SyntheticDoc: A Large Synthetic Dataset for Document Unwarping and Illumination Correction

2026-09-14

AI Summary

本文通过创建大规模高质量数据集SyntheticDoc，解决文档校正和光照修正训练数据不足的问题，使用物理模拟器生成和路径追踪渲染方法确保数据真实性。

Abstract

Deep learning models have become the standard tool for document rectification and illumination correction, yet their performance is fundamentally bound by their training data. For nearly a decade, the community has heavily relied on Doc3D, a pioneering but increasingly limited document unwarping dataset in terms of scale and quality. To address this bottleneck, we introduce SyntheticDoc, a massive, high-quality dataset designed to push the boundaries of document unwarping. SyntheticDoc is composed of 1,000,000 high-resolution procedurally generated training samples, alongside extensive validation and test sets. Each sample is paired with rich, pixel-perfect annotations, including UV maps, normal maps, albedo and shading. To ensure physical accuracy and photorealism, the paper geometries are generated via a physics-based simulator and rendered using a path tracer. To demonstrate the benefit of our dataset, we train a simple baseline model on SyntheticDoc and report on its performance in comparison to state-of-the-art methods on both document unwarping and illumination correction tasks. Our dataset is available at https://igl.ethz.ch/projects/SyntheticDoc/ and the code used to generate it at https://github.com/tanguymagne/SyntheticDoc .

Problem

• document unwarping
• illumination correction
• training data
• dataset scale
• quality

Innovation

• Synthetic Dataset
• Document Unwarping
• Illumination Correction
• Procedurally Generated
• Physics-based Simulator