FoundationGeo: Learning Spatial Pixel-Wise Fields for Monocular Metric Geometry

We present FoundationGeo, a two-stage framework that ex-plicitly bridges relative and metric prediction via spatial calibration andprincipled data design. Stage 1 learns a high-fidelity, affine-invariant ge-ometry model by initializing with DINOv3 and training on a curated10.2M-sample multi-domain corpus with complementary local–detail su-pervision, yielding sharp boundaries and strong cross-domain general-ization. Stage 2 moves beyond global scaling by introducing lightweightpixel-wise calibration fields for metric estimation: a scale field for spa-tially varying metric alignment and a ray-direction correction field thatmitigates directional bias in point-map geometry, together producingmetrically consistent 3D point maps. Beyond model design, we identifycamera intrinsic coverage, especially focal length distribution mismatchbetween training and test data, as a key bottleneck for zero-shot met-ric generalization: performance drops sharply when test intrinsics falloutside the training distribution. To address this, we synthesize addi-tional training data across diverse focal lengths using a Blender-baseddata engine, repairing under-covered focal regimes and improving robust-ness under intrinsic shift. Extensive zero-shot evaluations across sevenbenchmarks show that FoundationGeo significantly strengthens cross-domain robustness, staying near the top across diverse domains whileavoiding the sharp cross-domain performance drops observed in othermethods. This consistency translates into the best overall performance,surpassing heavier baselines by over 5.2% on average. Project page:https://mx-liu6.github.io/FoundationGeo-web/