Updating UPNP documentation

modules/calib3d/doc/camera_calibration_and_3d_reconstruction.rst

@@ -572,7 +572,7 @@ Finds an object pose from 3D-2D point correspondences.
 
     :param imagePoints: Array of corresponding image points, 2xN/Nx2 1-channel or 1xN/Nx1 2-channel, where N is the number of points.  ``vector<Point2f>``  can be also passed here.
 
-    :param cameraMatrix: Input camera matrix  :math:`A = \vecthreethree{fx}{0}{cx}{0}{fy}{cy}{0}{0}{1}` .
+    :param cameraMatrix: Input camera matrix  :math:`A = \vecthreethree{fx}{0}{cx}{0}{fy}{cy}{0}{0}{1}` . 
 
     :param distCoeffs: Input vector of distortion coefficients  :math:`(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])`  of 4, 5, 8 or 12 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
 
@@ -588,6 +588,7 @@ Finds an object pose from 3D-2D point correspondences.
             *  **SOLVEPNP_P3P**  Method is based on the paper of X.S. Gao, X.-R. Hou, J. Tang, H.-F. Chang "Complete Solution Classification for the Perspective-Three-Point Problem". In this case the function requires exactly four object and image points.
             *  **SOLVEPNP_EPNP** Method has been introduced by F.Moreno-Noguer, V.Lepetit and P.Fua in the paper "EPnP: Efficient Perspective-n-Point Camera Pose Estimation".
             *  **SOLVEPNP_DLS**  Method is based on the paper of Joel A. Hesch and Stergios I. Roumeliotis. "A Direct Least-Squares (DLS) Method for PnP".
+            *  **SOLVEPNP_UPNP** Method is based on the paper of A.Penate-Sanchez, J.Andrade-Cetto, F.Moreno-Noguer. "Exhaustive Linearization for Robust Camera Pose and Focal Length Estimation". In this case the function estimate the parameters :math:`f_x` and :math:`f_y` which are not needed as a input. 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.