Semi-supervised image depth prediction with deep learning and binocular algorithms

Combining the advantages and disadvantages of supervised learning and unsupervised learning strategies in convolution neural networks, this paper proposes a semi-supervised single-image depth prediction model based on binocular information and sparse laser data. The model improves the depth prediction accuracy by introducing sparse depth monitoring information, which provides a better convergence of the model with a local optimal solution. In the experiment, we validate the effectiveness of the model on the KITTI data set. Compared to the supervised algorithm, the root mean square error is reduced by 41.6% and, compared to the unsupervised algorithm, the root mean square error is reduced by 26.9%.