IMGS-SLAM: Monocular Gaussian Splatting SLAM for Indoor Reconstruction

Abstract

Existing neural SLAM and 3D Gaussian Splatting (3DGS) SLAM systems often suffer from insufficient observations during indoor turn-arounds and large viewpoint changes, leading to incomplete coverage and missing details in corner regions. We propose IMGS-SLAM, a monocular Gaussian SLAM system tailored for indoor reconstruction, which improves mapping completeness and visual detail fidelity using only RGB images while maintaining competitive tracking accuracy. The method leverages a learning-based dense SLAM frontend to provide camera poses and dense geometric priors, and adopts 3DGS as the map representation. We propose a coverage-aware Dual-Cue mapping-frame selection strategy that decouples tracking keyframes from mapping frames and selects views with high expected coverage gain. This design improves map completeness under indoor turn-around motions and large viewpoint changes, while online-to-offline refinement further improves visual consistency and local details. In addition, we introduce quadtree-guided structured initialization and a high-frequency weighted loss to enhance textures and edge details, and incorporate co-visibility-constrained densification and pruning to reduce artifacts. Experiments on Replica, TUM RGB-D, and ScanNet demonstrate improved map completeness and competitive rendering quality in both synthetic and real indoor scenes.