RGS-SLAM: Robust Gaussian Splatting SLAM with One-Shot Dense Initialization

cheng, Remi Chiou, YuanFu Yang

RGS-SLAM: Robust Gaussian Splatting SLAM with One-Shot Dense Initialization: 4 upvotes on Hugging Face Daily Papers, #16 of 20 papers on 2026-01-08. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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