activity
20222024
most citedSCENE: Reasoning about Traffic Scenes using Heterogeneous Graph Neural Networks

61 citations · 118 across the 18 of their papers we have counts for

collaborators

18 papers

cs.CV2024

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…

cs.CV202435 cited

The Radar Ghost Dataset -- An Evaluation of Ghost Objects in Automotive Radar Data

Florian Kraus, Nicolas Scheiner, Werner Ritter +1

Radar sensors have a long tradition in advanced driver assistance systems (ADAS) and also play a major role in current concepts for autonomous vehicles. Their importance is reasone…

cs.CV2023

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…

cs.CV2023

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…

cs.CV2023

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…

cs.LG2023

Group Regression for Query Based Object Detection and Tracking

Felicia Ruppel, Florian Faion, Claudius Gläser +1

Group regression is commonly used in 3D object detection to predict box parameters of similar classes in a joint head, aiming to benefit from similarities while separating highly d…