arXiv Computer Vision
GlassFormer: Learning Real-time Glass Segmentation using Radar-Depth Fusion
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arXiv AI
Jun 9
ATN3D: Density-Aware LiDAR-Radar Early 3D Object Detection Under Extreme Sparsity
arXiv:2606. 09634v1 Announce Type: cross Abstract: 3D object detection is the backbone of perception for automated vehicles (AV) and broader intelligent transportation systems applications.
Hugging Face Trending Papers
Jun 8
ATN3D: Density-Aware LiDAR-Radar Early 3D Object Detection Under Extreme Sparsity
3D object detection is the backbone of perception for automated vehicles (AV) and broader intelligent transportation systems applications. Long-range detection is challenging because sensing evidence is sparse; yet this ``long-range'' scenario is routine in traffic.
arXiv AI
Jul 13
4DR360: State Reasoning for Joint 3D Detection and Occupancy Prediction in 4D Radar-Camera Full-Scene Perception
arXiv:2607. 09629v1 Announce Type: cross Abstract: Reliable autonomous driving requires full-scene perception that couples foreground objects with dense semantic layout.
arXiv Computer Vision
Sep 18
4D Radar Perception Algorithms for Autonomous Driving: A Review
The review surveys 4D millimeter‑wave radar perception algorithms for autonomous driving, covering signal processing, object detection, semantic segmentation, motion estimation, occupancy prediction, and dynamic scene reconstruction. It organizes the field by perception tasks, discusses radar fundamentals, data representations, and quality‑enhancement methods, and compares radar‑only learning, multimodal fusion, and cross‑modal supervision. The paper also summarizes datasets, annotations, evaluation protocols, and outlines common challenges and future research directions.
arXiv Computer Vision
Sep 1
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.
arXiv AI
Sep 10
Segment Any Motion with Radar: Robust Multimodal Moving-Object Segmentation and Tracking
The paper introduces RGBTR‑Motion, a new benchmark that synchronizes RGB, thermal, and radar data with dense moving‑instance masks and consistent identities for surveillance scenes. It also presents SAM‑Radar, a segmentation and tracking framework that fuses calibrated RGBT features with radar returns, using radar‑aware detection and motion supervision to reject clutter and maintain identity continuity during low visibility or occlusion. SAM‑Radar achieves state‑of‑the‑art performance, improving IoU, F1‑50, MOTA, HOTA, and IDF1 metrics over existing methods.