Sim2Real：基于光线跟踪的自然场景文档区域随机化
Sim2Real Docs: Domain Randomization for Documents in Natural Scenes using Ray-traced Rendering
摘要：
在过去，数字化文档的计算机视觉系统可以依赖于系统捕获的高质量扫描。今天，涉及数字文档的交易更有可能始于非专业人士拍摄的手机照片上传。因此，用于文档自动化的计算机视觉现在必须考虑在自然场景上下文中捕获的文档。另一个挑战是，文档处理的任务目标可能是高度特定于用例的，这使得公开可用的数据集在其实用性方面受到限制，而手动数据标记也是昂贵的，并且在用例之间转换得很差。为了解决这些问题，我们创建了Sim2Real的框架，用于合成数据集并在自然场景中执行文档的域随机化。Sim2Real可使用Blender实现文档的编程3D渲染，Blender是一种用于3D建模和光线跟踪渲染的开源工具。通过使用渲染，模拟光，几何，相机和背景的物理相互作用，我们合成的文档数据集在一个自然场景的上下文中。每个渲染都与指定感兴趣的潜在特征的特定于用例的地面实况数据配对，从而产生无限的适合任务的训练数据。然后，机器学习模型的作用是解决由渲染流水线引起的逆问题。这样的模型可以通过微调或调整域随机化参数来进一步迭代真实世界的数据。
英文摘要：
In the past, computer vision systems for digitized documents could rely on systematically captured, high-quality scans. Today, transactions involving digital documents are more likely to start as mobile phone photo uploads taken by non-professionals. As such, computer vision for document automation must now account for documents captured in natural scene contexts. An additional challenge is that task objectives for document processing can be highly use-case specific, which makes publicly-available datasets limited in their utility, while manual data labeling is also costly and poorly translates between use cases. To address these issues we created Sim2Real Docs - a framework for synthesizing datasets and performing domain randomization of documents in natural scenes. Sim2Real Docs enables programmatic 3D rendering of documents using Blender, an open source tool for 3D modeling and ray-traced rendering. By using rendering that simulates physical interactions of light, geometry, camera, and background, we synthesize datasets of documents in a natural scene context. Each render is paired with use-case specific ground truth data specifying latent characteristics of interest, producing unlimited fit-for-task training data. The role of machine learning models is then to solve the inverse problem posed by the rendering pipeline. Such models can be further iterated upon with real-world data by either fine tuning or making adjustments to domain randomization parameters.
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。
