构建高效合成图像数据流水线，加速视觉机器人系统训练
Towards an Efficient Synthetic Image Data Pipeline for Training Vision-Based Robot Systems
- 提出合成图像数据流水线框架，整合关键组件
- 通过文献调研筛选最优组件，提升数据质量与效率
- 适合机器人视觉系统开发者与研究者参考使用
训练数据是构建强大且鲁棒的视觉系统的关键资源，而这类系统对许多机器人系统至关重要。近年来，合成训练数据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，本文提出一种定义合成图像数据流水线的框架，并通过文献调研识别出该流水线中最具前景的组件。我们认为，明确此类流水线将有助于缩短开发周期，并协调未来的研究工作。
原文摘要 · Abstract (English)
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.
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