MoonSplat: 基于Sim(3)全局优化的单目在线高斯泼溅
MoonSplat: Monocular Online Gaussian Splatting with Sim(3) Global Optimization
AI总结：
提出一种结合Sim(3)全局优化的在线体素化3DGS框架，通过颜色残差学习策略加速收敛，实现鲁棒的相机跟踪和全局闭环，在室内外数据集上达到SOTA性能。
摘要：
从单目图像序列进行在线3D重建是一个具有挑战性且持续的研究课题。3D高斯泼溅（3DGS）凭借其高质量的实时渲染能力，使得在线3D重建能够以更强的表达能力表示密集场景，因此在机器人、AR/VR等广泛应用中具有巨大潜力。然而，现有的在线3DGS方法仍面临一些关键挑战：由于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优化效率低下。为了解决这些问题，我们提出了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$\ ext{Sim}(3)$优化，能够实现可靠的相机跟踪以及针对相机位姿和体素化3DGS的高效全局闭环。为了加速体素化3DGS的收敛，我们进一步引入了一种颜色残差学习策略，这不仅提高了优化速度，还增强了渲染质量。在多种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和渲染质量方面均达到了最先进的性能，同时保持了实时效率。此外，我们基于所提出的方法开发并部署了一个真实的基于无人机的主动重建系统，验证了其在实际在线3D重建任务中的鲁棒性和泛化能力。我们的代码和数据可在该网址获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
