Reading a Ruler in the Wild
Journal:
arXiv
Published Date:
Jul 9, 2025
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.
