It is difficult to collect data on a large scale in a monocular depth estimation because the task requires the simultaneous acquisition of RGB images and depths. Data augmentation is thus important to this task. However, there has been little research on data augmentation for tasks such as monocular depth estimation, where the transformation is performed pixel by pixel. In this paper, we propose a data augmentation method, called CutDepth. In CutDepth, part of the depth is pasted onto an input image during training. The method extends variations data without destroying edge features. Experiments objectively and subjectively show that the proposed method outperforms conventional methods of data augmentation. The estimation accuracy is improved with CutDepth even though there are few training data at long distances.

翻译：在单层深度估计中,很难大规模收集数据,因为任务要求同时获取 RGB 图像和深度。 因此,数据增强对这项任务很重要。 但是,对于单层深度估计等任务的数据增强问题,没有多少研究,例如单层深度估计,通过像素进行变换。在本文中,我们提出了一个数据增强方法,称为CutDepeh。在CutDepeh,深度的一部分在培训期间被粘贴在输入图像上。该方法在不破坏边缘特征的情况下扩展变异数据。客观和主观地实验表明,拟议的方法优于传统的数据增强方法。与CutDept相比,估计的准确性得到了提高,尽管远距离的培训数据很少。