Millimeter-Wave Radar

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9 papers in the last four weeks, up 200% on the four weeks before. 0.1% of all new papers.

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Latest papers 43

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DAP: Doppler-aware Point Network for Heterogeneous mmWave Action Recognition
May 10, 2026Jiaying Lin, Shiman Wu, Jinfu Liu +2Human Activity Recognition Point Cloud Learning
State Key Laboratory of General Artificial Intelligence, Peking University, Shenzhen Graduate School, Shenzhen, China · Huazhong University of Science and Technology, Wuhan, China · DJI Technology Co., Ltd., Shenzhen, China +2
CAST: Channel-Aware Spatial Transfer Learning with Pseudo-Image Radar for Sign Language Recognition
May 9, 2026Md. Shakhoyat Rahman Shujon, Sheikh Md. Galib Mahim, Md. Milon Islam +4Millimeter-Wave Radar mmWave Radar Sensing
Department of Computer Science and Engineering, Khulna University of Engineering & Technology · Department of Electrical and Computer Engineering, University of Waterloo · Department of Electrical and Computer Engineering, Texas A&M University +2
A Two-Stage Motion-Aware Framework for mmWave-based Human Mesh Recovery
May 8, 2026Hoang Hai Pham, Shuntian Zheng, Jiaqi Li +13D Reconstruction Human Mesh Recovery
Department of Computer Science, University of Warwick, Coventry, UK
Non-intrusive Body Composition Assessment from Full-body mmWave Scans
May 8, 2026Miriam Senne, Benjamin D. Killeen, Tony Danjun Wang +1Medical Imaging Millimeter-Wave Radar
Chair for Computer Aided Medical Procedures Technical University of Munich, Boltzmannstr. 3, 85748 Garching, Germany · Munich Center for Machine Learning, Munich, Germany
Weather-Robust Scene Semantics with Vision-Aligned 4D Radar
May 8, 2026Kali Hamilton, Christoffer HeckmanCross-Modal Learning VLM Robustness
Autonomous Robotics and Perception Group at the University of Colorado Boulder, Boulder, CO 80309, USA.
Dr-PoGO: Direct Radar Pose-Graph Optimization
May 6, 2026Cedric Le Gentil, Weican Li, Leonardo Brizi +1Simultaneous Localization and Mapping Relative Pose Estimation
Robotics Institute University of Toronto. · Department of Computer, Control, and Management Engineering “Antonio Ruberti”, Sapienza University of Rome.
Terrain Perception for Agricultural UAVs in Complex Farmland via Rotating mmWave Radar
May 2, 2026Zhihao Zhan, Le Tao, Shaobin Li +5Aerial Robotics 3D Reconstruction
School of Computer Science, Hangzhou Dianzi University, Hangzhou, 310018, China · TopXGun Robotics, Nanjing, 211100, China · CARDC, Mianyang, 621000, China +2
MAEPose: Self-Supervised Spatiotemporal Learning for Human Pose Estimation on mmWave Video
Apr 30, 2026Xijia Wei, Yuan Fang, Kevin Chetty +2Masked Autoencoders Self-Supervised Learning
University College London London, UK
DGHMesh: A Large-scale Dual-radar mmWave Dataset and Generalization-focused Benchmark for Human Mesh Reconstruction
Apr 19, 2026Rongxiao Guo, Qingchao Chen3D Reconstruction Human Mesh Recovery
Institute of Medical Technology, Peking University Health Science Center · National Institute of Health Data Science at Peking University · State Key Laboratory of General Artificial Intelligence, Peking University
4D Radar Gaussian Modeling and Scan Matching with RCS
Apr 16, 2026Fernando Amodeo, Luis Merino, Fernando CaballeroMillimeter-Wave Radar mmWave Radar Sensing
Service Robotics Laboratory – Universidad Pablo de Olavide (Seville), Spain
On Learning Spatial Structure from Pre-Beamforming Per-Antenna Range-Doppler Radar Measurements
Apr 2, 2026George Sebastian, Philipp Berthold, Bianca Forkel +2Representation Learning Millimeter-Wave Radar
Institute for Autonomous Driving, Department of Aerospace Engineering, University of the Bundeswehr Munich, Neubiberg, Germany
Registering the 4D Millimeter Wave Radar Point Clouds Via Generalized Method of Moments
Aug 4, 2025Xingyi Li, Han Zhang, Ziliang Wang +2Millimeter-Wave Radar mmWave Radar Sensing
School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai, China · Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China · Department of Statistics, Uppsala University, Sweden
Simulating Automotive Radar with Lidar and Camera Inputs
Mar 11, 2025Peili Song, Dezhen Song, Yifan Yang +2Synthetic Data Augmentation Millimeter-Wave Radar
Institute of Robotics and Automatic Information System, Nankai University, Tianjin 300350, China; Tianjin Key Laboratory of Intelligent Robotics, Tianjin 300350, China; and also with TBI center, Nankai University, Tianjin 300350, China · Department of Robotics, Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, United Arab Emirates

May 10, 2026, cs.CVJ/K move · Enter open · S save

DAP: Doppler-aware Point Network for Heterogeneous mmWave Action Recognition

Jiaying Lin, Shiman Wu, Jinfu Liu, Can Wang+1

State Key Laboratory of General Artificial Intelligence, Peking University, Shenzhen Graduate School, Shenzhen, China · Huazhong University of Science and Technology, Wuhan, China · DJI Technology Co., Ltd., Shenzhen, China+2

Human Activity Recognition Point Cloud Learning Millimeter-Wave Radar Domain Generalization mmWave Radar Sensing

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Millimeter-wave (mmWave) radar provides privacy-preserving sensing and is valuable for human action recognition (HAR). Existing mmWave point cloud datasets are limited in scale and mostly collected under homogeneous single-source settings, preventing current methods from handling real-world distribution shifts caused by heterogeneous radar sources, such as different devices and frequency bands. To address this, we introduce UniMM-HAR, the largest and first mmWave point cloud HAR dataset for heterogeneous multi-source scenarios, standardizing three distinct radar configurations to realistically evaluate cross-source generalization. We further propose the Doppler-aware Point Cloud Network (DAP-Net) to tackle heterogeneity challenges. DAP-Net enhances intra-modal representations and performs cross-modal alignment to learn source-invariant action semantics. Leveraging action-consistent spatio-temporal Doppler patterns as anchors, the Dual-space Doppler Reparameterization (D2R) module performs sample-adaptive geometric densification and Doppler-guided feature recalibration, while the Text Alignment Module (TAM) provides stable semantic anchors via a pretrained textual space. Experiments show that DAP-Net significantly outperforms existing methods under heterogeneous radar settings, achieving state-of-the-art accuracy and strong cross-source robustness.

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