1 citations · 1 across the 4 of their papers we have counts for
5 papers · 1 filter
GhostPoint: Self-Supervised Representation Learning by Hallucinating Occluded LiDAR Structure
Mohamed Abdelsamad, Bin Yang, Michael Ulrich +4
3D object detection from LiDAR point clouds is a core problem in autonomous driving. Recent advances in self-supervised learning (SSL) enable scalable pretraining and transfers wel…
ALOOD: Exploiting Language Representations for LiDAR-based Out-of-Distribution Object Detection
Michael Kösel, Marcel Schreiber, Michael Ulrich +2
LiDAR-based 3D object detection plays a critical role for reliable and safe autonomous driving systems. However, existing detectors often produce overly confident predictions for o…
Multi-Scale Neighborhood Occupancy Masked Autoencoder for Self-Supervised Learning in LiDAR Point Clouds
Mohamed Abdelsamad, Michael Ulrich, Claudius Gläser +1
Masked autoencoders (MAE) have shown tremendous potential for self-supervised learning (SSL) in vision and beyond. However, point clouds from LiDARs used in automated driving are p…
Bosch Street Dataset: A Multi-Modal Dataset with Imaging Radar for Automated Driving
Karim Armanious, Maurice Quach, Michael Ulrich +25
This paper introduces the Bosch street dataset (BSD), a novel multi-modal large-scale dataset aimed at promoting highly automated driving (HAD) and advanced driver-assistance syste…
Revisiting Out-of-Distribution Detection in LiDAR-based 3D Object Detection
Michael Kösel, Marcel Schreiber, Michael Ulrich +2
LiDAR-based 3D object detection has become an essential part of automated driving due to its ability to localize and classify objects precisely in 3D. However, object detectors fac…