Edge Gaussian splatting (EGS), which aggregates data from distributed clients (e.g., drones) and trains a global GS model at the edge (e.g., ground server), is an emerging paradigm for scene reconstruction in low-altitude economy. Unlike traditional edge resource management methods that emphasize communication throughput or general-purpose learning performance, EGS explicitly aims to maximize the GS qualities, rendering existing approaches inapplicable. To address this problem, this paper formulates a novel GS-oriented objective function that distinguishes the heterogeneous view contributions of different clients. However, evaluating this function in turn requires clients' images, leading to a causality dilemma. To this end, this paper further proposes a sample-then-transmit EGS (or STT-GS for short) strategy, which first samples a subset of images as pilot data from each client for loss prediction. Based on the first-stage evaluation, communication resources are then prioritized towards more valuable clients. To achieve efficient sampling, a feature-domain clustering (FDC) scheme is proposed to select the most representative data and pilot transmission time minimization (PTTM) is adopted to reduce the pilot overhead. Subsequently, we develop a joint client selection and power control (JCSPC) framework to maximize the GS-oriented function under communication resource constraints. Despite the nonconvexity of the problem, we propose a low-complexity efficient solution based on the penalty alternating majorization minimization (PAMM) algorithm. Experiments reveal that the proposed scheme significantly outperforms existing benchmarks on real-world datasets. The GS-oriented objective can be accurately predicted with low sampling ratios (e.g., 10%), and our method achieves an excellent tradeoff between view contributions and communication costs.

翻译：边缘高斯泼溅（EGS）通过聚合分布式客户端（如无人机）的数据，在边缘侧（如地面服务器）训练全局高斯泼溅（GS）模型，是低空经济中场景重建的新兴范式。与传统强调通信吞吐量或通用学习性能的边缘资源管理方法不同，EGS明确以最大化GS质量为优化目标，使得现有方法不再适用。为解决此问题，本文构建了一种新颖的面向GS的目标函数，该函数能区分不同客户端异构视图的贡献度。然而，评估该函数又需要客户端的图像数据，导致因果困境。为此，本文进一步提出了一种采样后传输的EGS策略（简称STT-GS），其首先从每个客户端采样部分图像作为导频数据以进行损失预测。基于第一阶段的评估结果，通信资源将优先分配给贡献度更高的客户端。为实现高效采样，本文提出了特征域聚类（FDC）方案以选择最具代表性的数据，并采用导频传输时间最小化（PTTM）策略降低导频开销。随后，我们开发了联合客户端选择与功率控制（JCSPC）框架，在通信资源约束下最大化面向GS的目标函数。尽管该问题具有非凸性，我们基于惩罚交替主最小化（PAMM）算法提出了一种低复杂度高效求解方案。实验表明，所提方案在真实数据集上显著优于现有基准方法。面向GS的目标函数可在低采样率（如10%）下被准确预测，且我们的方法在视图贡献度与通信成本之间实现了优异权衡。