Monocular Depth Estimation

Monocular depth estimation aims to reconstruct three-dimensional scene depth from a single image, a challenging inverse problem due to the inherent loss of depth information during image formation. Current research focuses on improving accuracy and robustness, particularly in challenging scenarios like low-texture regions, viewpoint changes, and non-Lambertian surfaces, often employing deep learning models such as transformers and diffusion networks, along with techniques like multi-view rendering and radar fusion. These advancements have significant implications for various applications, including autonomous driving, robotics, and augmented reality, by enabling more accurate and reliable 3D scene understanding from readily available monocular vision data.