通过查询特征密度提升3D检测在分布偏移下的不确定性估计可靠性
Query2Uncertainty: Robust Uncertainty Quantification and Calibration for 3D Object Detection under Distribution Shift
- 利用DETR类模型的查询特征进行密度估计，实现自适应校准
- 在分布偏移场景下，分类与框回归不确定性校准效果优于传统方法
- 适合需要高可靠性推理的自动驾驶系统部署
可靠的3D目标检测不确定性估计对安全部署自主系统至关重要，但现有检测器在分布偏移下仍存在校准不足问题。尽管后验校准方法可改善分布内测试的校准性能，却无法适应分布偏移场景。本文提出一种密度感知校准方法，将后验校准器与DETR风格3D检测器中潜在对象查询的特征密度相结合。这些查询形成紧凑、位置与类别感知的特征表示，适用于密度估计，使模型能在分布偏移时动态调整置信度。通过对查询特征拟合密度估计器，本方法联合重校准分类与边界框回归的不确定性。在基于多视角相机和LiDAR的检测器上，该方法在分布内及分布偏移场景下均持续优于标准后验校准方法。代码已公开。
原文摘要 · Abstract (English)
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/ .
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