28 citations · 81 across the 10 of their papers we have counts for
5 papers · 1 filter
A Good Foundation is Worth Many Labels: Label-Efficient Panoptic Segmentation
Niclas Vödisch, Kürsat Petek, Markus Käppeler +2
A key challenge for the widespread application of learning-based models for robotic perception is to significantly reduce the required amount of annotated training data while achie…
Automatic Target-Less Camera-LiDAR Calibration From Motion and Deep Point Correspondences
Kürsat Petek, Niclas Vödisch, Johannes Meyer +3
Sensor setups of robotic platforms commonly include both camera and LiDAR as they provide complementary information. However, fusing these two modalities typically requires a highl…
BEVCar: Camera-Radar Fusion for BEV Map and Object Segmentation
Jonas Schramm, Niclas Vödisch, Kürsat Petek +4
Semantic scene segmentation from a bird's-eye-view (BEV) perspective plays a crucial role in facilitating planning and decision-making for mobile robots. Although recent vision-onl…
CoDEPS: Online Continual Learning for Depth Estimation and Panoptic Segmentation
Niclas Vödisch, Kürsat Petek, Wolfram Burgard +1
Operating a robot in the open world requires a high level of robustness with respect to previously unseen environments. Optimally, the robot is able to adapt by itself to new condi…
Robust Monocular Localization in Sparse HD Maps Leveraging Multi-Task Uncertainty Estimation
Kürsat Petek, Kshitij Sirohi, Daniel Büscher +1
Robust localization in dense urban scenarios using a low-cost sensor setup and sparse HD maps is highly relevant for the current advances in autonomous driving, but remains a chall…