RGS-SLAM: Robust Gaussian Splatting SLAM with One-Shot Dense Initialization
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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Wei-Tse Cheng
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2026-01-08T08:31:41.027Z
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cheng
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Yen-Jen Chiou
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2026-01-09T08:36:18.687Z
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Remi Chiou
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Yuan-Fu Yang
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2026-01-13T15:45:50.207Z
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YuanFu Yang
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RGS-SLAM: Robust Gaussian Splatting SLAM with One-Shot Dense Initialization
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.
RGS-SLAM presents a robust Gaussian-splatting SLAM framework that uses dense multi-view correspondences and DINOv3 descriptors for efficient, stable mapping with improved rendering fidelity.
Gaussian-splatting SLAM densification Gaussian seed triangulation DINOv3 descriptors confidence-aware inlier classifier rendering fidelity real-time mapping