For decades, stereo matching has been approached by developing hand-crafted algorithms, focused on measuring the visual appearance between local patterns in the two images and propagating this information globally. Since 2015, deep learning led to a paradigm shift in this field, driving the community to the design of end-to-end deep networks capable of matching pixels. The results of this revolution brought stereo matching to a whole new level of accuracy, yet not without any drawbacks. Indeed, some hard challenges remained unsolved by the first generation of deep stereo models, as they were often not capable of properly generalizing across different domains -- e.g., from synthetic to real, from indoor to outdoor -- or dealing with high-resolution images.

This was, however, three years ago. These and other challenges have been faced by the research community in the Twenties, making deep stereo matching even more mature and suitable to be a practical solution for everyday applications. For instance, now we have networks capable of generalizing much better from synthetic to real images, as well as handling high-resolution images or even estimating disparity correctly in the presence of non-Lambertian surfaces -- known to be among the ill-posed challenges for stereo. Accordingly, in this tutorial, we aim at giving a comprehensive overview of the state-of-the-art of deep stereo matching, which architectural designs have been crucial to reach this level of maturity and how to select the best solution for estimating depth from stereo in real applications.