1 code implementation • 13 Jun 2023 • Tobias Riedlinger, Marius Schubert, Sarina Penquitt, Jan-Marcel Kezmann, Pascal Colling, Karsten Kahl, Lutz Roese-Koerner, Michael Arnold, Urs Zimmermann, Matthias Rottmann
In order to address these two issues, we propose LidarMetaDetect (LMD), a light-weight post-processing scheme for prediction quality estimation.
no code implementations • 29 Oct 2021 • Pascal Colling, Matthias Rottmann, Lutz Roese-Koerner, Hanno Gottschalk
We present a novel post-processing tool for semantic segmentation of LiDAR point cloud data, called LidarMetaSeg, which estimates the prediction quality segmentwise.
no code implementations • 5 Oct 2020 • Pascal Colling, Lutz Roese-Koerner, Hanno Gottschalk, Matthias Rottmann
The comparison and analysis of the results provide insights into annotation costs as well as robustness and variance of the methods.
no code implementations • 18 Jun 2020 • Lukas Hahn, Lutz Roese-Koerner, Peet Cremer, Urs Zimmermann, Ori Maoz, Anton Kummert
Active Learning is concerned with the question of how to identify the most useful samples for a Machine Learning algorithm to be trained with.
1 code implementation • 6 May 2019 • Lukas Hahn, Lutz Roese-Koerner, Klaus Friedrichs, Anton Kummert
The performance of a Convolutional Neural Network (CNN) depends on its hyperparameters, like the number of layers, kernel sizes, or the learning rate for example.
1 code implementation • 19 Jan 2018 • Ido Freeman, Lutz Roese-Koerner, Anton Kummert
With the ever increasing application of Convolutional Neural Networks to customer products the need emerges for models to efficiently run on embedded, mobile hardware.