A Novel Self-Supervised Learning Network for Binocular Disparity Estimation

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 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.