Finds an object pose from 3D-2D point correspondences

[rvec, tvec, success] = cv.solvePnP(objectPoints, imagePoints, cameraMatrix)
[...] = cv.solvePnP(..., 'OptionName', optionValue, ...)

Input

• objectPoints Array of object points in the object coordinate space, 1xNx3/Nx1x3 or Nx3 array, where N is the number of points, or cell array of length N of 3-element vectors can be also passed here {[x,y,z], ...}.
• imagePoints Array of corresponding image points, 1xNx2/Nx1x2 or Nx2 array, where N is the number of points, or cell array of length N of 2-element vectors can be also passed here {[x,y], ...}.
• cameraMatrix Input camera matrix A = [fx 0 cx; 0 fy cy; 0 0 1].

Output

• rvec Output rotation vector (see cv.Rodrigues) that, together with tvec, brings points from the model coordinate system to the camera coordinate system.
• tvec Output translation vector.
• success success logical flag.

Options

• DistCoeffs Input vector of distortion coefficients [k1,k2,p1,p2,k3,k4,k5,k6,s1,s2,s3,s4,taux,tauy] of 4, 5, 8, 12 or 14 elements. If the vector is empty, the zero distortion coefficients are assumed. default empty.
• Rvec Initial rvec. Not set by default.
• Tvec Initial tvec. Not set by default.
• UseExtrinsicGuess Parameter used for Method='Iterative'. If true, the function uses the provided rvec and tvec values as initial approximations of the rotation and translation vectors, respectively, and further optimizes them. default false.
• Method Method for solving the PnP problem. One of the following:
  • Iterative Iterative method is based on Levenberg-Marquardt optimization. In this case the function finds such a pose that minimizes reprojection error, that is the sum of squared distances between the observed projections imagePoints and the projected (using cv.projectPoints) objectPoints. This is the default.
  • P3P Method is based on the paper [gao2003complete]. In this case the function requires exactly four object and image points.
  • EPnP Method has been introduced in the paper [morenoepnp] and [lepetit2009epnp].
  • DLS Method is based on the paper of [hesch2011direct].
  • UPnP Method is based on the paper of [penate2013exhaustive]. In this case the function also estimates the parameters fx and fy assuming that both have the same value. Then the cameraMatrix is updated with the estimated focal length.

The function estimates the object pose given a set of object points, their corresponding image projections, as well as the camera matrix and the distortion coefficients.

References

[gao2003complete]:

X.S. Gao, X.R. Hou, J. Tang, H.F. Chang; "Complete Solution Classification for the Perspective-Three-Point Problem", IEEE Trans. on PAMI, vol. 25, No. 8, p. 930-943, August 2003.

[morenoepnp]:

F. Moreno-Noguer, V. Lepetit and P. Fua; "EPnP: Efficient Perspective-n-Point Camera Pose Estimation".

[lepetit2009epnp]:

V. Lepetit, F. Moreno-Noguer, P. Fua; "EPnP: An accurate O(n) solution to the PnP problem". IJCV, vol. 81, No. 2, p. 155-166, 2009

[hesch2011direct]:

Joel A. Hesch and Stergios I. Roumeliotis. "A Direct Least-Squares (DLS) Method for PnP". IEEE International Conference on Computer Vision, p. 383-390, 2011.

[penate2013exhaustive]:

A. Penate-Sanchez, J. Andrade-Cetto, F. Moreno-Noguer; "Exhaustive Linearization for Robust Camera Pose and Focal Length Estimation". IEEE Trans. on PAMI, vol. 35, No. 10, p. 2387-2400, 2013.