Reading a Ruler in the Wild

• URL: http://arxiv.org/abs/2507.07077v1
• Date: Wed, 09 Jul 2025 17:35:58 GMT
• Title: Reading a Ruler in the Wild
• Authors: Yimu Pan, Manas Mehta, Gwen Sincerbeaux, Jeffery A. Goldstein, Alison D. Gernand, James Z. Wang,
• Abstract summary: Accurately converting pixel measurements into absolute real-world dimensions remains a fundamental challenge in computer vision.<n>We introduce RulerNet, a deep learning framework that robustly infers scale "in the wild"<n> Experiments show that RulerNet delivers accurate, consistent, and efficient scale estimates under challenging real-world conditions.
• Score: 1.4785540163232234
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Accurately converting pixel measurements into absolute real-world dimensions remains a fundamental challenge in computer vision and limits progress in key applications such as biomedicine, forensics, nutritional analysis, and e-commerce. We introduce RulerNet, a deep learning framework that robustly infers scale "in the wild" by reformulating ruler reading as a unified keypoint-detection problem and by representing the ruler with geometric-progression parameters that are invariant to perspective transformations. Unlike traditional methods that rely on handcrafted thresholds or rigid, ruler-specific pipelines, RulerNet directly localizes centimeter marks using a distortion-invariant annotation and training strategy, enabling strong generalization across diverse ruler types and imaging conditions while mitigating data scarcity. We also present a scalable synthetic-data pipeline that combines graphics-based ruler generation with ControlNet to add photorealistic context, greatly increasing training diversity and improving performance. To further enhance robustness and efficiency, we propose DeepGP, a lightweight feed-forward network that regresses geometric-progression parameters from noisy marks and eliminates iterative optimization, enabling real-time scale estimation on mobile or edge devices. Experiments show that RulerNet delivers accurate, consistent, and efficient scale estimates under challenging real-world conditions. These results underscore its utility as a generalizable measurement tool and its potential for integration with other vision components for automated, scale-aware analysis in high-impact domains. A live demo is available at https://huggingface.co/spaces/ymp5078/RulerNet-Demo.

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