RaUF：学习雷达的空间不确定性场
RaUF: Learning the Spatial Uncertainty Field of Radar
提出基于各向异性概率模型的空间不确定性场学习框架，可复用于雷达点云去噪与几何推理；双向域注意力机制利用空间结构与多普勒一致性互补抑制杂波，为多模态融合中的特征对齐提供新思路。
原文依据 we propose RaUF, a spatial uncertainty field learning framework that models radar measurements through their physically grounded anisotropic properties.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论毫米波雷达在恶劣天气下具有独特优势，但存在空间保真度低、方位模糊严重以及杂波引起的虚假回波等问题。现有方法主要关注通过由粗到细的跨模态监督来提升空间感知效果，却往往忽略了模糊的特征到标签映射，这可能导致不适定的几何推理，并对下游感知任务构成根本性挑战。在本工作中，我们提出RaUF，一种空间不确定性场学习框架，通过雷达测量固有的各向异性物理特性对其进行建模。为解决冲突的特征到标签映射，我们设计了一种各向异性概率模型来学习细粒度不确定性。为进一步提升可靠性，我们提出双向域注意力机制，利用空间结构与多普勒一致性之间的互补性，有效抑制虚假或多径引起的反射。在公开基准和真实世界数据集上的大量实验表明，RaUF能够提供高度可靠的空间检测，并具有良好校准的不确定性。此外，下游案例研究进一步验证了RaUF在具有挑战性的真实驾驶场景中增强的可靠性和可扩展性。我们的项目将发布于https://shengpeng.wang/rauf。
英文原文摘要 1380 字符
Millimeter-wave radar offers unique advantages in adverse weather but suffers from low spatial fidelity, severe azimuth ambiguity, and clutter-induced spurious returns. Existing methods mainly focus on improving spatial perception effectiveness via coarse-to-fine cross-modal supervision, yet often overlook the ambiguous feature-to-label mapping, which may lead to ill-posed geometric inference and pose fundamental challenges to downstream perception tasks. In this work, we propose RaUF, a spatial uncertainty field learning framework that models radar measurements through their physically grounded anisotropic properties. To resolve conflicting feature-to-label mapping, we design an anisotropic probabilistic model that learns fine-grained uncertainty. To further enhance reliability, we propose a Bidirectional Domain Attention mechanism that exploits the mutual complementarity between spatial structure and Doppler consistency, effectively suppressing spurious or multipath-induced reflections. Extensive experiments on public benchmarks and real-world datasets demonstrate that RaUF delivers highly reliable spatial detections with well-calibrated uncertainty. Moreover, downstream case studies further validate the enhanced reliability and scalability of RaUF under challenging real-world driving scenarios. Our project will be available at https://shengpeng.wang/rauf.