4 code implementations • 25 Oct 2019 • Maximilian Denninger, Martin Sundermeyer, Dominik Winkelbauer, Youssef Zidan, Dmitry Olefir, Mohamad Elbadrawy, Ahsan Lodhi, Harinandan Katam
BlenderProc is a modular procedural pipeline, which helps in generating real looking images for the training of convolutional neural networks.
11 code implementations • 17 Jul 2020 • Ludwig Kürzinger, Dominik Winkelbauer, Lujun Li, Tobias Watzel, Gerhard Rigoll
In this work, we combine freely available corpora for German speech recognition, including yet unlabeled speech data, to a big dataset of over $1700$h of speech data.
Ranked #5 on Speech Recognition on TUDA (using extra training data)
Speech Recognition Audio and Speech Processing
1 code implementation • 9 Nov 2020 • Dominik Winkelbauer, Maximilian Denninger, Rudolph Triebel
Our approach outperforms the 5-point algorithm using SIFT features on equally big images and additionally surpasses all previous learning-based approaches that were trained on different data.
1 code implementation • 1 Aug 2023 • Matthias Humt, Dominik Winkelbauer, Ulrich Hillenbrand
We train on this dataset and test each method in shape completion and prediction of uncertain regions for known and novel object instances and on synthetic and real data.
no code implementations • 31 Oct 2023 • Matthias Humt, Dominik Winkelbauer, Ulrich Hillenbrand, Berthold Bäuml
We present a novel, fast, and high fidelity deep learning pipeline consisting of a shape completion module that is based on a single depth image, and followed by a grasp predictor that is based on the predicted object shape.