M^3: Dense Matching Meets Multi-View Foundation Models for Monocular Gaussian Splatting SLAM

• URL: http://arxiv.org/abs/2603.16844v1
• Date: Tue, 17 Mar 2026 17:52:37 GMT
• Title: M^3: Dense Matching Meets Multi-View Foundation Models for Monocular Gaussian Splatting SLAM
• Abstract summary: M3 augments the Multi-view foundation model with a dedicated Matching head to facilitate fine-grained dense correspondences.<n>Experiments on diverse indoor and outdoor benchmarks demonstrate state-of-the-art accuracy in both pose estimation and scene reconstruction.
• Score: 36.28718478194476
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Streaming reconstruction from uncalibrated monocular video remains challenging, as it requires both high-precision pose estimation and computationally efficient online refinement in dynamic environments. While coupling 3D foundation models with SLAM frameworks is a promising paradigm, a critical bottleneck persists: most multi-view foundation models estimate poses in a feed-forward manner, yielding pixel-level correspondences that lack the requisite precision for rigorous geometric optimization. To address this, we present M^3, which augments the Multi-view foundation model with a dedicated Matching head to facilitate fine-grained dense correspondences and integrates it into a robust Monocular Gaussian Splatting SLAM. M^3 further enhances tracking stability by incorporating dynamic area suppression and cross-inference intrinsic alignment. Extensive experiments on diverse indoor and outdoor benchmarks demonstrate state-of-the-art accuracy in both pose estimation and scene reconstruction. Notably, M^3 reduces ATE RMSE by 64.3% compared to VGGT-SLAM 2.0 and outperforms ARTDECO by 2.11 dB in PSNR on the ScanNet++ dataset.

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