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Found 5 papers, 4 papers with code
Combining Shape Completion and Grasp Prediction for Fast and Versatile Grasping with a Multi-Fingered Hand
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.
Shape Completion with Prediction of Uncertain Regions
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.
Learning to Localize in New Environments from Synthetic Training Data
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.
CTC-Segmentation of Large Corpora for German End-to-end Speech Recognition
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
BlenderProc
BlenderProc is a modular procedural pipeline, which helps in generating real looking images for the training of convolutional neural networks.
