VarSplat: Uncertainty-aware 3D Gaussian Splatting for Robust RGB-D SLAM

• URL: http://arxiv.org/abs/2603.09673v1
• Date: Tue, 10 Mar 2026 13:42:58 GMT
• Title: VarSplat: Uncertainty-aware 3D Gaussian Splatting for Robust RGB-D SLAM
• Abstract summary: VarSplat is an uncertainty-aware 3DGS-SLAM system that explicitly learns per-splat appearance variance.<n>We show that VarSplat improves robustness and competitive or superior tracking, mapping, and novel view rendering compared to existing studies for dense RGB-D SLAM.
• Score: 2.5823344978857388
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Simultaneous Localization and Mapping (SLAM) with 3D Gaussian Splatting (3DGS) enables fast, differentiable rendering and high-fidelity reconstruction across diverse real-world scenes. However, existing 3DGS-SLAM approaches handle measurement reliability implicitly, making pose estimation and global alignment susceptible to drift in low-texture regions, transparent surfaces, or areas with complex reflectance properties. To this end, we introduce VarSplat, an uncertainty-aware 3DGS-SLAM system that explicitly learns per-splat appearance variance. By using the law of total variance with alpha compositing, we then render differentiable per-pixel uncertainty map via efficient, single-pass rasterization. This map guides tracking, submap registration, and loop detection toward focusing on reliable regions and contributes to more stable optimization. Experimental results on Replica (synthetic) and TUM-RGBD, ScanNet, and ScanNet++ (real-world) show that VarSplat improves robustness and achieves competitive or superior tracking, mapping, and novel view synthesis rendering compared to existing studies for dense RGB-D SLAM.

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