no code implementations • 31 Aug 2022 • Larissa T. Triess, Christoph B. Rist, David Peter, J. Marius Zöllner
In a series of experiments, we demonstrate the application of our metric to determine the realism of generated LiDAR data and compare the realism estimation of our metric to the performance of a segmentation model.
no code implementations • 17 Feb 2022 • Larissa T. Triess, Andre Bühler, David Peter, Fabian B. Flohr, J. Marius Zöllner
Generative models can be used to synthesize 3D objects of high quality and diversity.
no code implementations • NeurIPS Workshop ICBINB 2021 • Larissa T. Triess, David Peter, J. Marius Zöllner
A number of applications, such as mobile robots or automated vehicles, use LiDAR sensors to obtain detailed information about their three-dimensional surroundings.
no code implementations • 24 Sep 2021 • Larissa T. Triess, David Peter, Stefan A. Baur, J. Marius Zöllner
In a series of experiments, we confirm the soundness of our metric by applying it in controllable task setups and on unseen data.
no code implementations • 4 Jun 2021 • Larissa T. Triess, Mariella Dreissig, Christoph B. Rist, J. Marius Zöllner
Scalable systems for automated driving have to reliably cope with an open-world setting.
no code implementations • 6 Apr 2020 • Larissa T. Triess, David Peter, Christoph B. Rist, J. Marius Zöllner
Autonomous vehicles need to have a semantic understanding of the three-dimensional world around them in order to reason about their environment.
no code implementations • 28 Jun 2019 • Larissa T. Triess, David Peter, Christoph B. Rist, Markus Enzweiler, J. Marius Zöllner
This paper presents a novel CNN-based approach for synthesizing high-resolution LiDAR point cloud data.