用图神经网络优化低空无人机3D重建的资源分配，提升质量并加速推理。
LAGS: Low-Altitude Gaussian Splatting with Groupwise Heterogeneous Graph Learning
- 设计分组异构图网络，显式建模不同视角图像对重建的非均匀贡献。
- 在真实数据集上显著优于现有方法，PSNR、SSIM、LPIPS均更优。
- 计算延迟降低100倍，毫秒级响应，适合实时部署场景。
低空高斯点云（LAGS）通过聚合分布式无人机拍摄的航拍图像实现3D场景重建。然而，由于现有资源分配方案侧重于最大化重建质量而忽略通信吞吐量，导致效率低下，其根源在于未能考虑不同视角带来的图像多样性。为此，本文提出一种面向LAGS的分组异构图神经网络（GW-HGNN），显式建模不同图像组对重建过程的非均匀贡献，从而自动平衡数据保真度与传输成本。其核心思想是将LAGS损失与通信约束转化为图学习中的代价，并通过双层消息传递实现优化。在真实世界LAGS数据集上的实验表明，GW-HGNN在关键渲染指标（如PSNR、SSIM、LPIPS）上显著优于当前最优基准。此外，相比广泛使用的MOSEK求解器，其计算延迟降低了约100倍，实现毫秒级推理，适用于实时部署。
原文摘要 · Abstract (English)
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.
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