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

arXiv: https://arxiv.org/abs/2604.16910 · HTML: https://mlsanity.com/paper/2604.16910/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 22 references that are in the map.
- First posted on arXiv 18 Apr 2026.

## Builds on (most cited first, 15 of 15)

- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [CVXPY: A Python-Embedded Modeling Language for Convex Optimization](https://mlsanity.com/paper/1603.00943/) (2016)
- [Mega-NeRF: Scalable Construction of Large-Scale NeRFs for Virtual Fly-Throughs](https://mlsanity.com/paper/2112.10703/) (2021)
- [Learning Loss for Active Learning](https://mlsanity.com/paper/1905.03677/) (2019)
- [Learning Task-Oriented Communication for Edge Inference: An Information Bottleneck Approach](https://mlsanity.com/paper/2102.04170/) (2021)
- [Graph Neural Networks for Wireless Communications: From Theory to Practice](https://mlsanity.com/paper/2203.10800/) (2022)
- [GWM: Towards Scalable Gaussian World Models for Robotic Manipulation](https://mlsanity.com/paper/2508.17600/) (2025)
- [ActiveGS: Active Scene Reconstruction Using Gaussian Splatting](https://mlsanity.com/paper/2412.17769/) (2024)
- [From Ground to Sky: Architectures, Applications, and Challenges Shaping Low-Altitude Wireless Networks](https://mlsanity.com/paper/2506.12308/) (2025)
- [Integrated Sensing and Communications for Low-Altitude Economy: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2412.04074/) (2024)
- [ENGNN: A General Edge-Update Empowered GNN Architecture for Radio Resource Management in Wireless Networks](https://mlsanity.com/paper/2301.00757/) (2022)
- [Communication Efficient Robotic Mixed Reality with Gaussian Splatting Cross-Layer Optimization](https://mlsanity.com/paper/2508.08624/) (2025)
- [STT-GS: Sample-Then-Transmit Edge Gaussian Splatting with Joint Client Selection and Power Control](https://mlsanity.com/paper/2510.13186/) (2025)
- [Implicit Neural Representation of Beamforming for Continuous Aperture Array Systems](https://mlsanity.com/paper/2507.03609/) (2025)
- [Low-Altitude Satellite-AAV Collaborative Joint Mobile Edge Computing and Data Collection via Diffusion-based Deep Reinforcement Learning](https://mlsanity.com/paper/2601.07307/) (2026)

## Built on by (most cited first, 1 of 1)

- [DSWM: Decomposed Spatio-Temporal World Model for Demand-Driven UAV Base Station Repositioning](https://mlsanity.com/paper/2609.36845/) (2026)

## Abstract (arXiv)

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.