  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.
