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Document type:
Forschungsdaten
Publication date:
15.02.2023
Responsible:
Cao, Hongpeng
Authors:
Cao*, Hongpeng ; Dirnberger*, Lukas ; Bernardini, Daniele; Piazza, Cristina; Caccamo, Marco
Author affiliation:
TUM
Publisher:
TUM
Title:
Synthetic training dataset for LineMod objects
Identifier:
doi:10.14459/2023mp1695465
End date of data production:
12.01.2023
Subject area:
DAT Datenverarbeitung, Informatik; FER Fertigungstechnik
Other subject areas:
Robotics
Resource type:
Simulationen / simulations
Data type:
Bilder / images; mehrdimensionale Visualisierungen oder Modelle / models; Texte / texts
Description:
The dataset includes the synthetic data generated from rendering the 3D meshes of LM objects in Blender for training 6D pose estimation algorithms. The whole dataset contains the synthetic data for 13 objects with 20,000 data samples for each object. Each data sample includes an RGB image in .png format and a depth image in .exr format. Each sample has the annotations of mask labels in .png format and the ground truth pose labels saved in .json files. Apart from the training data, the 3D meshes...     »
Method of data assessment:
The dataset is generated by rendering the 3D meshes of objects in the simulation Blender. The generated data will be further processed in python for data augmentation, in which the domain randomization techniques are applied to increase variaty. Along with the geneated anotations, the data will be used for training 6D pose estimation algorithms in Tensorflow.
Links:

Additional information: https://doi.org/10.48550/arXiv.2208.14288

This dataset relates to the publication: https://doi.org/10.3389/frobt.2023.1176492
Key words:
6D pose estimation; Synthetic dataset; Computer Vision
Technical remarks:
View and download (1,2 TB total, 1600313 Files)
The data server also offers downloads with FTP
The data server also offers downloads with rsync (password m1695465):
rsync rsync://m1695465@dataserv.ub.tum.de/m1695465/
Language:
en
Rights:
by, http://creativecommons.org/licenses/by/4.0
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