Function find_essential_mat_3

pub fn find_essential_mat_3(
    points1: &impl ToInputArray,
    points2: &impl ToInputArray,
    camera_matrix1: &impl ToInputArray,
    dist_coeffs1: &impl ToInputArray,
    camera_matrix2: &impl ToInputArray,
    dist_coeffs2: &impl ToInputArray,
    method: i32,
    prob: f64,
    threshold: f64,
    mask: &mut impl ToOutputArray,
) -> Result<Mat>

Calculates an essential matrix from the corresponding points in two images from potentially two different cameras.

Parameters

• points1: Array of N (N >= 5) 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.
• cameraMatrix1: Camera matrix for the first camera .
• cameraMatrix2: Camera matrix for the second camera .
• distCoeffs1: Input vector of distortion coefficients for the first camera of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
• distCoeffs2: Input vector of distortion coefficients for the second camera of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
• method: Method for computing an essential matrix.
• RANSAC for the RANSAC algorithm.
• LMEDS for the LMedS algorithm.
• prob: Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct.
• threshold: Parameter used for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise.
• mask: Output array of N elements, every element of which is set to 0 for outliers and to 1 for the other points. The array is computed only in the RANSAC and LMedS methods.

This function estimates essential matrix based on the five-point algorithm solver in Nister03 . SteweniusCFS is also a related. The epipolar geometry is described by the following equation:

where is an essential matrix, and are corresponding points in the first and the second images, respectively. The result of this function may be passed further to decompose_essential_mat or recover_pose to recover the relative pose between cameras.

C++ default parameters

• method: RANSAC
• prob: 0.999
• threshold: 1.0
• mask: noArray()