A Novel Self-Supervised Learning Network for Binocular Disparity Estimation  

作　　者：Jiawei Tian Yu Zhou Xiaobing Chen Salman A.AlQahtani Hongrong Chen Bo Yang Siyu Lu Wenfeng Zheng 

机构地区：[1]Department of Computer Science and Engineering,Major in Bio Artificial Intelligence,Hanyang University,Ansan-si,15577,Republic of Korea [2]School of Electrical and Computer Engineering,Louisiana State University,Baton Rouge,LA 70803,USA [3]Department of Computer Engineering,College of Computer and Information Sciences,King Saud University,Riyadh,11574,Saudi Arabia [4]School of Automation,University of Electronic Science and Technology of China,Chengdu,610054,China

出　　处：《Computer Modeling in Engineering & Sciences》2025年第1期209-229,共21页工程与科学中的计算机建模（英文）

基　　金：Supported by Sichuan Science and Technology Program(2023YFSY0026,2023YFH0004);Supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korean government(MSIT)(No.RS-2022-00155885,Artificial Intelligence Convergence Innovation Human Resources Development(Hanyang University ERICA)).

摘　　要：Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical robots.This study proposes a novel end-to-end disparity estimation model to address these challenges.Our approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting interferences.This study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and efficiency.The model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video data.Experimental results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing parameters.Moreover,the model exhibited faster convergence during training,contributing to overall performance enhancement.This study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.

关 键 词：Parallax estimation parallax regression model self-supervised learning Pseudo-Siamese neural network pyramid dilated convolution binocular disparity estimation 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]