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Estimation of 6D Pose of Objects Based on a Variant Adversarial Autoencoder

Huang, Dan and Ahn, Hyemin and Li, Shile and Hu, Yueming and Lee, Dongheui (2023) Estimation of 6D Pose of Objects Based on a Variant Adversarial Autoencoder. Neural Processing Letters. Springer Nature. doi: 10.1007/s11063-023-11215-2. ISSN 1370-4621.

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Official URL: https://link.springer.com/article/10.1007/s11063-023-11215-2


The goal of this paper is to estimate objects 6D pose based on the texture-less dataset. The pose of each projection view is obtained by rendering the 3D model of each object, and then the orientation feature of the object is implicitly represented by the latent space obtained from the RGB image. The 3D rotation of the object is estimated by establishing the codebook based on a template matching architecture. To build the latent space from the RGB images, this paper proposes a network based on a variant Adversarial Autoencoder (Makhzani et al. in Computer Science, 2015). To train the network, we use the dataset without pose annotation, and the encoder and decoder do not have a structural symmetry. The encoder is inspired by the existing model (Yang et al. in proceedings of IJCAI, 2018), (Yang et al. in proceedings 11 of CVPR, 2019) that incorporates the function of feature extraction from two different streams. Based on this network, the latent feature vector that implicitly represents the orientation of the object is obtained from the RGB image. Experimental results show that the method in this paper can realize the 6D pose estimation of the object and the result accuracy is better than the advanced method (Sundermeyer et al. in proceedings of ECCV, 2018).

Item URL in elib:https://elib.dlr.de/194566/
Document Type:Article
Title:Estimation of 6D Pose of Objects Based on a Variant Adversarial Autoencoder
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Lee, DongheuiUNSPECIFIEDhttps://orcid.org/0000-0003-1897-7664UNSPECIFIED
Date:14 March 2023
Journal or Publication Title:Neural Processing Letters
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:Yes
Publisher:Springer Nature
Keywords:Robotics, orientation, RGB image
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Robotics
DLR - Research area:Raumfahrt
DLR - Program:R RO - Robotics
DLR - Research theme (Project):R - Autonomous learning robots [RO]
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Robotics and Mechatronics (since 2013) > Management
Deposited By: Geyer, Günther
Deposited On:31 Mar 2023 12:56
Last Modified:31 Mar 2023 12:56

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