Unsupervised monocular depth learning using self-teaching and contrast-enhanced SSIM loss

In recent years, unsupervised learning has gained significant attention as a promising approach for monocular depth estimation. We propose an unsupervised monocular depth learning approach that combines self-teaching method and contrast-enhanced structural similarity (SSIM) loss. The self-teaching method involves learning from both a teacher and a student network. The teacher network generates a pseudo-ground truth depth map from non-augmented images and highest predicted resolution. The student network is trained on augmented images and related low resolution to supervise the training process. This approach allows the student network to learn from the teacher network and improves the quality of the depth predictions. In addition, we introduce a new contrast-enhanced version of the SSIM loss that improves the learning process by emphasizing the contrast between the predicted depth and the ground truth. Our experiments on the KITTI datasets demonstrate that our approach outperforms many unsupervised depth learning methods.