| // Copyright (c) 2022, ETH Zurich and UNC Chapel Hill. | |
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| // | |
| // Author: Johannes L. Schoenberger (jsch-at-demuc-dot-de) | |
| namespace colmap { | |
| // Decompose an essential matrix into the possible rotations and translations. | |
| // | |
| // The first pose is assumed to be P = [I | 0] and the set of four other | |
| // possible second poses are defined as: {[R1 | t], [R2 | t], | |
| // [R1 | -t], [R2 | -t]} | |
| // | |
| // @param E 3x3 essential matrix. | |
| // @param R1 First possible 3x3 rotation matrix. | |
| // @param R2 Second possible 3x3 rotation matrix. | |
| // @param t 3x1 possible translation vector (also -t possible). | |
| void DecomposeEssentialMatrix(const Eigen::Matrix3d& E, Eigen::Matrix3d* R1, | |
| Eigen::Matrix3d* R2, Eigen::Vector3d* t); | |
| // Recover the most probable pose from the given essential matrix. | |
| // | |
| // The pose of the first image is assumed to be P = [I | 0]. | |
| // | |
| // @param E 3x3 essential matrix. | |
| // @param points1 First set of corresponding points. | |
| // @param points2 Second set of corresponding points. | |
| // @param inlier_mask Only points with `true` in the inlier mask are | |
| // considered in the cheirality test. Size of the | |
| // inlier mask must match the number of points N. | |
| // @param R Most probable 3x3 rotation matrix. | |
| // @param t Most probable 3x1 translation vector. | |
| // @param points3D Triangulated 3D points infront of camera. | |
| void PoseFromEssentialMatrix(const Eigen::Matrix3d& E, | |
| const std::vector<Eigen::Vector2d>& points1, | |
| const std::vector<Eigen::Vector2d>& points2, | |
| Eigen::Matrix3d* R, Eigen::Vector3d* t, | |
| std::vector<Eigen::Vector3d>* points3D); | |
| // Compose essential matrix from relative camera poses. | |
| // | |
| // Assumes that first camera pose has projection matrix P = [I | 0], and | |
| // pose of second camera is given as transformation from world to camera system. | |
| // | |
| // @param R 3x3 rotation matrix. | |
| // @param t 3x1 translation vector. | |
| // | |
| // @return 3x3 essential matrix. | |
| Eigen::Matrix3d EssentialMatrixFromPose(const Eigen::Matrix3d& R, | |
| const Eigen::Vector3d& t); | |
| // Compose essential matrix from two absolute camera poses. | |
| // | |
| // @param proj_matrix1 3x4 projection matrix. | |
| // @param proj_matrix2 3x4 projection matrix. | |
| // | |
| // @return 3x3 essential matrix. | |
| Eigen::Matrix3d EssentialMatrixFromAbsolutePoses( | |
| const Eigen::Matrix3x4d& proj_matrix1, | |
| const Eigen::Matrix3x4d& proj_matrix2); | |
| // Find optimal image points, such that: | |
| // | |
| // optimal_point1^t * E * optimal_point2 = 0 | |
| // | |
| // as described in: | |
| // | |
| // Lindstrom, P., "Triangulation made easy", | |
| // Computer Vision and Pattern Recognition (CVPR), | |
| // 2010 IEEE Conference on , vol., no., pp.1554,1561, 13-18 June 2010 | |
| // | |
| // @param E Essential or fundamental matrix. | |
| // @param point1 Corresponding 2D point in first image. | |
| // @param point2 Corresponding 2D point in second image. | |
| // @param optimal_point1 Estimated optimal image point in the first image. | |
| // @param optimal_point2 Estimated optimal image point in the second image. | |
| void FindOptimalImageObservations(const Eigen::Matrix3d& E, | |
| const Eigen::Vector2d& point1, | |
| const Eigen::Vector2d& point2, | |
| Eigen::Vector2d* optimal_point1, | |
| Eigen::Vector2d* optimal_point2); | |
| // Compute the location of the epipole in homogeneous coordinates. | |
| // | |
| // @param E 3x3 essential matrix. | |
| // @param left_image If true, epipole in left image is computed, | |
| // else in right image. | |
| // | |
| // @return Epipole in homogeneous coordinates. | |
| Eigen::Vector3d EpipoleFromEssentialMatrix(const Eigen::Matrix3d& E, | |
| const bool left_image); | |
| // Invert the essential matrix, i.e. if the essential matrix E describes the | |
| // transformation from camera A to B, the inverted essential matrix E' describes | |
| // the transformation from camera B to A. | |
| // | |
| // @param E 3x3 essential matrix. | |
| // | |
| // @return Inverted essential matrix. | |
| Eigen::Matrix3d InvertEssentialMatrix(const Eigen::Matrix3d& matrix); | |
| // Refine essential matrix. | |
| // | |
| // Decomposes the essential matrix into rotation and translation components | |
| // and refines the relative pose using the function `RefineRelativePose`. | |
| // | |
| // @param E 3x3 essential matrix. | |
| // @param points1 First set of corresponding points. | |
| // @param points2 Second set of corresponding points. | |
| // @param inlier_mask Inlier mask for corresponding points. | |
| // @param options Solver options. | |
| // | |
| // @return Flag indicating if solution is usable. | |
| bool RefineEssentialMatrix(const ceres::Solver::Options& options, | |
| const std::vector<Eigen::Vector2d>& points1, | |
| const std::vector<Eigen::Vector2d>& points2, | |
| const std::vector<char>& inlier_mask, | |
| Eigen::Matrix3d* E); | |
| } // namespace colmap | |