This paper proposes a low-overhead, vision-based 3D scene reconstruction framework for drones, named ExploreGS. By using RGB images, ExploreGS replaces traditional lidar-based point cloud acquisition process with a vision model, achieving a high-quality reconstruction at a lower cost. The framework integrates scene exploration and model reconstruction, and leverags a Bag-of-Words(BoW) model to enable real-time processing capabilities, therefore, the 3D Gaussian Splatting (3DGS) training can be executed on-board. Comprehensive experiments in both simulation and real-world environments demonstrate the efficiency and applicability of the ExploreGS framework on resource-constrained devices, while maintaining reconstruction quality comparable to state-of-the-art methods.
View on arXiv@article{feng2025_2505.10578,
title={ ExploreGS: a vision-based low overhead framework for 3D scene reconstruction },
author={ Yunji Feng and Chengpu Yu and Fengrui Ran and Zhi Yang and Yinni Liu },
journal={arXiv preprint arXiv:2505.10578},
year={ 2025 }
}