In order to use the navigation system effectively, distance information sensors such as depth sensors are essential. Since depth sensors are difficult to use in endoscopy, many groups propose a method using convolutional neural networks. In this paper, the ground truth of the depth image and the endoscopy image is generated through endoscopy simulation using the colon model segmented by CT colonography. Photo-realistic simulation images can be created using a sim-to-real approach using cycleGAN for endoscopy images. By training the generated dataset, we propose a quantitative endoscopy depth estimation network. The proposed method represents a better-evaluated score than the existing unsupervised training-based results.

翻译：为了有效使用导航系统,深度传感器等远程信息传感器是必不可少的。由于深度传感器在内镜检查中难以使用,许多团体提出使用进化神经网络的方法。在本文中,深度图像和内窥镜图像的地面真实性是通过内镜检查模拟生成的,其内镜检查模型由CT 结肠学分解,光-现实模拟图像可以用内镜检查图像的周期GAN模拟到真实性的方法生成。通过对生成的数据集进行培训,我们提出一个定量内镜检查深度估计网络。拟议方法比现有的未经监督的培训结果要好得多。