A 3D-SPADE algorithm uses LiDAR point clouds to predict scatterer distributions and classify them as dynamic or static, enabling accurate non-stationary U2V channel modeling that matches ray-tracing better than standardized models.
3D U-Net: learning dense volumetric segmentation from sparse annotation,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
eess.SP 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
A Multi-Modal Intelligent U2V Channel Model for 6G Sensing-Communication Integration
A 3D-SPADE algorithm uses LiDAR point clouds to predict scatterer distributions and classify them as dynamic or static, enabling accurate non-stationary U2V channel modeling that matches ray-tracing better than standardized models.