| // Copyright (c) 2022, ETH Zurich and UNC Chapel Hill. | |
| // All rights reserved. | |
| // | |
| // Redistribution and use in source and binary forms, with or without | |
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| // | |
| // Author: Johannes L. Schoenberger (jsch-at-demuc-dot-de) | |
| namespace colmap { | |
| std::vector<AffineTransformEstimator::M_t> AffineTransformEstimator::Estimate( | |
| const std::vector<X_t>& points1, const std::vector<Y_t>& points2) { | |
| CHECK_EQ(points1.size(), points2.size()); | |
| CHECK_GE(points1.size(), 3); | |
| // Sets up the linear system that we solve to obtain a least squared solution | |
| // for the affine transformation. | |
| Eigen::MatrixXd C(2 * points1.size(), 6); | |
| C.setZero(); | |
| Eigen::VectorXd b(2 * points1.size(), 1); | |
| for (size_t i = 0; i < points1.size(); ++i) { | |
| const Eigen::Vector2d& x1 = points1[i]; | |
| const Eigen::Vector2d& x2 = points2[i]; | |
| C(2 * i, 0) = x1(0); | |
| C(2 * i, 1) = x1(1); | |
| C(2 * i, 2) = 1.0f; | |
| b(2 * i) = x2(0); | |
| C(2 * i + 1, 3) = x1(0); | |
| C(2 * i + 1, 4) = x1(1); | |
| C(2 * i + 1, 5) = 1.0f; | |
| b(2 * i + 1) = x2(1); | |
| } | |
| const Eigen::VectorXd nullspace = | |
| C.jacobiSvd(Eigen::ComputeThinU | Eigen::ComputeThinV).solve(b); | |
| Eigen::Map<const Eigen::Matrix<double, 3, 2>> A_t(nullspace.data()); | |
| const std::vector<M_t> models = {A_t.transpose()}; | |
| return models; | |
| } | |
| void AffineTransformEstimator::Residuals(const std::vector<X_t>& points1, | |
| const std::vector<Y_t>& points2, | |
| const M_t& A, | |
| std::vector<double>* residuals) { | |
| CHECK_EQ(points1.size(), points2.size()); | |
| residuals->resize(points1.size()); | |
| // Note that this code might not be as nice as Eigen expressions, | |
| // but it is significantly faster in various tests. | |
| const double A_00 = A(0, 0); | |
| const double A_01 = A(0, 1); | |
| const double A_02 = A(0, 2); | |
| const double A_10 = A(1, 0); | |
| const double A_11 = A(1, 1); | |
| const double A_12 = A(1, 2); | |
| for (size_t i = 0; i < points1.size(); ++i) { | |
| const double s_0 = points1[i](0); | |
| const double s_1 = points1[i](1); | |
| const double d_0 = points2[i](0); | |
| const double d_1 = points2[i](1); | |
| const double pd_0 = A_00 * s_0 + A_01 * s_1 + A_02; | |
| const double pd_1 = A_10 * s_0 + A_11 * s_1 + A_12; | |
| const double dd_0 = d_0 - pd_0; | |
| const double dd_1 = d_1 - pd_1; | |
| (*residuals)[i] = dd_0 * dd_0 + dd_1 * dd_1; | |
| } | |
| } | |
| } // namespace colmap | |