arXiv Machine Learning November 6, 2026 By Adnan Armouti, Guanyue Qian, Rajalakshmi Nandakumar

4D Point Splatting for mmWave Radar Micro-Doppler Differentiable Rendering

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benchmarks

arXiv AI

Oct 21

Radar-Based 3D Human Pose Estimation via Motion-Aware Pose Initialization and Observation-Guided Refinement

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...

By Woo-Jin Jung, Chan-Bin Lim, Seung-Hyun Kong

computer-vision

arXiv Computer Vision

Oct 1

DyRAD: Radar Novel View Synthesis for Dynamic Driving Scenes

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

agents robotics benchmarks

arXiv AI

Sep 30

Privacy-Preserving Full-Body Meshing from mmWave Radar via Mesh Foundation Model Supervision

arXiv:2609.34768v2 Announce Type: replace Abstract: Millimeter-wave (mmWave) radar enables privacy-preserving human perception, but the extreme sparsity of point clouds from commercial single-chip se...

By Shuxing Zhang, Yongquan Ni, Zhenyu Ding, Yawen Lin

llms efficiency multimodal benchmarks safety

arXiv Machine Learning

Sep 1

mmIR: Frequency-Space Inverse Rendering for 3D Millimeter-Wave Radar ADC Synthesis

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

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

llms robotics multimodal

Hugging Face Trending Papers

Sep 10

3D Point Splatting for mmWave Radar Novel View Synthesis

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.