Honey, I Shrunk the Arc de Triomphe!
Metric scale monocular geometry estimation has seen significant progress through large-scale data aggregation, yet current foundation models suffer from a persistent ''scale-collapse'' phenomenon: distant landmarks and vast landscapes are metrically underestimated. We hypothesize that this performance gap stems from a training data bottleneck, where existing metric-scale datasets are hardware-constrained to homogenous vehicle-captured LiDAR or short-range indoor scans, or consist of synthetic data that lacks the semantic complexity of the physical world. To bridge this gap, we curate a new metrically-grounded, in-the-wild dataset that we call MetricScenes, gathered from a variety of sourc...
| Authors | Yuanbo Xiangli, Hanyu Chen, Xueqing Tsang et al. |
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| Primary category | cs.CV |
| Published | 1 June 2026 |
| Last updated at source | 26 June 2026 |
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