Depth Estimation
Working out how far away things are from an image — and from a single photo, the absolute scale is mathematically unknowable.
Reviewed July 14, 2026Stable
When not to use it
- For absolute measurement, from one image. Scale is destroyed by projection. This isn't fixable.
- For robot navigation or obstacle avoidance, alone. A dollhouse and a room are pixel-identical.
- On reflective or transparent surfaces. The model predicts the reflection's depth, confidently.
- Expecting stereo accuracy at distance. It degrades with distance squared.
Reach for something else instead
- Stereo — a known baseline is an external ruler. Real metres, good to a few of them.
- LiDAR / time-of-flight — measure it. Expensive and correct.
- Structure from motion — many views, and you get scale if you know the camera's movement.
- A known reference object — if there's an A4 sheet in frame, you have scale.
Further reading
- Ranftl et al. (2022), Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer — MiDaS; the scale-invariant loss that encodes the impossibility.
- Godard et al. (2019), Digging Into Self-Supervised Monocular Depth Estimation — monodepth2; supervision from geometry, no labels.
- Eigen, Puhrsch & Fergus (2014), Depth Map Prediction from a Single Image using a Multi-Scale Deep Network — the paper that started it.
Primary sources, listed so you can check the claims on this page rather than take them on trust.
Where people go wrong
- Reading monocular depth as metres. It's relative, and the absolute is unknowable from one image.
- Building navigation on it. That's the case where the ambiguity has consequences.
- Thinking a bigger model fixes scale. Projection destroyed the information; there's nothing to recover.
- Treating the output as a measurement. It's a prior about what scenes usually look like.
At a glance
FieldComputer Vision
What it givesrelative depth; ordering, not metres
Why absolute is impossibleprojection destroys scale; a room and a dollhouse are pixel-identical
The elegant bitMiDaS's scale-invariant loss encodes the impossibility and unlocks the data
Neverbuild a robot on it alone
DifficultyIntermediate
Often compared with
Monocular vs. stereo depth — one is a very good prior about what scenes usually look like; the other is a measurement with a ruler. Only one is safe to drive into a wall with.
Where this sits
A destination. 9 concepts lead here, and nothing in the corpus depends on it.
6Levelsteps in
9Needs firstconcepts
0Opens upnothing further
1Areastays here
Learn these firstComputer Vision
Computed from the prerequisite graph, not assigned. How this works