Till Beemelmanns — accepted AI-conference papers

2026

Query2Uncertainty: Robust Uncertainty Quantification and Calibration for 3D Object Detection under Distribution Shift

Till Beemelmanns, Alexey Nekrasov, Stefan Vilceanu, Jonas Steinhaus, Timo Woopen, Bastian Leibe +1

CVPR 2026

Reliable uncertainty estimation for 3D object detection is critical for deploying safe autonomous systems, yet modern detectors remain poorly calibrated, especially under distribution shifts. Although post-hoc calibration methods address this issue and provide improved calibration for in-distributio

Cited by 0 Source ↗code ↗Scholar ↗

2025

OCCUQ: Exploring Efficient Uncertainty Quantification for 3D Occupancy Prediction

Severin Heidrich, Till Beemelmanns, Alexey Nekrasov, Bastian Leibe, Lutz Eckstein

ICRA 2025

Autonomous driving has the potential to significantly enhance productivity and provide numerous societal benefits. Ensuring robustness in these safety-critical systems is essential, particularly when vehicles must navigate adverse weather conditions and sensor corruptions that may not have been enco

Cited by 4 Source ↗code ↗Scholar ↗