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IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE // POSSIBILITY OF SUCH DAMAGE. // // Author: Johannes L. Schoenberger (jsch-at-demuc-dot-de) #ifndef COLMAP_SRC_BASE_ESSENTIAL_MATRIX_H_ #define COLMAP_SRC_BASE_ESSENTIAL_MATRIX_H_ #include #include #include #include "util/alignment.h" #include "util/types.h" 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& points1, const std::vector& points2, Eigen::Matrix3d* R, Eigen::Vector3d* t, std::vector* 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& points1, const std::vector& points2, const std::vector& inlier_mask, Eigen::Matrix3d* E); } // namespace colmap #endif // COLMAP_SRC_BASE_ESSENTIAL_MATRIX_H_