Single Image Depth Estimation

Single image depth estimation aims to reconstruct three-dimensional scene geometry from a single two-dimensional image, a challenging inverse problem with multiple potential solutions. Current research emphasizes improving model generalizability across diverse datasets (including indoor, outdoor, and medical imaging) and handling challenges like noisy labels, moving objects, and varying resolutions, often employing transformer-based architectures, meta-learning, and self-supervised learning techniques. These advancements are crucial for applications in robotics, augmented reality, autonomous driving, and medical imaging, where accurate depth perception is essential for safe and effective operation.