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New dataset MetricScenes tackles scale-collapse in 3D geometry

By PulseAugur Editorial · [3 sources] ·

Researchers have developed a new dataset called MetricScenes to address the "scale-collapse" issue in monocular geometry estimation, where distant objects are inaccurately represented. This dataset, compiled from internet photos and stereo imagery, provides metrically-grounded, in-the-wild scenes. Fine-tuning the MoGe-2 model on MetricScenes significantly improves its accuracy in estimating absolute scale for unconstrained environments. AI

IMPACT Improves 3D scene understanding from monocular images, potentially aiding applications in robotics and augmented reality.

RANK_REASON The cluster contains an academic paper detailing a new dataset and model fine-tuning for a specific computer vision task.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

New dataset MetricScenes tackles scale-collapse in 3D geometry

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The cluster contains an academic paper detailing a new dataset and model fine-tuning for a specific computer vision task.
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COVERAGE [3]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    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…

  2. arXiv cs.CV TIER_1 English(EN) · Yuanbo Xiangli, Hanyu Chen, Xueqing Tsang, Noah Snavely ·

    Honey, I Shrunk the Arc de Triomphe!

    arXiv:2606.02379v1 Announce Type: new Abstract: 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 landscap…

  3. arXiv cs.CV TIER_1 English(EN) · Noah Snavely ·

    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…

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