Single View Depth

Single-view depth estimation aims to reconstruct three-dimensional scene geometry from a single 2D image, a fundamentally challenging inverse problem. Current research focuses on improving accuracy and robustness through techniques like fusing depth cues from different sources (e.g., defocus blur, focus stacking, and single-image priors), incorporating uncertainty quantification into model predictions, and leveraging self-supervised learning to address data scarcity. These advancements are crucial for applications such as augmented reality, robotics, and medical imaging, where accurate depth perception is essential for reliable operation and decision-making.