mlsanityData to 14 Oct 2026

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

arXiv 2605.05328 ↗First posted 6 May 2026cs.CV cs.ROcited by 1

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Abstract (arXiv)

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 https://tillbeemelmanns.github.io/query2uncertainty/ .

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 44 of its 56 references that are in the map. First posted on arXiv 6 May 2026.

Builds on

44 of 56 references.

References · 44

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32 more
  1. 2022PETRv2: A Unified Framework for 3D Perception from Multi-Camera Imagescited by 352
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deploymentcited by 0