Single View Depth Estimation Method

Single-view depth estimation aims to reconstruct three-dimensional scene geometry from a single 2D image, a challenging problem addressed by deep learning models. Current research focuses on improving accuracy and robustness, particularly by incorporating multi-view information to resolve ambiguities inherent in single-view data, developing novel loss functions and training strategies to leverage diverse datasets (including uncalibrated stereo data), and designing efficient architectures that handle various image types, such as panoramas. These advancements are crucial for applications in robotics, augmented reality, and 3D modeling, enabling more accurate and reliable scene understanding from readily available visual data.