用视觉代替激光雷达，实现低成本无人机3D重建
ExploreGS: a vision-based low overhead framework for 3D scene reconstruction
- 基于RGB图像与词袋模型，实现轻量级实时重建
- 在资源受限设备上实现与顶尖方法相当的重建质量
- 适合无人机等边缘设备，兼顾效率与精度
本文提出一种面向无人机的低开销视觉三维场景重建框架ExploreGS。该框架仅使用RGB图像，通过视觉模型替代传统的激光雷达点云采集过程，在降低成本的同时实现高质量重建。系统融合场景探索与模型重建，并利用词袋（Bag-of-Words, BoW）模型实现实时处理能力，使3D高斯溅射（3DGS）训练可在机载设备上执行。仿真与真实环境中的大量实验表明，ExploreGS在资源受限设备上具有高效性与适用性，重建质量可媲美当前最优方法。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。