4 citations · 6 across the 3 of their papers we have counts for
4 papers
3DMOTFormer: Graph Transformer for Online 3D Multi-Object Tracking
Shuxiao Ding, Eike Rehder, Lukas Schneider +2
Tracking 3D objects accurately and consistently is crucial for autonomous vehicles, enabling more reliable downstream tasks such as trajectory prediction and motion planning. Based…
Cooperative Motion Planning for Non-Holonomic Agents with Value Iteration Networks
Eike Rehder, Maximilian Naumann, Niels Ole Salscheider +1
Cooperative motion planning is still a challenging task for robots. Recently, Value Iteration Networks (VINs) were proposed to model motion planning tasks as Neural Networks. In th…
Pedestrian Prediction by Planning using Deep Neural Networks
Eike Rehder, Florian Wirth, Martin Lauer +1
Accurate traffic participant prediction is the prerequisite for collision avoidance of autonomous vehicles. In this work, we predict pedestrians by emulating their own motion plann…
Guided Depth Upsampling for Precise Mapping of Urban Environments
Sascha Wirges, Björn Roxin, Eike Rehder +2
We present an improved model for MRF-based depth upsampling, guided by image- as well as 3D surface normal features. By exploiting the underlying camera model we define a novel reg…