2 citations · 3 across the 8 of their papers we have counts for
10 papers · 1 filter
XD-MAP: Cross-Modal Domain Adaptation via Semantic Parametric Maps for Scalable Training Data Generation
Frank Bieder, Hendrik Königshof, Haohao Hu +4
Until open-world foundation models match the performance of specialized approaches, deep learning systems remain dependent on task- and sensor-specific data availability. To bridge…
SDTagNet: Leveraging Text-Annotated Navigation Maps for Online HD Map Construction
Fabian Immel, Jan-Hendrik Pauls, Richard Fehler +3
Autonomous vehicles rely on detailed and accurate environmental information to operate safely. High definition (HD) maps offer a promising solution, but their high maintenance cost…
M3TR: A Generalist Model for Real-World HD Map Completion
Fabian Immel, Richard Fehler, Frank Bieder +2
Autonomous vehicles rely on HD maps for their operation, but offline HD maps eventually become outdated. For this reason, online HD map construction methods use live sensor data to…
Mapping LiDAR and Camera Measurements in a Dual Top-View Grid Representation Tailored for Automated Vehicles
Sven Richter, Frank Bieder, Sascha Wirges +1
We present a generic evidential grid mapping pipeline designed for imaging sensors such as LiDARs and cameras. Our grid-based evidential model contains semantic estimates for cell…
Sensor Data Fusion in Top-View Grid Maps using Evidential Reasoning with Advanced Conflict Resolution
Sven Richter, Frank Bieder, Sascha Wirges +1
We present a new method to combine evidential top-view grid maps estimated based on heterogeneous sensor sources. Dempster's combination rule that is usually applied in this contex…
Improving Lidar-Based Semantic Segmentation of Top-View Grid Maps by Learning Features in Complementary Representations
Frank Bieder, Maximilian Link, Simon Romanski +2
In this paper we introduce a novel way to predict semantic information from sparse, single-shot LiDAR measurements in the context of autonomous driving. In particular, we fuse lear…