M^3: Dense Matching Meets Multi-View Foundation Models for Monocular Gaussian Splatting SLAM
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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Kerui Ren
admin_assigned
2026-03-18T10:07:05.415Z
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Kerui Ren
cskrren
user
cskrren
69ba13a85d2b9c28ea09f0de
Guanghao Li
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Changjian Jiang
admin_assigned
2026-03-18T10:07:16.975Z
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Changjian Jiang
didohulk
user
didohulk
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Yingxiang Xu
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Tao Lu
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Linning Xu
69ba13a85d2b9c28ea09f0e3
Junting Dong
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Jiangmiao Pang
admin_assigned
2026-03-18T10:07:33.228Z
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Jiangmiao Pang
Jiangmiao
user
Jiangmiao
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Mulin Yu
69ba13a85d2b9c28ea09f0e6
Bo Dai
M^3: Dense Matching Meets Multi-View Foundation Models for Monocular Gaussian Splatting SLAM
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.
Multi-view foundation model enhanced with matching head and monocular Gaussian splatting SLAM achieves improved pose estimation and scene reconstruction accuracy.
Multi-view foundation models SLAM pose estimation dense correspondences monocular Gaussian Splatting dynamic area suppression cross-inference intrinsic alignment