Reconstruction-Aware Observation Selection for Bandwidth-Constrained Multi-UAV Gaussian Splatting Reconstruction

Multi-UAV observation improves the efficiency and coverage of three-dimensional scene acquisition, but continuous onboard Gaussian Splatting reconstruction is computationally expensive, whereas transmitting dense RGB-D streams wastes communication bandwidth. We propose RAOS-GS (Reconstruction-Aware Observation Selection for Gaussian Splatting), an air–ground collaborative framework for bandwidth-constrained multi-UAV reconstruction. Each UAV maintains a lightweight Gaussian proxy solely to estimate the reconstruction value of candidate observations. The proposed Rendering-Aware Keyframe Selection (RAKS) evaluates uncovered scene content, appearance discrepancy within reliably rendered regions, and relative viewpoint change to retain complementary, non-redundant RGB-D keyframes. The ground station then constructs per-UAV Gaussian submaps, aligns their coordinate frames through cross-UAV RGB-D registration, and jointly refines camera poses and Gaussian parameters to produce a global representation. Experiments on three Replica scenes, two OpenFly-UE virtual outdoor scenes, and three real-world multi-UAV scenes demonstrate a favorable trade-off between observation volume and reconstruction quality. RAOS-GS retains only 5.1%–7.9% of the input observations on Replica and the real-world sequences and 18.7%–24.0% on OpenFly-UE. It consistently outperforms 10% uniform temporal sampling and remains competitive with or surpasses 30% sampling despite using fewer observations. Ablation studies confirm that rendering-aware selection identifies reconstruction-relevant observations, viewpoint constraints suppress redundancy, and ground-side pose–Gaussian refinement improves final reconstruction quality. These results show that reconstruction-aware observation selection can reduce air–ground data transmission without requiring high-fidelity onboard Gaussian mapping.