Jiawei Tian1, Yu Zhou1, Xiaobing Chen2, Salman A. AlQahtani3, Hongrong Chen4, Bo Yang4,*, Siyu Lu4, Wenfeng Zheng3,4,*
CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 209-229, 2025, DOI:10.32604/cmes.2024.057032
- 17 December 2024
Abstract 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 More >
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