用一次性匹配生成高精度初始点云，提升SLAM稳定性与速度
RGS-SLAM: Robust Gaussian Splatting SLAM with One-Shot Dense Initialization
- 通过多视角匹配直接生成初始高斯点，无需逐步优化
- 比传统方法快20%以上，纹理丰富场景重建更清晰
- 适合需要快速稳定建图的实时应用，兼容现有系统
我们提出RGS-SLAM，一种鲁棒的高斯溅射式SLAM框架。它将GS-SLAM中依赖残差的稠密化阶段替换为无需训练的对应关系到高斯点的初始化方法。该方法基于经置信度感知的内点分类器优化的DINOv3特征，一次性三角化密集多视图对应关系，生成分布良好且结构感知的高斯种子，再进行优化。此初始化显著提升了早期建图稳定性，加速收敛约20%，在纹理丰富和杂乱场景中获得更高渲染保真度，同时完全兼容现有GS-SLAM流程。在TUM RGB-D和Replica数据集上评估显示，RGS-SLAM在定位与重建精度上达到或优于当前最先进的高斯与点基SLAM系统，支持最高达925 FPS的实时建图性能。
原文摘要 · Abstract (English)
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/
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