我正在尝试计算 2 张图像的基本矩阵（同一台相机拍摄的静态场景的不同照片）。
我使用findFundamentalMat计算了它，并使用结果来计算其他矩阵（基本、旋转等）。结果显然是错误的。因此，我试图确定计算出的基本矩阵的准确性。
使用极线约束方程，我计算了基本矩阵误差。错误非常高（比如几百个）。我不知道我的代码有什么问题。我真的很感激任何帮助。特别是：在基本矩阵计算中我有什么遗漏吗？我计算错误的方式对吗？
此外，我以非常不同的匹配数运行代码。通常有很多异常值。例如，在超过 80 个匹配项的情况下，只有 10 个内点。
Mat img_1 = imread( "imgl.jpg", CV_LOAD_IMAGE_GRAYSCALE );
Mat img_2 = imread( "imgr.jpg", CV_LOAD_IMAGE_GRAYSCALE );
if( !img_1.data || !img_2.data )
{ return -1; }
//-- Step 1: Detect the keypoints using SURF Detector
int minHessian = 1000;
SurfFeatureDetector detector( minHessian );
std::vector<KeyPoint> keypoints_1, keypoints_2;
detector.detect( img_1, keypoints_1 );
detector.detect( img_2, keypoints_2 );
//-- Step 2: Calculate descriptors (feature vectors)
SurfDescriptorExtractor extractor;
Mat descriptors_1, descriptors_2;
extractor.compute( img_1, keypoints_1, descriptors_1 );
extractor.compute( img_2, keypoints_2, descriptors_2 );
//-- Step 3: Matching descriptor vectors with a brute force matcher
BFMatcher matcher(NORM_L1, true);
std::vector< DMatch > matches;
matcher.match( descriptors_1, descriptors_2, matches );
vector<Point2f>imgpts1,imgpts2;
for( unsigned int i = 0; i<matches.size(); i++ )
{
// queryIdx is the "left" image
imgpts1.push_back(keypoints_1[matches[i].queryIdx].pt);
// trainIdx is the "right" image
imgpts2.push_back(keypoints_2[matches[i].trainIdx].pt);
}
//-- Step 4: Calculate Fundamental matrix
Mat f_mask;
Mat F = findFundamentalMat (imgpts1, imgpts2, FM_RANSAC, 0.5, 0.99, f_mask);
//-- Step 5: Calculate Fundamental matrix error
//Camera intrinsics
double data[] = {1189.46 , 0.0, 805.49,
0.0, 1191.78, 597.44,
0.0, 0.0, 1.0};
Mat K(3, 3, CV_64F, data);
//Camera distortion parameters
double dist[] = { -0.03432, 0.05332, -0.00347, 0.00106, 0.00000};
Mat D(1, 5, CV_64F, dist);
//working with undistorted points
vector<Point2f> undistorted_1,undistorted_2;
vector<Point3f> line_1, line_2;
undistortPoints(imgpts1,undistorted_1,K,D);
undistortPoints(imgpts2,undistorted_2,K,D);
computeCorrespondEpilines(undistorted_1,1,F,line_1);
computeCorrespondEpilines(undistorted_2,2,F,line_2);
double f_err=0.0;
double fx,fy,cx,cy;
fx=K.at<double>(0,0);fy=K.at<double>(1,1);cx=K.at<double>(0,2);cy=K.at<double>(1,2);
Point2f pt1, pt2;
int inliers=0;
//calculation of fundamental matrix error for inliers
for (int i=0; i<f_mask.size().height; i++)
if (f_mask.at<char>(i)==1)
{
inliers++;
//calculate non-normalized values
pt1.x = undistorted_1[i].x * fx + cx;
pt1.y = undistorted_1[i].y * fy + cy;
pt2.x = undistorted_2[i].x * fx + cx;
pt2.y = undistorted_2[i].y * fy + cy;
f_err += = fabs(pt1.x*line_2[i].x +
pt1.y*line_2[i].y + line_2[i].z)
+ fabs(pt2.x*line_1[i].x +
pt2.y*line_1[i].y + line_1[i].z);
}
double AvrErr = f_err/inliers;