LAGS: Low-Altitude Gaussian Splatting with Groupwise Heterogeneous Graph Learning

TL;DR

This paper introduces GW-HGNN, a graph neural network that improves resource allocation in low-altitude Gaussian splatting for 3D scene reconstruction, enhancing quality and efficiency.

Contribution

The paper proposes a novel groupwise heterogeneous graph neural network for LAGS resource allocation, explicitly modeling image contributions to optimize reconstruction and transmission.

Findings

GW-HGNN outperforms state-of-the-art benchmarks in PSNR, SSIM, and LPIPS.

It reduces computational latency by approximately 100x compared to MOSEK.

Achieves millisecond-level inference for real-time deployment.

Abstract

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.