Function recover_pose_triangulated

pub fn recover_pose_triangulated(
    e: &impl ToInputArray,
    points1: &impl ToInputArray,
    points2: &impl ToInputArray,
    camera_matrix: &impl ToInputArray,
    r: &mut impl ToOutputArray,
    t: &mut impl ToOutputArray,
    distance_thresh: f64,
    mask: &mut impl ToInputOutputArray,
    triangulated_points: &mut impl ToOutputArray,
) -> Result<i32>

Recovers the relative camera rotation and the translation from an estimated essential matrix and the corresponding points in two images, using chirality check. Returns the number of inliers that pass the check.

Parameters

• E: The input essential matrix.
• points1: Array of N 2D points from the first image. The point coordinates should be floating-point (single or double precision).
• points2: Array of the second image points of the same size and format as points1 .
• cameraMatrix: Camera intrinsic matrix . Note that this function assumes that points1 and points2 are feature points from cameras with the same camera intrinsic matrix.
• R: Output rotation matrix. Together with the translation vector, this matrix makes up a tuple that performs a change of basis from the first camera’s coordinate system to the second camera’s coordinate system. Note that, in general, t can not be used for this tuple, see the parameter described below.
• t: Output translation vector. This vector is obtained by [decomposeEssentialMat] and therefore is only known up to scale, i.e. t is the direction of the translation vector and has unit length.
• mask: Input/output mask for inliers in points1 and points2. If it is not empty, then it marks inliers in points1 and points2 for the given essential matrix E. Only these inliers will be used to recover pose. In the output mask only inliers which pass the chirality check.

This function decomposes an essential matrix using [decomposeEssentialMat] and then verifies possible pose hypotheses by doing chirality check. The chirality check means that the triangulated 3D points should have positive depth. Some details can be found in Nister03.

This function can be used to process the output E and mask from [findEssentialMat]. In this scenario, points1 and points2 are the same input for find_essential_mat :

   // Example. Estimation of fundamental matrix using the RANSAC algorithm
   int point_count = 100;
   vector<Point2f> points1(point_count);
   vector<Point2f> points2(point_count);

   // initialize the points here ...
   for( int i = 0; i < point_count; i++ )
   {
       points1[i] = ...;
       points2[i] = ...;
   }

   // cametra matrix with both focal lengths = 1, and principal point = (0, 0)
   Mat cameraMatrix = Mat::eye(3, 3, CV_64F);

   Mat E, R, t, mask;

   E = findEssentialMat(points1, points2, cameraMatrix, RANSAC, 0.999, 1.0, mask);
   recoverPose(E, points1, points2, cameraMatrix, R, t, mask);

Overloaded parameters

• E: The input essential matrix.
• points1: Array of N 2D points from the first image. The point coordinates should be floating-point (single or double precision).
• points2: Array of the second image points of the same size and format as points1.
• cameraMatrix: Camera intrinsic matrix . Note that this function assumes that points1 and points2 are feature points from cameras with the same camera intrinsic matrix.
• R: Output rotation matrix. Together with the translation vector, this matrix makes up a tuple that performs a change of basis from the first camera’s coordinate system to the second camera’s coordinate system. Note that, in general, t can not be used for this tuple, see the parameter description below.
• t: Output translation vector. This vector is obtained by [decomposeEssentialMat] and therefore is only known up to scale, i.e. t is the direction of the translation vector and has unit length.
• distanceThresh: threshold distance which is used to filter out far away points (i.e. infinite points).
• mask: Input/output mask for inliers in points1 and points2. If it is not empty, then it marks inliers in points1 and points2 for the given essential matrix E. Only these inliers will be used to recover pose. In the output mask only inliers which pass the chirality check.
• triangulatedPoints: 3D points which were reconstructed by triangulation.

This function differs from the one above that it outputs the triangulated 3D point that are used for the chirality check.

C++ default parameters

• mask: noArray()
• triangulated_points: noArray()