Training data is an essential resource for creating capable and robust vision systems which are integral to the proper function of many robotic systems. Synthesized training data has been shown in recent years to be a viable alternative to manually collecting and labelling data. In order to meet the rising popularity of synthetic image training data we propose a framework for defining synthetic image data pipelines. Additionally we survey the literature to identify the most promising candidates for components of the proposed pipeline. We propose that defining such a pipeline will be beneficial in reducing development cycles and coordinating future research.

翻译：训练数据是构建具备能力且鲁棒的视觉系统的关键资源，这些系统对众多机器人系统的正常运行至关重要。近年来，合成训练数据已被证明是手动收集和标注数据的可行替代方案。为应对合成图像训练数据日益增长的需求，本文提出一个用于定义合成图像数据管道的框架。此外，我们通过文献调研，识别出该管道组件中最具潜力的候选方法。我们认为，定义此类管道将有助于缩短开发周期并协调未来的研究方向。