# Large-scale Photorealistic Outdoor 3D Scene Reconstruction from UAV Imagery Using Gaussian Splatting Techniques

arXiv: https://arxiv.org/abs/2602.20342 · HTML: https://mlsanity.com/paper/2602.20342/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 54 references that are in the map.
- First posted on arXiv 23 Feb 2026.

## Builds on (most cited first, 22 of 22)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields](https://mlsanity.com/paper/2111.12077/) (2021)
- [Panoptic Segmentation](https://mlsanity.com/paper/1801.00868/) (2018)
- [Feature 3DGS: Supercharging 3D Gaussian Splatting to Enable Distilled Feature Fields](https://mlsanity.com/paper/2312.03203/) (2023)
- [GaussianAvatars: Photorealistic Head Avatars with Rigged 3D Gaussians](https://mlsanity.com/paper/2312.02069/) (2023)
- [ASH: Animatable Gaussian Splats for Efficient and Photoreal Human Rendering](https://mlsanity.com/paper/2312.05941/) (2023)
- [Multimodal Explainable Artificial Intelligence: A Comprehensive Review of Methodological Advances and Future Research Directions](https://mlsanity.com/paper/2306.05731/) (2023)
- [A comprehensive survey of research towards AI-enabled unmanned aerial systems in pre-, active-, and post-wildfire management](https://mlsanity.com/paper/2401.02456/) (2024)
- [DroneSplat: 3D Gaussian Splatting for Robust 3D Reconstruction from In-the-Wild Drone Imagery](https://mlsanity.com/paper/2503.16964/) (2025)
- [Gaussian Splatting Decoder for 3D-aware Generative Adversarial Networks](https://mlsanity.com/paper/2404.10625/) (2024)
- [Self-supervised visual learning in the low-data regime: a comparative evaluation](https://mlsanity.com/paper/2404.17202/) (2024)
- [DRAGON: Drone and Ground Gaussian Splatting for 3D Building Reconstruction](https://mlsanity.com/paper/2407.01761/) (2024)
- [Survey on Hand Gesture Recognition from Visual Input](https://mlsanity.com/paper/2501.11992/) (2025)
- [Towards open and expandable cognitive AI architectures for large-scale multi-agent human-robot collaborative learning](https://mlsanity.com/paper/2012.08174/) (2020)
- [User profile-driven large-scale multi-agent learning from demonstration in federated human-robot collaborative environments](https://mlsanity.com/paper/2103.16434/) (2021)
- [Enhanced 3D Urban Scene Reconstruction and Point Cloud Densification using Gaussian Splatting and Google Earth Imagery](https://mlsanity.com/paper/2405.11021/) (2024)
- [Exploring Energy Landscapes for Minimal Counterfactual Explanations: Applications in Cybersecurity and Beyond](https://mlsanity.com/paper/2503.18185/) (2025)
- [Distributed maze exploration using multiple agents and optimal goal assignment](https://mlsanity.com/paper/2405.20232/) (2024)
- [TRIFFID: Autonomous Robotic Aid For Increasing First Responders Efficiency](https://mlsanity.com/paper/2502.09379/) (2025)
- [Object-centric Reconstruction and Tracking of Dynamic Unknown Objects using 3D Gaussian Splatting](https://mlsanity.com/paper/2405.20104/) (2024)

## Abstract (arXiv)

In this study, we present an end-to-end pipeline capable of converting drone-captured video streams into high-fidelity 3D reconstructions with minimal latency. Unmanned aerial vehicles (UAVs) are extensively used in aerial real-time perception applications. Moreover, recent advances in 3D Gaussian Splatting (3DGS) have demonstrated significant potential for real-time neural rendering. However, their integration into end-to-end UAV-based reconstruction and visualization systems remains underexplored. Our goal is to propose an efficient architecture that combines live video acquisition via RTMP streaming, synchronized sensor fusion, camera pose estimation, and 3DGS optimization, achieving continuous model updates and low-latency deployment within interactive visualization environments that supports immersive augmented and virtual reality (AR/VR) applications. Experimental results demonstrate that the proposed method achieves competitive visual fidelity, while delivering significantly higher rendering performance and substantially reduced end-to-end latency, compared to NeRF-based approaches. Reconstruction quality remains within 4-7% of high-fidelity offline references, confirming the suitability of the proposed system for real-time, scalable augmented perception from aerial platforms.