Depth Estimation

Depth estimation recovers 3D structure from 2D images — a problem that haunted computer vision for decades before deep learning cracked monocular depth prediction. The field shifted dramatically with MiDaS (2019) showing that mixing diverse training data beats task-specific models, then again with Depth Anything (2024) proving foundation model scale changes everything. Modern systems achieve sub-5% relative error on NYU Depth V2, but real-world robustness — handling reflections, transparency, and extreme lighting — remains the frontier. Critical for autonomous driving, AR/VR, and robotics where accurate 3D perception is non-negotiable.