# Efficient Transceiver Design for Aerial Image Transmission and Large-scale Scene Reconstruction

arXiv: https://arxiv.org/abs/2604.11098 · HTML: https://mlsanity.com/paper/2604.11098/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 23 references that are in the map.
- First posted on arXiv 13 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)
- [Mega-NeRF: Scalable Construction of Large-Scale NeRFs for Virtual Fly-Throughs](https://mlsanity.com/paper/2112.10703/) (2021)
- [End-to-end Learning for OFDM: From Neural Receivers to Pilotless Communication](https://mlsanity.com/paper/2009.05261/) (2020)
- [Task-Oriented Image Transmission for Scene Classification in Unmanned Aerial Systems](https://mlsanity.com/paper/2112.10948/) (2021)
- [GauU-Scene V2: Assessing the Reliability of Image-Based Metrics with Expansive Lidar Image Dataset Using 3DGS and NeRF](https://mlsanity.com/paper/2404.04880/) (2024)
- [AirCopBench: A Benchmark for Multi-drone Collaborative Embodied Perception and Reasoning](https://mlsanity.com/paper/2511.11025/) (2025)
- [Unveiling the Power of Complex-Valued Transformers in Wireless Communications](https://mlsanity.com/paper/2502.11151/) (2025)
- [Flying in Clutter on Monocular RGB by Learning in 3D Radiance Fields with Domain Adaptation](https://mlsanity.com/paper/2512.17349/) (2025)
- [Communication Efficient Robotic Mixed Reality with Gaussian Splatting Cross-Layer Optimization](https://mlsanity.com/paper/2508.08624/) (2025)
- [Adaptive End-to-End Transceiver Design for NextG Pilot-Free and CP-Free Wireless Systems](https://mlsanity.com/paper/2510.25416/) (2025)
- [STT-GS: Sample-Then-Transmit Edge Gaussian Splatting with Joint Client Selection and Power Control](https://mlsanity.com/paper/2510.13186/) (2025)
- [Edge Collaborative Gaussian Splatting with Integrated Rendering and Communication](https://mlsanity.com/paper/2510.22718/) (2025)
- [SOAR: Simultaneous Exploration and Photographing with Heterogeneous UAVs for Fast Autonomous Reconstruction](https://mlsanity.com/paper/2409.02738/) (2024)
- [Perception-aware Planning for Quadrotor Flight in Unknown and Feature-limited Environments](https://mlsanity.com/paper/2503.15273/) (2025)
- [Sensing for Free: Learn to Localize More Sources than Antennas without Pilots](https://mlsanity.com/paper/2506.23525/) (2025)

## Abstract (arXiv)

Large-scale three-dimensional (3D) scene reconstruction in low-altitude intelligent networks (LAIN) demands highly efficient wireless image transmission. However, existing schemes struggle to balance severe pilot overhead with the transmission accuracy required to maintain reconstruction fidelity. To strike a balance between efficiency and reliability, this paper proposes a novel deep learning-based end-to-end (E2E) transceiver design that integrates 3D Gaussian Splatting (3DGS) directly into the training process. By jointly optimizing the communication modules via the combined 3DGS rendering loss, our approach explicitly improves scene recovery quality. Furthermore, this task-driven framework enables the use of a sparse pilot scheme, significantly reducing transmission overhead while maintaining robust image recovery under low-altitude channel conditions. Extensive experiments on real-world aerial image datasets demonstrate that the proposed E2E design significantly outperforms existing baselines, delivering superior transmission performance and accurate 3D scene reconstructions.