Depth Estimate

Estimate depth from a single image with machine-learning inference.

What it does

Depth Estimate reads a single image and predicts a depth map from it with an ML model, needing no camera, sensor or 3D data. The result is a greyscale image where brightness encodes distance: by default dark is near, light is far.

Quality picks the model size: fast is quickest, accurate takes longer and resolves finer detail. Metric switches the model to output depth in real metres instead of a relative near/far range. Invert flips the brightness mapping so near reads as white.

When to use it

A flat photo or video frame needing a depth map to drive a 2.5D parallax push with Displace
A depth-based mask (near/far selection, fog falloff, focus falloff) without manual painting
A depth signal into a Color Map node for a heat-map style visualisation
Real-world distances rather than relative near/far, with Metric on

Parameter	Type	Default
`image`	OneOf([FieldOf(Raster)])	—
`quality`	String	"fast"
`metric`	Boolean	false
`invert`	Boolean	false
`enabled`	Boolean	true
`position`	Vec3	(0, 0, 0)
`strength`	Number	1

Gotchas

Depth is estimated per image, with no notion of a previous frame, so on video the depth map can flicker slightly frame to frame rather than tracking smoothly as a real depth sensor would.
Where a downstream effect expects near-white instead of near-black, use Invert rather than a separate levels flip afterwards.

Accurate quality can take roughly 5–6 times longer per frame than Fast. Use it on stills or final renders, not on a live feed being scrubbed in real time.

Worked example

Add a Depth Estimate node and wire an image into Image.
Leave Quality on fast while setting things up, and switch to accurate for the final look.
Wire depth_map into Displace to push pixels toward camera by estimated depth, creating a 2.5D parallax effect.
If the parallax reads backwards, turn on Invert to flip near/far.

Depth Estimate

What it does

When to use it

Gotchas

Worked example

See also