{MonoEM}-{GS}: Monocular Expectation-Maximization Gaussian Splatting {SLAM}

Feed-forward geometric foundation models can infer dense point clouds and camera motion directly from {RGB} streams, providing priors for monocular {SLAM}. However, their predictions are often view-dependent and noisy: geometry can vary across viewpoints and under image transformations, and local metric properties may drift between frames. We present {MonoEM}-{GS}, a monocular mapping pipeline that integrates such geometric predictions into a global Gaussian Splatting representation while explicitly addressing these inconsistencies. {MonoEM}-{GS} couples Gaussian Splatting with an Expectation–Maximization formulation to stabilize geometry, and employs {ICP}-based alignment for monocular pose estimation. Beyond geometry, {MonoEM}-{GS} parameterizes Gaussians with multi-modal features, enabling in-place open-set segmentation and other downstream queries directly on the reconstructed map. We evaluate {MonoEM}-{GS} on 7-Scenes, {TUM} {RGB}-D and Replica, and compare against recent baselines.