Online 3D reconstruction from monocular image sequences is a challenging and ongoing research topic. 3D Gaussian Splatting (3DGS), leveraging its high-quality real-time rendering capability, empowers online 3D reconstruction to represent dense scenes with enhanced expressiveness, and thus holds great promise for a wide range of applications such as robotics and AR/VR. However, existing online 3DGS methods still suffer from some key challenges: fragile camera pose estimation due to the lack of global optimization, and low optimization efficiency in large-scale or long-sequence scenarios. To address these issues, we propose a robust and efficient online voxelized 3DGS reconstruction framework integrated with global $\text{Sim}(3)$ optimization, which enables reliable camera tracking and efficient global loop closure for both camera poses and voxelized 3DGS. To accelerate the convergence of the voxelized 3DGS, we further introduce a color residual learning strategy, which not only boosts optimization speed but also enhances rendering quality. Extensive experiments on diverse indoor and outdoor datasets demonstrate that our method achieves state-of-the-art performance in both camera pose estimation accuracy and rendering quality, while retaining real-time efficiency. Additionally, we develop and deploy a real-world UAV-based active reconstruction system grounded on our proposed method, validating its robustness and generalizability for practical online 3D reconstruction tasks. Our code and data are available at https://github.com/TrickyGo/MoonSplat.

翻译：从单目图像序列进行在线三维重建是一个充满挑战且持续活跃的研究课题。三维高斯泼溅（3DGS）凭借其高质量实时渲染能力，使在线三维重建能够以更强的表达力表征密集场景，因此有望广泛应用于机器人、AR/VR等领域。然而，现有在线3DGS方法仍面临若干关键挑战：因缺乏全局优化导致的脆弱相机位姿估计，以及在大规模或长序列场景中的低优化效率。为解决这些问题，我们提出一种鲁棒且高效的在线体素化3DGS重建框架，该框架集成了全局$\text{Sim}(3)$优化，能够实现可靠的相机跟踪与高效的全局闭环校正——同时适用于相机位姿与体素化3DGS。为进一步加速体素化3DGS的收敛，我们引入颜色残差学习策略，该策略不仅提升了优化速度，还增强了渲染质量。在多样化室内外数据集上的大量实验表明，我们的方法在相机位姿估计精度与渲染质量上均达到了最先进水平，同时保持了实时效率。此外，我们基于所提方法开发并部署了真实的无人机主动重建系统，验证了其在实际在线三维重建任务中的鲁棒性与泛化能力。代码与数据已开源至https://github.com/TrickyGo/MoonSplat。