论文标题
Blendtorch：实时自适应域随机库
BlendTorch: A Real-Time, Adaptive Domain Randomization Library
论文作者
论文摘要
通过深度学习技术解决复杂的计算机视觉任务取决于大量（有监督的）图像数据，通常在工业环境中不可用。缺乏培训数据开始阻碍计算机愿景中最先进的方法转移到工业应用中。我们介绍了自适应域随机化（DR）库Blendtorch，以帮助创建无限的合成训练数据流。 Blendtorch通过大量随机对低保真模拟进行随机生成数据，并照顾分发人工培训数据，以实时进行模型学习。我们表明，经过Blendtorch训练的模型在工业对象检测任务中反复的表现要比在真实或照片现实数据集中训练的模型更好。
Solving complex computer vision tasks by deep learning techniques relies on large amounts of (supervised) image data, typically unavailable in industrial environments. The lack of training data starts to impede the successful transfer of state-of-the-art methods in computer vision to industrial applications. We introduce BlendTorch, an adaptive Domain Randomization (DR) library, to help creating infinite streams of synthetic training data. BlendTorch generates data by massively randomizing low-fidelity simulations and takes care of distributing artificial training data for model learning in real-time. We show that models trained with BlendTorch repeatedly perform better in an industrial object detection task than those trained on real or photo-realistic datasets.
