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By synthesizing diverse training data and leveraging pixel-wise calibration, FoundationGeo achieves unprecedented robustness in monocular metric geometry, even under varying camera intrinsics.
Models ace existing claim verification benchmarks by only checking the most obvious constraint, and fall apart when forced to consider multiple constraints together.
