SolvePnp doesn't give the same result with four points and more points
I'm computing the pose of the camera using SolvePnP
function of OpenCv
library and I'm facing the issue that when I give four point it gives me slightly better result than if I give more points. Here is an example of the code with some points as an example:
#include <opencv2/opencv.hpp>
int main(int argc, char **argv)
{
//with 11 points
std::vector<cv::Point2f> image_points_eleven = {
cv::Point2f(905.63, 694.37),
cv::Point2f(936.403, 696.781),
cv::Point2f(988.424, 700.553),
cv::Point2f(1020.02, 702.846),
cv::Point2f(1016.45, 741.839),
cv::Point2f(1012.79, 781.955),
cv::Point2f(1009.06, 822.609),
cv::Point2f(951.48, 815.937),
cv::Point2f(894.528, 810.86),
cv::Point2f(898.26, 772.418),
cv::Point2f(901.867, 732.744) };
std::vector<cv::Point3f> model_points_eleven = {
cv::Point3f(-155.32,155.32, 0),
cv::Point3f(-70.6,155.32, 0),
cv::Point3f(70.6,155.32, 0),
cv::Point3f(155.32,155.32, 0),
cv::Point3f(155.32,52.95, 0),
cv::Point3f(155.32,-52.95, 0),
cv::Point3f(155.32,-158.85, 0),
cv::Point3f(0, -155.32, 0),
cv::Point3f(-155.32,-155.32, 0),
cv::Point3f(-155.32,-52.95, 0),
cv::Point3f(-155.32, 52.95, 0)};
//with 4 points
std::vector<cv::Point2f> image_points_four = {
cv::Point2f(921.808,714.396),
cv::Point2f(999.474,720.263),
cv::Point2f(992.486,800.519),
cv::Point2f(914.465,793.569) };
std::vector<cv::Point3f> model_points_four = {
cv::Point3f(-211.5 / 2.0f, 211.5 / 2.0f, 0),
cv::Point3f(-211.5 / 2.0f, 211.5 / 2.0f, 0),
cv::Point3f(-211.5 / 2.0f, 211.5 / 2.0f, 0),
cv::Point3f(-211.5 / 2.0f, 211.5 / 2.0f, 0)};
// Camera internals
cv::Mat camera_matrix = (cv::Mat_<double>(3,3) << 1296.2477, 0, 1028, 0 , 1296.2477, 771, 0, 0, 1);
cv::Mat dist_coeffs = (cv::Mat_<double>(1,5) << -0.12658285, 0.13909541, 0, 0, -0.040676277); // Assuming no lens distortion
// Output rotation and translation
cv::Mat rotation_vector; // Rotation in axis-angle form
cv::Mat translation_vector;
// Solve for pose 11 points
cv::solvePnP(model_points_eleven, image_points_eleven, camera_matrix, dist_coeffs, rotation_vector, translation_vector);
rotation_vector.convertTo(rotation_vector, CV_32F);
translation_vector.convertTo(translation_vector, CV_32F);
std::cout << "Rotation Vector 11pts: " << rotation_vector << std::endl;
std::cout << "Translation Vector 11pts: " << translation_vector << std::endl;
// Solve for pose 11 points
cv::solvePnP(model_points_four, image_points_four, camera_matrix, dist_coeffs, rotation_vector, translation_vector);
rotation_vector.convertTo(rotation_vector, CV_32F);
translation_vector.convertTo(translation_vector, CV_32F);
std::cout << "Rotation Vector 4pts: " << rotation_vector << std::endl;
std::cout << "Translation Vector 4pts: " << translation_vector << std::endl;
}
I do get:
Rotation Vector 11pts: [3.0777242; 0.13331571; -0.26817828]
Translation Vector 11pts: [-187.29686; -36.374325; 3410.5]
Rotation Vector 4pts: [1.5553488; 0.13531595; -0.19250046]
Translation Vector 4pts: [83.4842; -4.0712709; -163.0168]
Comparing to an external results (lazer), for me I should get more precision the more points I have or at least the same result, however it doesn ...
With 4 points, the Z-component of the translation vector is negative, which implies that the points are behind the camera. Also, you have 4 times the same 3D coordinates in the 4 points case.
With real data, you have to take into account the precision of the 2 coordinates. 4 2D/3D points with precise 2D coordinates with give better results than 11 2D/3D coordinates with noisy data.