Dense Stereo

Dense stereo aims to reconstruct highly detailed 3D models from pairs of stereo images, overcoming limitations of sparse methods. Current research focuses on improving accuracy and efficiency through novel neural network architectures, such as those incorporating implicit surface representations, differentiable RANSAC for pose estimation, and hybrid models combining deep learning with traditional computer vision techniques like multi-view stereo. These advancements enable applications ranging from robotic manipulation and augmented reality to high-resolution city modeling from satellite imagery, significantly impacting fields requiring precise 3D scene understanding.