28 citations · 81 across the 10 of their papers we have counts for
5 papers · 1 filter
Label-Efficient LiDAR Panoptic Segmentation
Ahmet Selim Çanakçı, Niclas Vödisch, Kürsat Petek +2
A main bottleneck of learning-based robotic scene understanding methods is the heavy reliance on extensive annotated training data, which often limits their generalization ability.…
LetsMap: Unsupervised Representation Learning for Semantic BEV Mapping
Nikhil Gosala, Kürsat Petek, B Ravi Kiran +4
Semantic Bird's Eye View (BEV) maps offer a rich representation with strong occlusion reasoning for various decision making tasks in autonomous driving. However, most BEV mapping a…
Panoptic Out-of-Distribution Segmentation
Rohit Mohan, Kiran Kumaraswamy, Juana Valeria Hurtado +2
Deep learning has led to remarkable strides in scene understanding with panoptic segmentation emerging as a key holistic scene interpretation task. However, the performance of pano…
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…
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…