PRI-Net: A Lightweight Multimodal Framework for 3D UAV Localization
Accurate 3D localization of unmanned aerial vehicles (UAVs) remains challenging for existing multimodal approaches due to sparse LiDAR geometry, modality-imbalanced fusion, and redundant feature transmission over constrained edge-to-server links. To address these limitations, we propose PRI-Net, an efficient and lightweight multimodal fusion framework for UAV localization that integrates point cloud splatting, residual attention fusion, and an information bottleneck. Specifically, a 3D point cloud splatting (3DPCS) strategy is introduced to transform sparse LiDAR observations into geometrically consistent dense depth maps. A residual attention fusion (RAF) module is then designed to allev...
| Authors | Zhixuan Chen, Jialiang Lu, Zhong Ye et al. |
|---|---|
| Primary category | cs.CV |
| Published | 13 September 2026 |
| Last updated at source | 13 September 2026 |
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