Disparity estimation of stereo-endoscopic images using deep generative network

IF 4.1 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Bo Yang , Siyuan Xu , Lirong Yin , Chao Liu , Wenfeng Zheng
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引用次数: 0

Abstract

A novel disparity estimation pipeline is proposed for 3D reconstruction of dynamic soft tissues in minimally invasive surgery (MIS), which uses a deep generative network to learn manifold distributions of reasonable disparity maps from past stereo images in the training phase, and transforms stereo matching into an optimization problem with respect to the low-dimensional latent vector of the learned generator in the application phase. The proposed pipeline is particularly suitable for dynamic MIS scenarios with insufficient training data, as the photometric loss is explicitly used in the application phase and the scenario priors are introduced via a deep generative network.
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来源期刊
ICT Express
ICT Express Multiple-
CiteScore
10.20
自引率
1.90%
发文量
167
审稿时长
35 weeks
期刊介绍: The ICT Express journal published by the Korean Institute of Communications and Information Sciences (KICS) is an international, peer-reviewed research publication covering all aspects of information and communication technology. The journal aims to publish research that helps advance the theoretical and practical understanding of ICT convergence, platform technologies, communication networks, and device technologies. The technology advancement in information and communication technology (ICT) sector enables portable devices to be always connected while supporting high data rate, resulting in the recent popularity of smartphones that have a considerable impact in economic and social development.
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