Web: http://arxiv.org/abs/2103.09696

May 12, 2022, 1:10 a.m. | Paul Koch, Marian Schlüter, Serge Thill

cs.CV updates on arXiv.org arxiv.org

Recently developed deep neural networks achieved state-of-the-art results in
the subject of 6D object pose estimation for robot manipulation. However, those
supervised deep learning methods require expensive annotated training data.
Current methods for reducing those costs frequently use synthetic data from
simulations, but rely on expert knowledge and suffer from the "domain gap" when
shifting to the real world. Here, we present a proof of concept for a novel
approach of autonomously generating annotated training data for 6D object pose …

arxiv data training training data

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