18 citations · 49 across the 15 of their papers we have counts for
10 papers · 1 filter
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…
The ADUULM-360 Dataset -- A Multi-Modal Dataset for Depth Estimation in Adverse Weather
Markus Schön, Jona Ruof, Thomas Wodtko +2
Depth estimation is an essential task toward full scene understanding since it allows the projection of rich semantic information captured by cameras into 3D space. While the field…
MGNiceNet: Unified Monocular Geometric Scene Understanding
Markus Schön, Michael Buchholz, Klaus Dietmayer
Monocular geometric scene understanding combines panoptic segmentation and self-supervised depth estimation, focusing on real-time application in autonomous vehicles. We introduce…
SemanticSpray++: A Multimodal Dataset for Autonomous Driving in Wet Surface Conditions
Aldi Piroli, Vinzenz Dallabetta, Johannes Kopp +3
Autonomous vehicles rely on camera, LiDAR, and radar sensors to navigate the environment. Adverse weather conditions like snow, rain, and fog are known to be problematic for both c…
Label-Efficient Semantic Segmentation of LiDAR Point Clouds in Adverse Weather Conditions
Aldi Piroli, Vinzenz Dallabetta, Johannes Kopp +3
Adverse weather conditions can severely affect the performance of LiDAR sensors by introducing unwanted noise in the measurements. Therefore, differentiating between noise and vali…
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…
Simultaneous Clutter Detection and Semantic Segmentation of Moving Objects for Automotive Radar Data
Johannes Kopp, Dominik Kellner, Aldi Piroli +2
The unique properties of radar sensors, such as their robustness to adverse weather conditions, make them an important part of the environment perception system of autonomous vehic…
Multimodal Object Query Initialization for 3D Object Detection
Mathijs R. van Geerenstein, Felicia Ruppel, Klaus Dietmayer +1
3D object detection models that exploit both LiDAR and camera sensor features are top performers in large-scale autonomous driving benchmarks. A transformer is a popular network ar…
Towards Robust 3D Object Detection In Rainy Conditions
Aldi Piroli, Vinzenz Dallabetta, Johannes Kopp +3
LiDAR sensors are used in autonomous driving applications to accurately perceive the environment. However, they are affected by adverse weather conditions such as snow, fog, and ra…
LS-VOS: Identifying Outliers in 3D Object Detections Using Latent Space Virtual Outlier Synthesis
Aldi Piroli, Vinzenz Dallabetta, Johannes Kopp +3
LiDAR-based 3D object detectors have achieved unprecedented speed and accuracy in autonomous driving applications. However, similar to other neural networks, they are often biased…