A Comparative Study for 6D Pose Estimation of Textureless and Symmetric Objects Used in Automotive Manufacturing Industry

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dc.contributor.authorDoruk, Abdullah Enes
dc.contributor.authorOzkaya, Tayyip Ensar
dc.contributor.authorGulmez, Furkan
dc.contributor.authorUslu, Fatmatulzehra
dc.date.accessioned2026-02-12T21:02:49Z
dc.date.available2026-02-12T21:02:49Z
dc.date.issued2023
dc.departmentBursa Teknik Üniversitesi
dc.description5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2023 -- 2023-06-08 through 2023-06-10 -- Istanbul -- 190025
dc.description.abstract6D pose estimation of industrial objects on RGB images has a high potential to accelerate the automation of robotic manipulations in the automotive manufacturing industry. Despite its high potential, this problem has not been adequately addressed in the computer vision community. Main factors leading to under investigation of this problem are industrial objects to be textureless, thin, and symmetrical, which hinder the automatic estimation of their poses from color images. Deep learning models have shown promising results for pose estimation of household objects thanks to availability of large datasets with labels. In contrast to many household objects, there are few datasets for industrial objects with limited representation capacity, which restricts the use of deep models in pose estimation of industrial objects. In this study, we examine the eligibility of deep models on 6D pose estimation of industrial objects used in the automotive manufacturing industry. For this aim, we compare the performance of three deep models, DeepIM, CosyPose, and EPOS. To meet the need for large training dataset of these models, we produce a large synthetic dataset from the CAD data of the industrial objects. We also collect a small real dataset for training and performance evaluation purposes. We find that CosyPose outperforms other methods with a large margin, by showing its potential to solve such a hard problem. We also observe that training models with both synthetic and real images yield the best results. © 2023 IEEE.
dc.description.sponsorshipTürkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK
dc.identifier.doi10.1109/HORA58378.2023.10156677
dc.identifier.isbn9798350337525
dc.identifier.scopus2-s2.0-85165641512
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/HORA58378.2023.10156677
dc.identifier.urihttps://hdl.handle.net/20.500.12885/6551
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofHORA 2023 - 2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Proceedings
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.snmzKA_Scopus_20260212
dc.subject6D pose estimation
dc.subjectdeep networks
dc.subjectdomain randomization
dc.subjectsynthetic data
dc.titleA Comparative Study for 6D Pose Estimation of Textureless and Symmetric Objects Used in Automotive Manufacturing Industry
dc.typeConference Object

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