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This article proposes a novel approach for predicting depth from a single image in a self-supervised manner, specifically using generative adversarial networks (GANs) with enhancements to improve training performance. We train our model to generate disparity maps or inverse depth maps using left/right stereo image supervision rather than direct supervision from depth ground truth. We avoid the problem of training instability in standard GANs by employing a relativistic discriminator that evaluates how realistic an input image is. Since the reconstructed image quality determines the disparity map's accuracy, we adopt the GMSD, one of the standout IQA algorithms, as a part of the image reconstruction loss function. A VGG-19 pre-trained model-based perceptual loss is used to optimize our model in a feature space instead of pixel space and to enhance reconstructed images' perceptual quality with the learning-based perspective. Also, we add another symmetric generator that takes the right images as input, which helps to maintain the consistency of left/right views and enrich our training data with reasonable overhead on the training time. Our network is trained to predict depths of 640 &#x00D7; 192 images using the KITTI stereo dataset, which shows an outstanding performance compared to state-of-the-art works in various evaluation metrics. We verify our method's modules by performing an ablation study and evaluate the generalization performance on CityScapes and Make3D datasets.

Relativistic Approach for Training Self-Supervised Adversarial Depth Prediction Model Using Symmetric Consistency