911 citations · 1.1k across the 34 of their papers we have counts for
23 papers · 1 filter
Few-Shot Panoptic Segmentation With Foundation Models
Markus Käppeler, Kürsat Petek, Niclas Vödisch +2
Current state-of-the-art methods for panoptic segmentation require an immense amount of annotated training data that is both arduous and expensive to obtain posing a significant ch…
AutoGraph: Predicting Lane Graphs from Traffic Observations
Jannik Zürn, Ingmar Posner, Wolfram Burgard
Lane graph estimation is a long-standing problem in the context of autonomous driving. Previous works aimed at solving this problem by relying on large-scale, hand-annotated lane g…
Learning and Aggregating Lane Graphs for Urban Automated Driving
Martin Büchner, Jannik Zürn, Ion-George Todoran +2
Lane graph estimation is an essential and highly challenging task in automated driving and HD map learning. Existing methods using either onboard or aerial imagery struggle with co…
EvCenterNet: Uncertainty Estimation for Object Detection using Evidential Learning
Monish R. Nallapareddy, Kshitij Sirohi, Paulo L. J. Drews-Jr +3
Uncertainty estimation is crucial in safety-critical settings such as automated driving as it provides valuable information for several downstream tasks including high-level decisi…
SkyEye: Self-Supervised Bird's-Eye-View Semantic Mapping Using Monocular Frontal View Images
Nikhil Gosala, Kürsat Petek, Paulo L. J. Drews-Jr +2
Bird's-Eye-View (BEV) semantic maps have become an essential component of automated driving pipelines due to the rich representation they provide for decision-making tasks. However…
Uncertainty-aware LiDAR Panoptic Segmentation
Kshitij Sirohi, Sajad Marvi, Daniel Büscher +1
Modern autonomous systems often rely on LiDAR scanners, in particular for autonomous driving scenarios. In this context, reliable scene understanding is indispensable. Current lear…