MonoEM-GS: 单目期望-最大化高斯点云SLAM
MonoEM-GS: Monocular Expectation-Maximization Gaussian Splatting SLAM
AI总结：
MonoEM-GS通过整合几何预测到全局高斯点云表示中，解决视点依赖性和噪声问题，结合期望-最大化方法稳定几何并采用ICP对齐估计姿态，支持多模式特征参数化实现开放集分割等下游任务。
摘要：
Feed-forward几何基础模型可通过RGB流直接推断密集点云和相机运动，为单目SLAM提供先验信息。然而，其预测通常视点依赖且噪声大：几何在不同视点和图像变换下可能变化，局部度量属性可能在帧间漂移。我们提出了MonoEM-GS，一种单目映射流程，将此类几何预测整合到全局高斯点云表示中，同时显式处理这些不一致。MonoEM-GS将高斯点云与期望-最大化方法结合以稳定几何，并采用基于ICP的对齐进行单目姿态估计。除几何外，MonoEM-GS用多模式特征参数化高斯点云，使能在重建地图上直接执行开放集分割等下游查询。我们在7-Scenes、TUM RGB-D和Replica数据集上评估MonoEM-GS，并与最近的基线进行比较。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
