Cross-Modal Hierarchical Fusion for from Multi-Sensor Ground Observation
Dense volumetric reconstruction of cloud microphysical fields from sparse ground-based instruments remains an open problem, largely because the available measurements are heterogeneous in both modality and spatial coverage. We present AtmoFuseNet, a framework that fuses multi-view sky camera imagery with millimeter-wave cloud radar and ceilometer observations to produce 4D (three spatial dimensions plus time) estimates of cloud state and wind. The method operates in three stages: a cross-modal hierarchical aggregation module that combines image feature pyramids with instrument-derived vertical profiles through layer-wise cross-attention; a conditional variational refinement module that ma...
| Authors | Xinze Zhang |
|---|---|
| Primary category | cs.CV |
| Published | 31 May 2026 |
| Last updated at source | 31 May 2026 |
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