Mono3DGS-SLAM: Reliability-Aware Geometric Canonicalization for Monocular Gaussian–TSDF Reconstruction

Monocular Gaussian SLAM must recover camera motion, surface structure, and appearance from an RGB sequence without metric depth input or benchmark geometry during reconstruction. This setting is challenged by scale-ambiguous predictions, spatially varying reliability, and the tendency of an unconstrained Gaussian map to absorb pose and depth errors into its geometric and appearance parameters. We present Mono3DGS-SLAM, a reliability-guided Gaussian–TSDF framework with a sequence-precalibrated prediction stage and an incremental mapping backend. Deterministic reliability gates select admissible observations, while one frozen scale jointly transports predicted depth, camera translation, and length-valued mapping parameters into an internally consistent canonical coordinate. A confidence-weighted colorized TSDF stores the persistent surface, base color, and visibility support. A capacity-bounded Gaussian layer is restricted to reliable residual regions and primarily restores appearance detail over the fused surface. On Replica, Mono3DGS-SLAM records 0.249 cm mean ATE in the evaluated runs and has lower ATE in five of eight scenes within the locally evaluated monocular group. With identical poses, depths, and views, the hybrid output reaches 30.678 dB PSNR in a paired comparison of outputs from the same hybrid runs. This comparison does not establish superiority over independently optimized single-representation systems. The evaluated full pipeline uses future observations and does not establish online or real-time SLAM performance. ScanNet and TUM-RGBD results further assess transfer to real monocular sequences.