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Multi-Path Learning for Object Pose Estimation Across Domains

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: IEEE Conference on Computer Vision and Pattern Recognition. IEEE Conference on Computer Vision and Pattern Recognition, Seattle, USA.

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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/
Document Type:Conference or Workshop Item (Poster)
Title:Multi-Path Learning for Object Pose Estimation Across Domains
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Sundermeyer, Martinmartin.sundermeyer (at) dlr.dehttps://orcid.org/0000-0003-0587-9643
Durner, MaximilianMaximilian.Durner (at) dlr.dehttps://orcid.org/0000-0001-8885-5334
Puang, En YenEn.Puang (at) dlr.deUNSPECIFIED
Marton, Zoltan-CsabaZoltan.Marton (at) dlr.dehttps://orcid.org/0000-0002-3035-493X
Vaskevicius, NarunasNarunas.Vaskevicius (at) de.bosch.comUNSPECIFIED
Kai, O. ArrasKaiOliver.Arras (at) de.bosch.comUNSPECIFIED
Triebel, RudolphRudolph.Triebel (at) dlr.dehttps://orcid.org/0000-0002-7975-036X
Date:June 2020
Journal or Publication Title:IEEE Conference on Computer Vision and Pattern Recognition
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:Yes
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
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:22 Jul 2020 18:48

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