LAGS: Low-Altitude Gaussian Splatting with Groupwise Heterogeneous Graph Learning
Low-altitude Gaussian splatting (LAGS) facilitates 3D scene reconstruction by aggregating aerial images from distributed drones. However, as LAGS prioritizes maximizing reconstruction quality over communication throughput, existing low-altitude resource allocation schemes become inefficient. This inefficiency stems from their failure to account for image diversity introduced by varying viewpoints. To fill this gap, we propose a groupwise heterogeneous graph neural network (GW-HGNN) for LAGS resource allocation. GW-HGNN explicitly models the non-uniform contribution of different image groups to the reconstruction process, thus automatically balancing data fidelity and transmission cost. Th...
| Authors | Yikun Wang, Yujie Wan, Wei Zuo et al. |
|---|---|
| Primary category | cs.CV |
| Published | 18 April 2026 |
| Last updated at source | 15 August 2026 |
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