Sundermeyer, Martin and Durner, Maximilian and Puang, En Yen and Marton, Zoltan-Csaba and Vaskevicius, Narunas and Kai, O. Arras and Triebel, Rudolph (2020) Multi-Path Learning for Object Pose Estimation Across Domains. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, pp. 13916-13925. IEEE. IEEE Conference on Computer Vision and Pattern Recognition, 2020-06-14 - 2020-06-19, Seattle, USA. doi: 10.1109/CVPR42600.2020.01393. ISBN 978-172817168-5. ISSN 1063-6919.
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Abstract
We introduce a scalable approach for object pose estima-tion trained on simulated RGB views of multiple 3D modelstogether. We learn an encoding of object views that doesnot only describe an implicit orientation of all objects seenduring training, but can also relate views of untrained ob-jects. Our single-encoder-multi-decoder network is trainedusing a technique we denote multi-path learning: Whilethe encoder is shared by all objects, each decoder only re-constructs views of a single object. Consequently, viewsof different instances do not have to be separated in thelatent space and can share common features. The result-ing encoder generalizes well from synthetic to real dataand across various instances, categories, model types anddatasets. We systematically investigate the learned encod-ings, their generalization, and iterative refinement strate-gies on the ModelNet40 and T-LESS dataset. Despite train-ing jointly on multiple objects, our 6D Object Detectionpipeline achieves state-of-the-art results on T-LESS at muchlower runtimes than competing approaches.
Item URL in elib: | https://elib.dlr.de/135550/ | ||||||||||||||||||||||||||||||||
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Document Type: | Conference or Workshop Item (Poster) | ||||||||||||||||||||||||||||||||
Title: | Multi-Path Learning for Object Pose Estimation Across Domains | ||||||||||||||||||||||||||||||||
Authors: |
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Date: | June 2020 | ||||||||||||||||||||||||||||||||
Journal or Publication Title: | 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020 | ||||||||||||||||||||||||||||||||
Refereed publication: | Yes | ||||||||||||||||||||||||||||||||
Open Access: | Yes | ||||||||||||||||||||||||||||||||
Gold Open Access: | No | ||||||||||||||||||||||||||||||||
In SCOPUS: | Yes | ||||||||||||||||||||||||||||||||
In ISI Web of Science: | Yes | ||||||||||||||||||||||||||||||||
DOI: | 10.1109/CVPR42600.2020.01393 | ||||||||||||||||||||||||||||||||
Page Range: | pp. 13916-13925 | ||||||||||||||||||||||||||||||||
Publisher: | IEEE | ||||||||||||||||||||||||||||||||
ISSN: | 1063-6919 | ||||||||||||||||||||||||||||||||
ISBN: | 978-172817168-5 | ||||||||||||||||||||||||||||||||
Status: | Published | ||||||||||||||||||||||||||||||||
Keywords: | Object Pose Estimation, Encodings, Multi Object, Synthetic Data, Symmetries, Autoencoder, Embedding, 6D Object Detection, T-LESS, Relative Pose Estimation | ||||||||||||||||||||||||||||||||
Event Title: | IEEE Conference on Computer Vision and Pattern Recognition | ||||||||||||||||||||||||||||||||
Event Location: | Seattle, USA | ||||||||||||||||||||||||||||||||
Event Type: | international Conference | ||||||||||||||||||||||||||||||||
Event Start Date: | 14 June 2020 | ||||||||||||||||||||||||||||||||
Event End Date: | 19 June 2020 | ||||||||||||||||||||||||||||||||
HGF - Research field: | Aeronautics, Space and Transport | ||||||||||||||||||||||||||||||||
HGF - Program: | Space | ||||||||||||||||||||||||||||||||
HGF - Program Themes: | Space System Technology | ||||||||||||||||||||||||||||||||
DLR - Research area: | Raumfahrt | ||||||||||||||||||||||||||||||||
DLR - Program: | R SY - Space System Technology | ||||||||||||||||||||||||||||||||
DLR - Research theme (Project): | R - Vorhaben Multisensorielle Weltmodellierung (old) | ||||||||||||||||||||||||||||||||
Location: | Oberpfaffenhofen | ||||||||||||||||||||||||||||||||
Institutes and Institutions: | Institute of Robotics and Mechatronics (since 2013) > Perception and Cognition | ||||||||||||||||||||||||||||||||
Deposited By: | Sundermeyer, Martin | ||||||||||||||||||||||||||||||||
Deposited On: | 22 Jul 2020 18:48 | ||||||||||||||||||||||||||||||||
Last Modified: | 04 Jun 2024 15:06 |
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