arXiv Computer Vision By Yinghao Sun, Shuguang Li, Jinliang Shao, Tieshan Li

If It Moves, Radar Knows: A Physics-Aware Radar Transformer for Class-Agnostic Moving-Object Detection

Read the original on arXiv Computer Vision →

The paper introduces the Physics-Aware Radar Transformer (PART), a radar-only detector that predicts moving-object existence, surface points, and ground-plane velocity using Doppler-aware query initialization and physics-guided cross-attention. PART achieves high class-agnostic performance on the nuScenes dataset, excelling in rare categories and adverse conditions such as night, rain, and occlusion. The model is lightweight, with only 1.1 million parameters, and its code and pretrained weights will be released publicly.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

arXiv Computer Vision
3d ago

RLG-TPV: Radar- and LiDAR-Guided Tri-Perspective View Fusion for Camera-Radar 3D Object Detection

RLG-TPV introduces a multimodal Tri-Perspective View framework that fuses camera, radar, and training‑time LiDAR data for 3D object detection. It uses radar and LiDAR to guide a ray‑deformable attention lift, refining depth distributions and providing geometric supervision for side and front planes, while radar cross‑section awareness spreads evidence spatially. On nuScenes, the method attains 0.4981 mAP and 0.5959 NDS, improving orientation and velocity accuracy by about 32 % and 31 % over the CRN baseline.

By Ahmet Mete Dokgoz, A. Enes Doruk, Hasan F. Ates
arXiv Machine Learning
Jun 30

RadarTwin: Scene-Specific mmWave Radar Simulation and Learning for Mobile Indoor Perception

arXiv:2606. 28396v1 Announce Type: cross Abstract: Millimeter-wave (mmWave) radar perception is limited by data scarcity: models trained on existing radar datasets fail to generalize to new objects, environments, and sensing trajectories.

By Emily Bejerano, Federico Tondolo, Devang Gupta, Aaron Mano Cherian, Taeyoo Kim, Ayaan Qayyum, Xiaofan Yu, Xiaofan Jiang