Hierarchical neural networks with large numbers of layers are the state of the art for most computer vision problems including image classification, multi-object detection and semantic segmentation. While the computational demands of training such deep networks can be addressed using specialized hardware, the availability of training data in sufficient quantity and quality remains a limiting factor. Main reasons are that measurement or manual labelling are prohibitively expensive, ethical considerations can limit generating data, or a phenomenon in questions has been predicted, but not yet observed. In this position paper, we present the Digital Reality concept are a structured approach to generate training data synthetically. The central idea is to simulate measurements based on scenes that are generated by parametric models of the real world. By investigating the parameter space defined of such models, training data can be generated in a controlled way compared to data that was captured from real world situations. We propose the Digital Reality concept and demonstrate its potential in different application domains, including industrial inspection, autonomous driving, smart grid, and microscopy research in material science and engineering.

中文翻译：

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数字现实：基于模型的从合成数据进行监督学习的方法

对于大多数计算机视觉问题，包括图像分类，多对象检测和语义分割，具有大量层的分层神经网络是最新技术。尽管可以使用专用硬件来解决训练此类深度网络的计算需求，但训练数据的数量和质量的可用性仍然是一个限制因素。主要原因是测量或手动标记过于昂贵，出于道德考虑可能会限制生成数据，或者已经预料到但尚未发现有问题的现象。在本立场文件中，我们提出了数字现实概念，这是一种综合生成训练数据的结构化方法。中心思想是基于现实世界的参数模型生成的场景来模拟测量。通过调查此类模型定义的参数空间，与从现实情况中捕获的数据相比，可以以受控方式生成训练数据。我们提出了数字现实概念，并展示了其在不同应用领域中的潜力，包括工业检查，自动驾驶，智能电网以及材料科学和工程学中的显微镜研究。