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. The key insight of GW-HGNN is to transform LAGS losses and communication constraints into graph learning costs for dual-level message passing. Experiments on real-world LAGS datasets demonstrate that GW-HGNN significantly outperforms state-of-the-art benchmarks across key rendering metrics, including PSNR, SSIM, and LPIPS. Furthermore, GW-HGNN reduces computational latency by approximately 100x compared to the widely-used MOSEK solver, achieving millisecond-level inference suitable for real-time deployment.

翻译：摘要：低空高斯泼溅（LAGS）通过聚合分布式无人机采集的航空图像实现三维场景重建。然而，由于LAGS优先最大化重建质量而非通信吞吐量，现有低空资源配置方案效率低下。这一效率损失源于其未能考虑不同视角引入的图像多样性。为填补这一空白，我们提出一种面向LAGS资源配置的分组异构图表征网络（GW-HGNN）。GW-HGNN显式建模不同图像组对重建过程的非均匀贡献，从而自动权衡数据保真度与传输成本。其核心思想在于将LAGS损失函数与通信约束转化为图学习成本，实现双层级消息传递。在真实LAGS数据集上的实验表明，GW-HGNN在PSNR、SSIM和LPIPS等关键渲染指标上显著优于当前最优基准。此外，与广泛使用的MOSEK求解器相比，GW-HGNN将计算延迟降低约100倍，实现毫秒级推理，适合实时部署。