What is meant by 'Depth Estimation' in computer vision?

Instruction: Define depth estimation and its significance in computer vision.

Context: This question probes the candidate's knowledge on how computer vision algorithms determine the distance of objects from the viewpoint.

Updated

Example Answer

Depth estimation needs a source of scale and a clear error model. Calibrated stereo uses the baseline and pixel disparity to estimate metric depth. A single image does not determine metric scale by geometry alone; a learned monocular model uses priors, and its metric accuracy depends on training, camera assumptions and deployment conditions. I would validate near and far objects separately and pass uncertainty or invalid regions onward rather than presenting every predicted depth as measured truth.

Make it your own

Illustrative check: compare a nearby textured object, a distant object, a reflective surface and an occluded region against an independent range reference. Keep camera calibration and units with the results.

Why this works

Low disparity magnifies depth error, while monocular priors can fail on unfamiliar scenes. Follow-up: what extra measurement would recover scale for a monocular reconstruction?

Sources: OpenCV stereo depth, OpenCV calibration and reconstruction.

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