Since 2015, there has been ongoing research on stereo learning. Siamese networks were initially employed to densely match the patches. Cost volume regularization-based stereo techniques have become more common since 2017. Reconstructing more complete 3D models was made possible by learning-based multi view stereo.Approaches based on differentiable rendering and neural rendering have recently gained in popularity. Utilizing positional encoding and volumetric rendering, it was feasible to reconstruct surfaces with non-Lambertian surfaces in addition to synthesizing novel viewpoints.We will go through the both paradigms’ most recent developments (stereo-based and neural rendering-based) during this talk.