|Phone:||+49 631 20575-3605|
René Schuster received a master degree in Computational Engineering from the Technical University of Darmstadt, where he was student assistant at the Visual Inference Group.
His master thesis for the Robert Bosch GmbH was about 3D vehicle detection based on motion and geometry.
In 2017 he joined the Augmented Vision Department of the German Research Center for Artifical Intelligence where he is currently focussing his research on Scene Flow algorithms for automotive applications.
Dense Scene Flow from Stereo Disparity and Optical FlowDense
Computer Science in Cars Symposium ACM Chapters Computer Science in Cars Symposium (CSCS-2018), September 13-14, Munich, Germany
SceneFlowFields: Dense Interpolation of Sparse Scene Flow Correspondences
IEEE Winter Conference on Applications of Computer Vision (WACV-18), March 12-14, Lake Tahoe, NV/CA, USA
Combining Stereo Disparity and Optical Flow for Basic Scene Flow
Commercial Vehicle Technology Symposium (CVT-18), March 13-15, Kaiserslautern, Germany
FlowFields++: Accurate Optical Flow Correspondences Meet Robust Interpolation
IEEE International Conference on Image Processing
Dynamic Risk Assessment for Vehicles of Higher Automation Levels by Deep Learning
International Workshop on Artificial Intelligence Safety Engineering (WAISE-2018), located at SAFECOMP 2018, September 18, Västerås, Sweden
Automated Scene Flow Data Generation for Training and Verification
ACM Computer Science in Cars Symposium (CSCS) ACM Computer Science in Cars Symposium (CSCS-2018), Munich, Germany
Towards Flow Estimation in Automotive Scenarios
Computer Science in Cars Symposium ACM Chapters Computer Science in Cars Symposium (CSCS-17), Computer Science in Cars Symposium 2017, July 6, Munich, Germany