The multiview module consists of:

  • a collection of solvers for 2 to n-view geometry constraints that arise in multiple view geometry,
  • a generic framework “Kernel” that can embed these solvers for robust estimation.

First accessible solvers are listed and explained and the “Kernel” concept is documented.

2-view solvers (2d-2d correspondences)

openMVG provides solver for the following geometric estimation:

  • affine,
  • homographic,
  • fundamental,
    • 7 to n pt,
    • 8 to n pt (Direct Linear Transform) [HZ].
  • essential,

N-View geometry estimation

  • Triangulation
    • 2 to n view (Direct Linear Transform),
    • 2 to n view (Iterated least square).
  • Rotation averaging
  • Translation averaging

Homography matrix:

The homography matrix maps the relation between two projections of a plane: Figure.

\(H\) is a (3 x 3) matrix that links coordinates in left and right images with the following relation.

(1)\[x'_i = Hx_i\]

The homography matrix and the point to point constraint.

OpenMVG implementation follows the DLT (Direct Linear Transform) explained in [HZ] book: \(H\) can be estimated from 4 to \(n\) corresponding points.

Fundamental matrix:

The fundamental matrix is a relation between two images viewing the same scene where those point’s projections are visible in the two images. Given a point correspondence between two views \((x_i,x'_i)\):

We obtain the following relation:

\[x'^T_i Fx_i = 0\]

\(F\) is the (3 x 3) Fundamental matrix, it puts in relation a point x to a line where belong the projection of the 3D X point. \(l'_i = Fx_i\) designs the epipolar line on which the point \(x'_i\) could be. The relation \(x'^T_i Fx_i = 0\) exists for all corresponding point belonging to a stereo pair.


The fundamental matrix and the point to line constraint.

The fundamental matrix is sometime called bifocal-tensor, it is a 3 x 3 matrix of rank 2 with 7 degree of freedom. 8 ou 7 correspondences are sufficient to compute the \(F\) matrix. Implementation follows the DLT (Direct Linear Transform) explained in [HZ] book.

Relative pose estimation (Essential matrix)

Adding intrinsic parameters to the fundamental matrix gives a metric “object” that provides the following relation \(E = K'^T FK\) , this is the Essential relation explained by Longuet-Higgins in 1981 [Longuet]. This essential matrix links the relative position of the camera to the fundamental matrix relation.

\[E = R[t]x = K'^T FK\]

The essential matrix geometric relation.

Absolute pose estimation/Camera resection (Pose matrix)

Given a list of 3D-2D point correspondences it is possible to compute a camera pose estimation. It consists in estimating the camera parameters of the right camera that minimizes the residual error of the 3D points re-projections, it’s an optimization problem that trying to solve P parameters in order to minimize

\[min \sum^n_{i=1} x_i - P(X_i).\]

Resection/Pose estimation from 3D-2D correspondences.


Residual error.

openMVG provides 3 different solvers for this problem:

  • 6pt Direct Linear Transform [HZ],
  • 3pt with intrinsic EPnP [Ke],
  • 3pt with intrinsic P3P [Kneip].

Kernel concept

In order to use the solver in a generic robust estimation framework, we use them in conjuction with the Kernel class that allow to link:

  • data points,
    • the set of correspondences that are used for a robust estimation problem.
  • a model solver/estimator,
  • a metric to measure data fitting to a putative model.

The Kernel concept (the two view case).

MINIMUM_SAMPLES: The minimal number of point required for the model estimation, MAX_MODELS: The number of models that the minimal solver could return, A Solve function that estimates a model from MINIMUM_SAMPLES to n vector data.
An metric function that return the error of a sample data to the provided model.
Embed data (putative), the model estimator and the error model. This kernel is core brick used in the openMVG robust estimator framework.