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VGGT-GS SLAM: Uncalibrated Monocular Gaussian Splatting SLAM with Feed-Forward Priors
YH

Yuhang Han, Hao Wang, Jiaxi Cao, Xingyu Liu

· 1 min read

ResearcharXiv cs.CV

VGGT-GS SLAM: Uncalibrated Monocular Gaussian Splatting SLAM with Feed-Forward Priors

arXiv:2609.19628v1 Announce Type: new Abstract: We present VGGT-GS SLAM, a monocular 3D Gaussian Splatting SLAM system designed for uncalibrated videos. Starting from feed-forward VGGT pose and depth priors, our system performs submap differentiable bundle adjustment that jointly refines camera poses and a 3D Gaussian map, while optimizing submap-shared intrinsics and radial--tangential distortion through analytic calibration Jacobians. To improve global consistency, we introduce Gaussian-native alignment (GNA) for camera-anchored scale refinement between sequential submaps and verification of loop-closure candidates. Extensive experiments on standard indoor benchmarks show consistent improvements in localization accuracy and strong rendering quality under uncalibrated settings, establishing a strong baseline for uncalibrated Gaussian SLAM.

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This story was published by arXiv cs.CV and written by Yuhang Han, Hao Wang, Jiaxi Cao, Xingyu Liu. SyncAI.news shows a preview; the complete article is on the publisher's site.

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