Depth estimation of endoscopy using sim-to-real transfer

• URL: http://arxiv.org/abs/2112.13595v1
• Date: Mon, 27 Dec 2021 10:05:01 GMT
• Title: Depth estimation of endoscopy using sim-to-real transfer
• Authors: Bong Hyuk Jeong, Hang Keun Kim, and Young Don Son
• Abstract summary: In this paper, the ground truth of the depth image and the endoscopy image is generated through endoscopy simulation. By training the generated dataset, we propose a quantitative endoscopy depth estimation network.
• Score: 1.5293427903448025
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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