Implicit Depth

Implicit depth methods represent a shift in computer vision, aiming to directly estimate depth or depth-related properties (like occlusion masks) without explicitly regressing depth values as an intermediate step. Current research focuses on developing robust and efficient implicit models, often leveraging deep equilibrium networks or diffusion models, to improve accuracy and temporal consistency, particularly in challenging scenarios like low-light conditions or noisy data. This approach shows promise for enhancing applications such as augmented reality (by improving virtual object occlusion), 3D scene reconstruction, and optical flow estimation, offering advantages in computational efficiency and accuracy compared to traditional explicit depth methods.