Query2Uncertainty improves 3D object detection calibration under distribution shifts

Researchers have developed Query2Uncertainty, a novel method to improve the reliability of uncertainty estimation in 3D object detection systems. This approach specifically addresses the challenge of distribution shift, where existing detectors often fail to provide accurate confidence scores. By coupling post-hoc calibration with the density of latent object queries from DETR-style detectors, Query2Uncertainty can adjust model confidences even when encountering unseen data distributions, leading to better calibration for both classification and bounding box regression. AI

影响 Enhances safety and reliability of autonomous systems by improving uncertainty estimation in 3D object detection under varied conditions.

排序理由 This is a research paper detailing a new method for uncertainty quantification in 3D object detection. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

• arXiv
• Query2Uncertainty

• 论文
• 安全

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →