Object 6 Degrees of Freedom Pose Estimation with Mask-R-CNN and Virtual Training

Pose estimation algorithms’ goal is to find the position and the orientation of an object in space, given only an image. This task may be complex, especially in an uncontrolled environment with several parameters that can vary, like the object texture, background or the lightning conditions. Most al...
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Autor*in:	Victor Pujolle [verfasserIn] Eiji Hayashi [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Pose estimation deep-learning keypoints localization instance segmentation virtual training factory automation

Übergeordnetes Werk:	In: Journal of Robotics, Networking and Artificial Life (JRNAL) - Atlantis Press, 2017, 7(2020), 4
Übergeordnetes Werk:	volume:7 ; year:2020 ; number:4

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.2991/jrnal.k.201215.008

Katalog-ID:	DOAJ00772876X

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author	Victor Pujolle
spellingShingle	Victor Pujolle misc Pose estimation misc deep-learning misc keypoints localization misc instance segmentation misc virtual training misc factory automation misc Technology misc T Object 6 Degrees of Freedom Pose Estimation with Mask-R-CNN and Virtual Training
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title	Object 6 Degrees of Freedom Pose Estimation with Mask-R-CNN and Virtual Training
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title_full	Object 6 Degrees of Freedom Pose Estimation with Mask-R-CNN and Virtual Training
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title_sort	object 6 degrees of freedom pose estimation with mask-r-cnn and virtual training
title_auth	Object 6 Degrees of Freedom Pose Estimation with Mask-R-CNN and Virtual Training
abstract	Pose estimation algorithms’ goal is to find the position and the orientation of an object in space, given only an image. This task may be complex, especially in an uncontrolled environment with several parameters that can vary, like the object texture, background or the lightning conditions. Most algorithms performing pose estimation use deep learning methods. However, it may be difficult to create dataset to train such kind of models. In this paper we developed a new algorithm robust to a high variability of conditions using instance segmentation of the image and trainable on a virtual dataset. This system performs semantic keypoints based pose estimation without considering background, lighting or texture changes on the object.
abstractGer	Pose estimation algorithms’ goal is to find the position and the orientation of an object in space, given only an image. This task may be complex, especially in an uncontrolled environment with several parameters that can vary, like the object texture, background or the lightning conditions. Most algorithms performing pose estimation use deep learning methods. However, it may be difficult to create dataset to train such kind of models. In this paper we developed a new algorithm robust to a high variability of conditions using instance segmentation of the image and trainable on a virtual dataset. This system performs semantic keypoints based pose estimation without considering background, lighting or texture changes on the object.
abstract_unstemmed	Pose estimation algorithms’ goal is to find the position and the orientation of an object in space, given only an image. This task may be complex, especially in an uncontrolled environment with several parameters that can vary, like the object texture, background or the lightning conditions. Most algorithms performing pose estimation use deep learning methods. However, it may be difficult to create dataset to train such kind of models. In this paper we developed a new algorithm robust to a high variability of conditions using instance segmentation of the image and trainable on a virtual dataset. This system performs semantic keypoints based pose estimation without considering background, lighting or texture changes on the object.
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title_short	Object 6 Degrees of Freedom Pose Estimation with Mask-R-CNN and Virtual Training
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