DGHMesh supplies a large-scale dual-radar mmWave dataset and generalization benchmark for human mesh reconstruction, together with the mmPTM multi-radar fusion model that reports strong accuracy and cross-configuration performance.
A survey of mmwave radar- based sensing in autonomous vehicles, smart homes and industry,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Near-field mmWave imaging is highly vulnerable to waveform-domain attacks that conceal or alter targets with moderate power, with deep-learning algorithms demonstrating higher robustness than classical methods.
citing papers explorer
-
DGHMesh: A Large-scale Dual-radar mmWave Dataset and Generalization-focused Benchmark for Human Mesh Reconstruction
DGHMesh supplies a large-scale dual-radar mmWave dataset and generalization benchmark for human mesh reconstruction, together with the mmPTM multi-radar fusion model that reports strong accuracy and cross-configuration performance.
-
Adversarial Robustness of Near-Field Millimeter-Wave Imaging under Waveform-Domain Attacks
Near-field mmWave imaging is highly vulnerable to waveform-domain attacks that conceal or alter targets with moderate power, with deep-learning algorithms demonstrating higher robustness than classical methods.