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cs.CV2026
Query2Uncertainty: Robust Uncertainty Quantification and Calibration for 3D Object Detection under Distribution Shift
Till Beemelmanns, Alexey Nekrasov, Stefan Vilceanu +4
Reliable uncertainty estimation for 3D object detection is critical for deploying safe autonomous systems, yet modern detectors remain poorly calibrated, especially under distribut…
cs.CV2026
Robust Fusion of Object-Level V2X for Learned 3D Object Detection
Lukas Ostendorf, Lennart Reiher, Onn Haran +1
Perception for automated driving is largely based on onboard environmental sensors, such as cameras and radar, which are cost-effective but limited by line-of-sight and field-of-vi…
cs.CV2025
OCCUQ: Exploring Efficient Uncertainty Quantification for 3D Occupancy Prediction
Severin Heidrich, Till Beemelmanns, Alexey Nekrasov +2
Autonomous driving has the potential to significantly enhance productivity and provide numerous societal benefits. Ensuring robustness in these safety-critical systems is essential…