Learning Based Stereo

Learning-based stereo vision aims to accurately estimate depth from pairs of images by leveraging deep learning techniques, overcoming limitations of traditional methods. Current research focuses on improving accuracy and robustness in challenging scenarios (low texture, occlusions) through novel architectures incorporating geometric cues like surface normals and employing iterative refinement strategies with multi-peak matching and adaptive search ranges. These advancements lead to state-of-the-art performance on benchmark datasets and enable applications in areas such as autonomous driving, 3D scene reconstruction, and computer-assisted surgery, particularly by improving the accuracy and robustness of depth estimation in diverse settings.