Honey, I Shrunk the Arc de Triomphe!

Metric scale monocular geometry estimation has seen signif-icant progress through large-scale data aggregation, yet current founda-tion models suffer from a persistent “scale-collapse” phenomenon: dis-tant landmarks and vast landscapes are metrically underestimated. Thisperformance gap stems from a training data bottleneck, where exist-ing metric-scale datasets are hardware-constrained to unvaried street-level LiDAR or short-range indoor scans, or consist of synthetic datathat lacks the semantic complexity of the physical world. To bridge thisgap, we curate a new metrically-grounded, in-the-wild dataset that wecall MetricScenes, gathered from a variety of sources including Inter-net photo collections and stereo imagery. We estimate camera poses andinitial depth maps for each scene using off-the-shelf methods, and re-cover absolute scale from geo-tagged metadata as well as known stereocamera baselines. We also improve the quality of depth maps derivedfrom MetricScenes via a new two-stage Poisson completion method. Fine-tuning MoGe-2 on our dataset significantly mitigates scale-collapse andachieves superior metric accuracy in unconstrained, open-domain sceneswhile maintaining state-of-the-art performance on standard benchmarks.Project page: https://metricscenes.github.io/.