Multi Frame Depth

Multi-frame depth estimation aims to reconstruct 3D scene geometry from multiple images, improving upon single-image methods by leveraging geometric consistency across views. Current research focuses on self-supervised learning approaches, employing various architectures like cost volume fusion, transformer networks, and iterative refinement methods that combine monocular and multi-view cues to handle challenges posed by dynamic objects and low-textured areas. These advancements significantly enhance the accuracy and robustness of depth estimation, with implications for applications such as autonomous driving, robotics, and 3D scene understanding.