RGS-SLAM: 基于单次密集初始化的鲁棒高斯点云SLAM
RGS-SLAM: Robust Gaussian Splatting SLAM with One-Shot Dense Initialization
AI总结：
RGS-SLAM通过单次密集初始化提升SLAM鲁棒性与效率，实现高保真渲染和实时映射
摘要：
我们介绍了RGS-SLAM，一种鲁棒的高斯点云SLAM框架，该框架将GS-SLAM中的残差驱动密集化阶段替换为无训练的对应关系到高斯初始化。不同于逐步添加高斯点作为残差揭示缺失几何，RGS-SLAM对来自DINOv3描述符经过置信度感知内点分类器优化的密集多视角对应关系进行单次三角化，生成一个分布良好且结构感知的高斯种子先验，以供优化。这种初始化稳定了早期映射并加速了收敛，大约提高了20%，在纹理丰富和杂乱的场景中实现了更高的渲染保真度，同时仍与现有的GS-SLAM流程完全兼容。在TUM RGB-D和Replica数据集上评估，RGS-SLAM在定位和重建精度上与最先进的高斯和点SLAM系统相比具有竞争力或更优，能够保持高达925 FPS的实时映射性能。更多细节和资源请访问此URL：https://breeze1124.github.io/rgs-slam-project-page/
英文摘要：
We introduce RGS-SLAM, a robust Gaussian-splatting SLAM framework that replaces the residual-driven densification stage of GS-SLAM with a training-free correspondence-to-Gaussian initialization. Instead of progressively adding Gaussians as residuals reveal missing geometry, RGS-SLAM performs a one-shot triangulation of dense multi-view correspondences derived from DINOv3 descriptors refined through a confidence-aware inlier classifier, generating a well-distributed and structure-aware Gaussian seed prior to optimization. This initialization stabilizes early mapping and accelerates convergence by roughly 20\%, yielding higher rendering fidelity in texture-rich and cluttered scenes while remaining fully compatible with existing GS-SLAM pipelines. Evaluated on the TUM RGB-D and Replica datasets, RGS-SLAM achieves competitive or superior localization and reconstruction accuracy compared with state-of-the-art Gaussian and point-based SLAM systems, sustaining real-time mapping performance at up to 925 FPS. Additional details and resources are available at this URL: https://breeze1124.github.io/rgs-slam-project-page/
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。
