TY  - CHAP
A1  - Engemann, Heiko
A1  - Du, Shengzhi
A1  - Kallweit, Stephan
A1  - Ning, Chuanfang
A1  - Anwar, Saqib
T1  - AutoSynPose: Automatic Generation of Synthetic Datasets for 6D Object Pose Estimation
T2  - Machine Learning and Artificial Intelligence. Proceedings of MLIS 2020
N2  - We present an automated pipeline for the generation of synthetic datasets for six-dimension (6D) object pose estimation. Therefore, a completely automated generation process based on predefined settings is developed, which enables the user to create large datasets with a minimum of interaction and which is feasible for applications with a high object variance. The pipeline is based on the Unreal 4 (UE4) game engine and provides a high variation for domain randomization, such as object appearance, ambient lighting, camera-object transformation and distractor density. In addition to the object pose and bounding box, the metadata includes all randomization parameters, which enables further studies on randomization parameter tuning. The developed workflow is adaptable to other 3D objects and UE4 environments. An exemplary dataset is provided including five objects of the Yale-CMU-Berkeley (YCB) object set. The datasets consist of 6 million subsegments using 97 rendering locations in 12 different UE4 environments. Each dataset subsegment includes one RGB image, one depth image and one class segmentation image at pixel-level.
Y1  - 2020
UR  - https://opus.bibliothek.fh-aachen.de/opus4/frontdoor/index/index/docId/9508
SN  - 978-1-64368-137-5
N1  - Frontiers in Artificial Intelligence and Applications. Vol 332
SP  - 89
EP  - 97
PB  - IOS Press
CY  - Amsterdam
ER  -