[2605.05328] Query2Uncertainty: Robust Uncertainty Quantification and Calibration for 3D Object Detection under Distribution Shift
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Computer Science > Computer Vision and Pattern Recognition
arXiv:2605.05328 (cs)
[Submitted on 6 May 2026]
Title:Query2Uncertainty: Robust Uncertainty Quantification and Calibration for 3D Object Detection under Distribution Shift
Authors:Till Beemelmanns, Alexey Nekrasov, Stefan Vilceanu, Jonas Steinhaus, Timo Woopen, Bastian Leibe, Lutz Eckstein
View a PDF of the paper titled Query2Uncertainty: Robust Uncertainty Quantification and Calibration for 3D Object Detection under Distribution Shift, by Till Beemelmanns and 6 other authors
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Abstract: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-distribution tests, they fail to adapt in distribution-shifted scenarios. In this work, we address this issue and introduce a density-aware calibration method that couples post-hoc calibrators with the feature density of latent object queries from DETR-style 3D object detectors. These queries form a compact, location and class-aware feature, ideal for density estimation, allowing our approach to adjust model confidences in distribution-shift scenarios. By fitting a density estimator on these query features, our approach jointly recalibrates both classification and bounding box regression uncertainties. On both a multi-view camera and LiDAR-based detector, our approach consistently outperforms standard post-hoc methods in both in-distribution and distribution-shifted scenarios. Code available this https URL .
Comments: Accepted for publication at CVPR 2026
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
Cite as: arXiv:2605.05328 [cs.CV]
(or arXiv:2605.05328v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.05328
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arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Till Beemelmanns [view email] [v1] Wed, 6 May 2026 18:01:19 UTC (14,466 KB)
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