Planar Parallax

Planar parallax leverages the geometric relationship between image motion and depth, particularly on planar surfaces, to infer 3D scene structure from images or video. Current research focuses on improving depth estimation and 3D reconstruction accuracy using techniques like neural networks (e.g., U-Nets, transformer networks), incorporating geometric constraints (e.g., planar homographies), and refining existing methods for handling challenges such as motion blur, JPEG artifacts, and limited parallax in specific applications (e.g., autonomous driving). These advancements are significant for applications requiring accurate 3D scene understanding from monocular vision, including autonomous driving, robotics, and 3D modeling from readily available video sources.