UVDoc：基于神经网格的文档展平方法
UVDoc: Neural Grid-based Document Unwarping
AI总结：
本文提出UVDoc方法，通过全卷积神经网络以双任务方式预测文档3D网格和2D展平网格，并发布伪照片级真实感UVDoc数据集，在DocUNet基准上取得最先进结果。
摘要：
从弯曲和褶皱页面的随手拍摄照片中恢复印刷文档原本平整的外观，是一个常见的日常问题。本文提出了一种新颖的基于网格的单图像文档展平方法。我们的方法通过一个全卷积深度神经网络进行几何畸变校正，该网络以双任务方式学习预测文档的3D网格以及相应的2D展平网格，隐式编码了3D纸张形状与其2D图像之间的耦合关系。为了使展平模型能够在比常用的合成Doc3D数据集外观更真实的数据上进行训练，我们创建并发布了自有数据集UVDoc，该数据集结合了伪照片级真实感的文档图像与物理精确的3D形状及展平函数标注。我们的数据集标注了训练展平网络所需的全部信息，无需单独设计损失函数来处理野外文档数据集中通常存在的真值缺失问题。我们进行了深入评估，结果表明，在引入我们新颖的伪照片级真实感数据集后，我们相对较小的网络架构在DocUNet基准上取得了最先进的结果。我们表明，UVDoc数据集的伪照片级真实感特性支持新的、更好的评估方法，例如光照校正的MS-SSIM。我们提供了一个新颖的基准数据集以促进此类评估，并提出了一种量化展平后线条直线度的指标。我们的代码、结果和UVDoc数据集可在https://github.com/tanguymagne/UVDoc获取。
英文摘要：
Restoring the original, flat appearance of a printed document from casual photographs of bent and wrinkled pages is a common everyday problem. In this paper we propose a novel method for grid-based single-image document unwarping. Our method performs geometric distortion correction via a fully convolutional deep neural network that learns to predict the 3D grid mesh of the document and the corresponding 2D unwarping grid in a dual-task fashion, implicitly encoding the coupling between the shape of a 3D piece of paper and its 2D image. In order to allow unwarping models to train on data that is more realistic in appearance than the commonly used synthetic Doc3D dataset, we create and publish our own dataset, called UVDoc, which combines pseudo-photorealistic document images with physically accurate 3D shape and unwarping function annotations. Our dataset is labeled with all the information necessary to train our unwarping network, without having to engineer separate loss functions that can deal with the lack of ground-truth typically found in document in the wild datasets. We perform an in-depth evaluation that demonstrates that with the inclusion of our novel pseudo-photorealistic dataset, our relatively small network architecture achieves state-of-the-art results on the DocUNet benchmark. We show that the pseudo-photorealistic nature of our UVDoc dataset allows for new and better evaluation methods, such as lighting-corrected MS-SSIM. We provide a novel benchmark dataset that facilitates such evaluations, and propose a metric that quantifies line straightness after unwarping. Our code, results and UVDoc dataset are available at https://github.com/tanguymagne/UVDoc.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
