Depth Consistency

Depth consistency in computer vision focuses on generating accurate and temporally coherent depth maps from various image sources, aiming to overcome limitations of existing monocular and multi-view depth estimation methods. Current research emphasizes developing novel architectures, such as diffusion models and transformer networks, often incorporating data fusion techniques (e.g., camera-radar) and employing innovative loss functions to enforce geometric and photometric consistency across multiple views or time frames. These advancements are crucial for improving the reliability of 3D scene understanding in applications like autonomous driving, robotics, and augmented reality, where accurate and consistent depth information is essential.