A comparative study for 6D pose estimation of textureless and symmetric objects used in automotive manufacturing industry

Name: A comparative study for 6D pose estimation of textureless and symmetric objects used in automotive manufacturing industry
Author: Doruk, Abdullah Enes, Ozkaya, T. E., Gülez, F., Uslu, F.

İsim	A comparative study for 6D pose estimation of textureless and symmetric objects used in automotive manufacturing industry
Yazar	Doruk, Abdullah Enes, Ozkaya, T. E., Gülez, F., Uslu, F.
Basım Tarihi:	2023
Basım Yeri	- IEEE
Konu	6D pose estimation, Deep networks, Domain randomization, Synthetic data
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	979-835033752-5
Kayıt Numarası	9da85496-74e5-47ad-b8e3-85d18e6e7492
Tarih	2023
Notlar	TÜBİTAK
Örnek Metin	6D 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.
DOI	10.1109/HORA58378.2023.10156677

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A comparative study for 6D pose estimation of textureless and symmetric objects used in automotive manufacturing industry

Yazar Doruk, Abdullah Enes, Ozkaya, T. E., Gülez, F., Uslu, F.

Basım Tarihi 2023

Basım Yeri - IEEE

Konu 6D pose estimation, Deep networks, Domain randomization, Synthetic data

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 979-835033752-5

Kayıt Numarası 9da85496-74e5-47ad-b8e3-85d18e6e7492

Tarih 2023

Notlar TÜBİTAK

Örnek Metin 6D 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.

DOI 10.1109/HORA58378.2023.10156677