Skip to content
opencv-wasm

Triangulation

Algorithm atlas3D vision

Recover a 3D point by intersecting the viewing rays implied by multiple camera observations.

Try it on your image ↓

VISUAL WALKTHROUGH3D vision
REFERENCE INPUTScene and camera observations

Two cameras observe the same scene point from different positions.

RESULTReconstruct the scene point

Convert homogeneous coordinates and check depth and reprojection error.

STEP 03 / 03

Convert homogeneous coordinates and check depth and reprojection error.

Illustrative example. The stages explain the method; they are not a live OpenCV execution.

Try it on an image

YOUR IMAGE · REAL OPENCV

Experiment at pixel level

Open full lab ↗

Triangulate editable correspondences with two parallel synthetic pinhole cameras. Focal length and baseline must match your rectified images for physical units to be meaningful.

The engine loads on your first run. Your images stay in this browser.

Input448 × 320
OutputWaiting for a result

Scroll over either image to zoom at the pointer. Use the scrollbars to pan both views over the same relative area. Zoom is relative to the input; pixel coordinates belong to each image. Warps can change scene correspondence.

Pixel inspector RGBA · native values · matched scale · 9 × 9 output pixels
Hover to inspect. Click to pin a pixel.
Input 
Select a pixel

Output 
Select a pixel

Sample models and licenses

When to use it

Reconstruct sparse geometry from matched points and known camera matrices.

How it works

  1. 01Back-project each observation through its camera model.
  2. 02Solve for the point best satisfying the projection equations.
  3. 03Convert homogeneous coordinates and check depth and reprojection error.

corresponding rays → least-squares 3D intersection

What to tune

Use well-conditioned views and reject points behind cameras or with poor reprojection consistency.

Where it breaks down

Nearly parallel rays create unstable depth. All observations and camera matrices must use compatible coordinate systems.

TypeScript API

Open an entry for its exact overloads, parameter descriptions, result ownership and pinned upstream source.

triangulatePointsReference →sfm_triangulatePointsReference →

All of these calls execute on the CPU. Native objects need explicit disposal. See matrices and ownership and build compatibility.

Camera calibration

Estimate a camera’s intrinsic parameters and lens distortion from known target geometry.

Lens undistortion

Invert a calibrated lens model to map distorted observations onto an idealized camera image.

Perspective-n-point pose

Estimate a camera-relative object pose from known 3D points and their 2D image observations.

Fundamental and essential matrices

Constrain where a point from one camera view can appear in another.