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| // | |
| // Author: Johannes L. Schoenberger (jsch-at-demuc-dot-de) | |
| namespace colmap { | |
| // Triangulate 3D point from corresponding image point observations. | |
| // | |
| // Implementation of the direct linear transform triangulation method in | |
| // R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision, | |
| // Cambridge Univ. Press, 2003. | |
| // | |
| // @param proj_matrix1 Projection matrix of the first image as 3x4 matrix. | |
| // @param proj_matrix2 Projection matrix of the second image as 3x4 matrix. | |
| // @param point1 Corresponding 2D point in first image. | |
| // @param point2 Corresponding 2D point in second image. | |
| // | |
| // @return Triangulated 3D point. | |
| Eigen::Vector3d TriangulatePoint(const Eigen::Matrix3x4d& proj_matrix1, | |
| const Eigen::Matrix3x4d& proj_matrix2, | |
| const Eigen::Vector2d& point1, | |
| const Eigen::Vector2d& point2); | |
| // Triangulate multiple 3D points from multiple image correspondences. | |
| std::vector<Eigen::Vector3d> TriangulatePoints( | |
| const Eigen::Matrix3x4d& proj_matrix1, | |
| const Eigen::Matrix3x4d& proj_matrix2, | |
| const std::vector<Eigen::Vector2d>& points1, | |
| const std::vector<Eigen::Vector2d>& points2); | |
| // Triangulate point from multiple views minimizing the L2 error. | |
| // | |
| // @param proj_matrices Projection matrices of multi-view observations. | |
| // @param points Image observations of multi-view observations. | |
| // | |
| // @return Estimated 3D point. | |
| Eigen::Vector3d TriangulateMultiViewPoint( | |
| const std::vector<Eigen::Matrix3x4d>& proj_matrices, | |
| const std::vector<Eigen::Vector2d>& points); | |
| // Triangulate optimal 3D point from corresponding image point observations by | |
| // finding the optimal image observations. | |
| // | |
| // Note that camera poses should be very good in order for this method to yield | |
| // good results. Otherwise just use `TriangulatePoint`. | |
| // | |
| // Implementation of the method described in | |
| // P. Lindstrom, "Triangulation Made Easy," IEEE Computer Vision and Pattern | |
| // Recognition 2010, pp. 1554-1561, June 2010. | |
| // | |
| // @param proj_matrix1 Projection matrix of the first image as 3x4 matrix. | |
| // @param proj_matrix2 Projection matrix of the second image as 3x4 matrix. | |
| // @param point1 Corresponding 2D point in first image. | |
| // @param point2 Corresponding 2D point in second image. | |
| // | |
| // @return Triangulated optimal 3D point. | |
| Eigen::Vector3d TriangulateOptimalPoint(const Eigen::Matrix3x4d& proj_matrix1, | |
| const Eigen::Matrix3x4d& proj_matrix2, | |
| const Eigen::Vector2d& point1, | |
| const Eigen::Vector2d& point2); | |
| // Triangulate multiple optimal 3D points from multiple image correspondences. | |
| std::vector<Eigen::Vector3d> TriangulateOptimalPoints( | |
| const Eigen::Matrix3x4d& proj_matrix1, | |
| const Eigen::Matrix3x4d& proj_matrix2, | |
| const std::vector<Eigen::Vector2d>& points1, | |
| const std::vector<Eigen::Vector2d>& points2); | |
| // Calculate angle in radians between the two rays of a triangulated point. | |
| double CalculateTriangulationAngle(const Eigen::Vector3d& proj_center1, | |
| const Eigen::Vector3d& proj_center2, | |
| const Eigen::Vector3d& point3D); | |
| std::vector<double> CalculateTriangulationAngles( | |
| const Eigen::Vector3d& proj_center1, const Eigen::Vector3d& proj_center2, | |
| const std::vector<Eigen::Vector3d>& points3D); | |
| } // namespace colmap | |