Consistent transform convention for CenterAndNormalizeImagePoints (co…

…lmap#2092)
KellyGui · Aug 1, 2023 · 911779d · 911779d
1 parent c1e43c0
commit 911779d
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Showing 3 changed files with 16 additions and 17 deletions.
diff --git a/src/colmap/estimators/essential_matrix.cc b/src/colmap/estimators/essential_matrix.cc
@@ -166,10 +166,10 @@ EssentialMatrixEightPointEstimator::Estimate(const std::vector<X_t>& points1,
   // Center and normalize image points for better numerical stability.
   std::vector<X_t> normed_points1;
   std::vector<Y_t> normed_points2;
-  Eigen::Matrix3d points1_norm_matrix;
-  Eigen::Matrix3d points2_norm_matrix;
-  CenterAndNormalizeImagePoints(points1, &normed_points1, &points1_norm_matrix);
-  CenterAndNormalizeImagePoints(points2, &normed_points2, &points2_norm_matrix);
+  Eigen::Matrix3d normed_from_orig1;
+  Eigen::Matrix3d normed_from_orig2;
+  CenterAndNormalizeImagePoints(points1, &normed_points1, &normed_from_orig1);
+  CenterAndNormalizeImagePoints(points2, &normed_points2, &normed_from_orig2);
 
   // Setup homogeneous linear equation as x2' * F * x1 = 0.
   Eigen::Matrix<double, Eigen::Dynamic, 9> cmatrix(points1.size(), 9);
@@ -188,8 +188,8 @@ EssentialMatrixEightPointEstimator::Estimate(const std::vector<X_t>& points1,
   const Eigen::Map<const Eigen::Matrix3d> ematrix_t(ematrix_nullspace.data());
 
   // De-normalize to image points.
-  const Eigen::Matrix3d E_raw = points2_norm_matrix.transpose() *
-                                ematrix_t.transpose() * points1_norm_matrix;
+  const Eigen::Matrix3d E_raw = normed_from_orig2.transpose() *
+                                ematrix_t.transpose() * normed_from_orig1;
 
   // Enforcing the internal constraint that two singular values must be equal
   // and one must be zero.

diff --git a/src/colmap/estimators/fundamental_matrix.cc b/src/colmap/estimators/fundamental_matrix.cc
@@ -158,10 +158,10 @@ FundamentalMatrixEightPointEstimator::Estimate(
   // Center and normalize image points for better numerical stability.
   std::vector<X_t> normed_points1;
   std::vector<Y_t> normed_points2;
-  Eigen::Matrix3d points1_norm_matrix;
-  Eigen::Matrix3d points2_norm_matrix;
-  CenterAndNormalizeImagePoints(points1, &normed_points1, &points1_norm_matrix);
-  CenterAndNormalizeImagePoints(points2, &normed_points2, &points2_norm_matrix);
+  Eigen::Matrix3d normed_from_orig1;
+  Eigen::Matrix3d normed_from_orig2;
+  CenterAndNormalizeImagePoints(points1, &normed_points1, &normed_from_orig1);
+  CenterAndNormalizeImagePoints(points2, &normed_points2, &normed_from_orig2);
 
   // Setup homogeneous linear equation as x2' * F * x1 = 0.
   Eigen::Matrix<double, Eigen::Dynamic, 9> cmatrix(points1.size(), 9);
@@ -189,7 +189,7 @@ FundamentalMatrixEightPointEstimator::Estimate(
                             singular_values.asDiagonal() *
                             fmatrix_svd.matrixV().transpose();
 
-  return {points2_norm_matrix.transpose() * F * points1_norm_matrix};
+  return {normed_from_orig2.transpose() * F * normed_from_orig1};
 }
 
 void FundamentalMatrixEightPointEstimator::Residuals(

diff --git a/src/colmap/estimators/homography_matrix.cc b/src/colmap/estimators/homography_matrix.cc
@@ -50,10 +50,10 @@ std::vector<HomographyMatrixEstimator::M_t> HomographyMatrixEstimator::Estimate(
   // Center and normalize image points for better numerical stability.
   std::vector<X_t> normed_points1;
   std::vector<Y_t> normed_points2;
-  Eigen::Matrix3d points1_norm_matrix;
-  Eigen::Matrix3d points2_norm_matrix;
-  CenterAndNormalizeImagePoints(points1, &normed_points1, &points1_norm_matrix);
-  CenterAndNormalizeImagePoints(points2, &normed_points2, &points2_norm_matrix);
+  Eigen::Matrix3d normed_from_orig1;
+  Eigen::Matrix3d normed_from_orig2;
+  CenterAndNormalizeImagePoints(points1, &normed_points1, &normed_from_orig1);
+  CenterAndNormalizeImagePoints(points2, &normed_points2, &normed_from_orig2);
 
   // Setup constraint matrix.
   Eigen::Matrix<double, Eigen::Dynamic, 9> A = Eigen::MatrixXd::Zero(2 * N, 9);
@@ -86,8 +86,7 @@ std::vector<HomographyMatrixEstimator::M_t> HomographyMatrixEstimator::Estimate(
   const Eigen::VectorXd nullspace = svd.matrixV().col(8);
   Eigen::Map<const Eigen::Matrix3d> H_t(nullspace.data());
 
-  return {points2_norm_matrix.inverse() * H_t.transpose() *
-          points1_norm_matrix};
+  return {normed_from_orig2.inverse() * H_t.transpose() * normed_from_orig1};
 }
 
 void HomographyMatrixEstimator::Residuals(const std::vector<X_t>& points1,