Depth Estimation Model

Monocular depth estimation aims to reconstruct 3D scene depth from a single 2D image, a crucial task for applications like autonomous driving and robotics. Current research focuses on improving accuracy and robustness, particularly addressing challenges like viewpoint changes, limited training data, and adverse weather conditions, employing techniques such as autoregressive refinement, diffusion models, and transfer learning with architectures like EfficientNet and Vision Attention Networks. These advancements are significant because reliable depth estimation is essential for safe and efficient operation of numerous computer vision systems.