Monocular Depth

Monocular depth estimation aims to reconstruct three-dimensional scene depth from a single two-dimensional image, a challenging inverse problem due to inherent ambiguities. Current research focuses on improving accuracy and robustness, particularly in dynamic scenes and challenging conditions, using various deep learning architectures including convolutional neural networks, transformers, and diffusion models, often incorporating self-supervised learning and multi-view consistency constraints. These advancements have significant implications for robotics, autonomous driving, augmented reality, and 3D scene reconstruction, enabling more efficient and reliable perception systems.