XGMap: Evidence-Grounded Active Mapping with Foundation Geometry

Active mapping requires geometry that is useful for choosing future observations, not only accurate on a fixed image set. Feed-forward geometry models offer dense reconstruction from RGB inputs, yet integrating their predictions into a persistent planning state raises two distinct challenges: metric compatibility across causal inference windows and the interpretation of incomplete evidence. We introduce XGMap, an RGB-only active mapping framework built around causal, oracle-free geometry integration and evidence-grounded planning. The intended integration module reconciles predictions with known metric poses and past observations; the planning interface distinguishes observed surfaces, verified free space, and unknown regions while combining anticipated coverage with exploration value. To establish when these components help, our evaluation separates matched geometry and planner interventions from end-to-end baseline comparisons. It also distinguishes coverage measured from ground-truth observations at visited poses from the fidelity of the native reconstructed map.