsende: | 1 Oktober 2021 | ||||||||||||||||||||||||||||
HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||||||||||||||||||||||
HGF - Programm: | Raumfahrt | ||||||||||||||||||||||||||||
HGF - Programmthema: | Robotik | ||||||||||||||||||||||||||||
DLR - Schwerpunkt: | Raumfahrt | ||||||||||||||||||||||||||||
DLR - Forschungsgebiet: | R RO - Robotik | ||||||||||||||||||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | R - Multisensorielle Weltmodellierung (RM) [RO] | ||||||||||||||||||||||||||||
Standort: | Oberpfaffenhofen | ||||||||||||||||||||||||||||
Institute & Einrichtungen: | Institut für Robotik und Mechatronik (ab 2013) > Perzeption und Kognition Institut für Robotik und Mechatronik (ab 2013) | ||||||||||||||||||||||||||||
Hinterlegt von: | Durner, Maximilian | ||||||||||||||||||||||||||||
Hinterlegt am: | 22 Nov 2021 09:55 | ||||||||||||||||||||||||||||
Letzte Änderung: | 24 Apr 2024 20:44 |
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