Unsupervised Monocular Depth Estimation

Unsupervised monocular depth estimation aims to reconstruct 3D scene depth from a single 2D image without relying on labeled training data, a significant challenge in computer vision. Current research focuses on improving accuracy and robustness by leveraging techniques like generative models (e.g., diffusion models), incorporating camera intrinsic parameters as prior knowledge, and refining depth estimation through dense correspondence analysis and flow-guided optimization. These advancements have implications for various applications, including robotics, autonomous driving, and medical imaging, by enabling more efficient and reliable 3D scene understanding from readily available monocular video data.