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

• URL: http://arxiv.org/abs/2601.00705v1
• Date: Sun, 28 Dec 2025 03:45:57 GMT
• Title: RGS-SLAM: Robust Gaussian Splatting SLAM with One-Shot Dense Initialization
• Authors: Wei-Tse Cheng, Yen-Jen Chiou, Yuan-Fu Yang,
• Abstract summary: RGS-SLAM is a robust Gaussian-splatting SLAM framework that replaces the residual-driven densification stage of GS-SLAM.<n>RGS-SLAM achieves competitive or superior localization and reconstruction accuracy compared with state-of-the-art Gaussian and point-based SLAM systems.
• Score: 1.1470070927586018
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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