arXiv:2609.34220v2 Announce Type: replace-cross Abstract: Assistive robots increasingly operate in many human-centered environments and perform various human-robot interaction (HRI) tasks, such as ob...
arXiv:2609.21000v1 Announce Type: cross Abstract: Spinning frequency-modulated continuous-wave (FMCW) radars have been gaining popularity in autonomous vehicle perception on account of their robustne...
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:2610.27774v1 Announce Type: cross Abstract: 3D Human Pose Estimation (3D HPE) is used in indoor environments, such as smart homes, to analyze human movement and monitor human states. Millimeter...
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.
DiFF is a generative framework that uses Doppler velocity cues from 4D millimeter-wave radar to improve human motion flow estimation. It combines Doppler-informed motion priors with a Kolmogorov‑Arnold Network (KAN) based conditional flow matching model, featuring a KAN‑attention mechanism for expressive feature extraction. Experiments demonstrate that DiFF achieves state‑of‑the‑art performance, reducing 3D endpoint error to the millimeter scale on the mmBody benchmark.
Assistive robots increasingly operate in many human-centered environments and perform various human-robot interaction (HRI) tasks, such as object delivery. However, most existing HRI systems rely on R...
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.
arXiv:2605. 00242v2 Announce Type: replace-cross Abstract: Millimetre-wave (mmWave) radar offers a more privacy-preserving alternative to RGB-based human pose estimation.
arXiv:2607. 09629v1 Announce Type: cross Abstract: Reliable autonomous driving requires full-scene perception that couples foreground objects with dense semantic layout.
The paper presents a stereo 4D Radar framework for 3D object detection that uses geometric disparity between left and right radars to estimate absolute velocity and fuse complementary features. It addresses clutter, ghost reflections, and sparse data issues inherent in raw 4D Radar signals. Experiments on an in‑house dataset show significant gains, improving AP 3D by 8.82 points and AP BEV by 9.0 points over mono‑radar baselines.
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.