no code implementations • ICCV 2021 • Stefan Andreas Baur, David Josef Emmerichs, Frank Moosmann, Peter Pinggera, Bjorn Ommer, Andreas Geiger
Recently, several frameworks for self-supervised learning of 3D scene flow on point clouds have emerged.
no code implementations • 3 Jul 2019 • Florian Piewak, Peter Pinggera, Marius Zöllner
In this paper we propose a new CNN architecture for the point-wise semantic labeling of LiDAR data which achieves state-of-the-art results while increasing portability across sensor types.
no code implementations • 24 Sep 2018 • Florian Piewak, Peter Pinggera, Markus Enzweiler, David Pfeiffer, Marius Zöllner
Our results indicate that the proposed mid-level fusion of LiDAR and camera data improves both the geometric and semantic accuracy of the Stixel model significantly while reducing the computational overhead as well as the amount of generated data in comparison to using a single modality on its own.
no code implementations • 26 Apr 2018 • Florian Piewak, Peter Pinggera, Manuel Schäfer, David Peter, Beate Schwarz, Nick Schneider, David Pfeiffer, Markus Enzweiler, Marius Zöllner
The effectiveness of the proposed network architecture as well as the automated data generation process is demonstrated on a manually annotated ground truth dataset.
no code implementations • 20 Dec 2016 • Sebastian Ramos, Stefan Gehrig, Peter Pinggera, Uwe Franke, Carsten Rother
To utilize the appearance and contextual cues, we propose a new deep learning-based obstacle detection framework.
no code implementations • 15 Sep 2016 • Peter Pinggera, Sebastian Ramos, Stefan Gehrig, Uwe Franke, Carsten Rother, Rudolf Mester
The proposed approach outperforms all considered baselines in our evaluations on both pixel and object level and runs at frame rates of up to 20 Hz on 2 mega-pixel stereo imagery.
no code implementations • 2 Aug 2016 • Nick Schneider, Lukas Schneider, Peter Pinggera, Uwe Franke, Marc Pollefeys, Christoph Stiller
We present a novel method for accurate and efficient up- sampling of sparse depth data, guided by high-resolution imagery.