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Investigations on Output Parameterizations of Neural Networks for Single Shot 6D Object Pose Estimation

: Kleeberger, Kilian; Völk, Markus; Bormann, Richard; Huber, Marco


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Robotics and Automation Society:
IEEE International Conference on Robotics and Automation, ICRA 2021 : May 30 - June 5, 2021, Xi'an, China, (Hybrid Event)
Piscataway, NJ: IEEE, 2021
ISBN: 978-1-7281-9078-5
ISBN: 978-1-7281-9077-8
International Conference on Robotics and Automation (ICRA) <2021, Xian; Online>
Conference Paper
Fraunhofer IPA ()
neuronales Netz; Bildverarbeitung; Robotik; Greifen

Single shot approaches have demonstrated tremendous success on various computer vision tasks. Finding good parameterizations for 6D object pose estimation remains an open challenge. In this work, we propose different novel parameterizations for the output of the neural network for single shot 6D object pose estimation. Our learning-based approach achieves state-of-the-art performance on two public benchmark datasets. Furthermore, we demonstrate that the pose estimates can be used for real-world robotic grasping tasks without additional ICP refinement.