arXiv:2608.28913v1 Announce Type: cross
Abstract: High-resolution 3D radar data is scarce. Commodity mmWave sensors use small antenna arrays that limit angular resolution to several degrees, and exis...
By Adnan Armouti, Yixuan Gao, Rajalakshmi Nandakumar
DyRAD introduces a novel radar novel‑view synthesis framework that models dynamic driving scenes by separating static background reflectors from motion‑tracked dynamic point reflectors, enabling the rendering of full range‑azimuth‑Doppler (RAD) tensors. The method derives reflector velocities from object tracks, projects them onto the line of sight, and uses a fixed analytic point‑spread function to avoid embedding sensor‑induced spread into the scene representation. This design allows accurate scene reconstruction and zero‑shot transfer to different radar configurations, achieving a 90.7% recovery of radar detections on the RADIal dataset compared to 26.9% for the best baseline.
By Merav Keidar, Tomer Borreda, Rajalakshmi Nandakumar, Or Litany
arXiv:2606. 31609v1 Announce Type: cross Abstract: Radar sensors provide reliable perception under adverse weather and lighting conditions, but their sparse, noisy, and weakly semantic measurements make dense semantic segmentation challenging.
By Ali Zia, Muhammad Umer Ramzan, Abdelwahed Khamis, Usman Ali, Abdul Rehman
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.
By Xumin Wu, Jun Zhou, Jilin Mei, Chen Min, Yu Hu
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
The paper introduces 3D Point Splatting (3DPS), a differentiable point renderer for millimeter-wave radar that directly implements the radar equation with an explicit material model and complex-valued outputs. Unlike prior methods, 3DPS simultaneously provides physical fidelity, complex-valued rendering, and multi-viewpoint tractability, enabling a single optimized scene to generate ADC, complex range profile, and range-azimuth images via FFT pipelines. On six outdoor ColoRadar scenes, 3DPS achieves a mean Pearson correlation of 0.587 on held-out range-azimuth images, outperforming optical-NVS baselines by 1.7x to 5.2x, and trains in about three minutes per scene on an RTX 4090.