# RaUF: Learning the Spatial Uncertainty Field of Radar

arXiv: https://arxiv.org/abs/2603.01026 · HTML: https://mlsanity.com/paper/2603.01026/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 31 references that are in the map.
- First posted on arXiv 1 Mar 2026.

## Builds on (most cited first, 16 of 16)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [CRN: Camera Radar Net for Accurate, Robust, Efficient 3D Perception](https://mlsanity.com/paper/2304.00670/) (2023)
- [RCBEVDet: Radar-camera Fusion in Bird's Eye View for 3D Object Detection](https://mlsanity.com/paper/2403.16440/) (2024)
- [FAST-LIVO2: Fast, Direct LiDAR-Inertial-Visual Odometry](https://mlsanity.com/paper/2408.14035/) (2024)
- [High Resolution Point Clouds from mmWave Radar](https://mlsanity.com/paper/2206.09273/) (2022)
- [ColoRadar: The Direct 3D Millimeter Wave Radar Dataset](https://mlsanity.com/paper/2103.04510/) (2021)
- [A New Wave in Robotics: Survey on Recent mmWave Radar Applications in Robotics](https://mlsanity.com/paper/2305.01135/) (2023)
- [Under the Radar: Learning to Predict Robust Keypoints for Odometry Estimation and Metric Localisation in Radar](https://mlsanity.com/paper/2001.10789/) (2020)
- [Towards Dense and Accurate Radar Perception Via Efficient Cross-Modal Diffusion Model](https://mlsanity.com/paper/2403.08460/) (2024)
- [4D iRIOM: 4D Imaging Radar Inertial Odometry and Mapping](https://mlsanity.com/paper/2303.13962/) (2023)
- [A Deep Automotive Radar Detector using the RaDelft Dataset](https://mlsanity.com/paper/2406.04723/) (2024)
- [RadarMOSEVE: A Spatial-Temporal Transformer Network for Radar-Only Moving Object Segmentation and Ego-Velocity Estimation](https://mlsanity.com/paper/2402.14380/) (2024)
- [Enhancing mmWave Radar Point Cloud via Visual-inertial Supervision](https://mlsanity.com/paper/2404.17229/) (2024)
- [DC-Loc: Accurate Automotive Radar Based Metric Localization with Explicit Doppler Compensation](https://mlsanity.com/paper/2112.14887/) (2021)
- [S3E: Self-Supervised State Estimation for Radar-Inertial System](https://mlsanity.com/paper/2509.25984/) (2025)
- [RaLD: Generating High-Resolution 3D Radar Point Clouds with Latent Diffusion](https://mlsanity.com/paper/2511.07067/) (2025)

## Abstract (arXiv)

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.