# Pose and Resectioning¶

Theia contains efficient and robust implementations of the following pose and resectioning algorithms. We attempted to make each method as general as possible so that users were not tied to Theia data structures to use the methods. The interface for all pose methods uses Eigen types for feature positions, 3D positions, and pose rotations and translations.

## Perspective Three Point (P3P)¶

bool PoseFromThreePoints(const Eigen::Vector2d feature_position[3], const Eigen::Vector3d world_point[3], std::vector<Eigen::Matrix3d> *solution_rotations, std::vector<Eigen::Vector3d> *solution_translations)

Computes camera pose using the three point algorithm and returns all possible solutions (up to 4). Follows steps from the paper “A Novel Parameterization of the Perspective-Three-Point Problem for a direct computation of Absolute Camera position and Orientation” by [Kneip]. This algorithm has been proven to be up to an order of magnitude faster than other methods. The output rotation and translation define world-to-camera transformation.

feature_position: Image points corresponding to model points. These should be calibrated image points as opposed to pixel values.

world_point: 3D location of features.

solution_rotations: the rotation matrix of the candidate solutions

solution_translation: the translation of the candidate solutions

returns: Whether the pose was computed successfully, along with the output parameters rotation and translation filled with the valid poses.

## Five Point Relative Pose¶

bool FivePointRelativePose(const Eigen::Vector2d image1_points[5], const Eigen::Vector2d image2_points[5], std::vector<Eigen::Matrix3d> *rotation, std::vector<Eigen::Vector3d> *translation)

Computes the relative pose between two cameras using 5 corresponding points. Algorithm is implemented based on “Recent Developments on Direct Relative Orientation” by [Stewenius5pt]. This algorithm is known to be more numerically stable while only slightly slower than the [Nister] method. The rotation and translation returned are defined such that $$E=[t]_{\times} * R$$ and $$y^\top * E * x = 0$$ where $$y$$ are points from image2 and $$x$$ are points from image1.

image1_points: Location of features on the image plane of image 1.

image2_points: Location of features on the image plane of image 2.

returns: Output the number of poses computed as well as the relative rotation and translation.

## Four Point Algorithm for Homography¶

bool FourPointHomography(const std::vector<Eigen::Vector2d> &image_1_points, const std::vector<Eigen::Vector2d> &image_2_points, Eigen::Matrix3d *homography)

Computes the 2D homography mapping points in image 1 to image 2 such that: $$x' = Hx$$ where $$x$$ is a point in image 1 and $$x'$$ is a point in image 2. The algorithm implemented is the DLT algorithm based on algorithm 4.2 in [HartleyZisserman].

image_1_points: Image points from image 1. At least 4 points must be passed in.

image_2_points: Image points from image 2. At least 4 points must be passed in.

homography: The computed 3x3 homography matrix.

## Eight Point Algorithm for Fundamental Matrix¶

bool EightPointFundamentalMatrix(const std::vector<Eigen::Vector2d> &image_1_points, const std::vector<Eigen::Vector2d> &image_2_points, Eigen::Matrix3d *fundamental_matrix)

Computes the fundamental matrix relating image points between two images such that $$x' F x = 0$$ for all correspondences $$x$$ and $$x'$$ in images 1 and 2 respectively. The normalized eight point algorithm is a speedy estimation of the fundamental matrix (Alg 11.1 in [HartleyZisserman]) that minimizes an algebraic error.

image_1_points: Image points from image 1. At least 8 points must be passed in.

image_2_points: Image points from image 2. At least 8 points must be passed in.

fundamental_matrix: The computed fundamental matrix.

returns: true on success, false on failure.

## Perspective N-Point¶

void DlsPnp(const std::vector<Eigen::Vector2d> &feature_position, const std::vector<Eigen::Vector3d> &world_point, std::vector<Eigen::Quaterniond> *solution_rotation, std::vector<Eigen::Vector3d> *solution_translation)

Computes the camera pose using the Perspective N-point method from “A Direct Least-Squares (DLS) Method for PnP” by [Hesch] and Stergios Roumeliotis. This method is extremely scalable and highly accurate for the PnP problem. A minimum of 4 points are required, but there is no maximum number of points allowed as this is a least-squared approach. Theoretically, up to 27 solutions may be returned, but in practice only 4 real solutions arise and in almost all cases where n >= 6 there is only one solution which places the observed points in front of the camera. The returned rotation and translations are world-to-camera transformations.

feature_position: Normalized image rays corresponding to model points. Must contain at least 4 points.

points_3d: 3D location of features. Must correspond to the image_ray of the same index. Must contain the same number of points as image_ray, and at least 4.

solution_rotation: the rotation quaternion of the candidate solutions

solution_translation: the translation of the candidate solutions

## Four Point Focal Length¶

int FourPointPoseAndFocalLength(const std::vector<Eigen::Vector2d> &feature_positions, const std::vector<Eigen::Vector3d> &world_points, std::vector<Eigen::Matrix<double, 3, 4>> *projection_matrices)

Computes the camera pose and unknown focal length of an image given four 2D-3D correspondences, following the method of [Bujnak]. This method involves computing a grobner basis from a modified constraint of the focal length and pose projection.

feature_position: Normalized image rays corresponding to model points. Must contain at least 4 points.

points_3d: 3D location of features. Must correspond to the image_ray of the same index. Must contain the same number of points as image_ray, and at least 4.

projection_matrices: The solution world-to-camera projection matrices, inclusive of the unknown focal length. For a focal length f and a camera calibration matrix $$K=diag(f, f, 1)$$, the projection matrices returned are of the form $$P = K * [R | t]$$.

## Five Point Focal Length and Radial Distortion¶

bool FivePointFocalLengthRadialDistortion(const std::vector<Eigen::Vector2d> &feature_positions, const std::vector<Eigen::Vector3d> &world_points, const int num_radial_distortion_params, std::vector<Eigen::Matrix<double, 3, 4>> *projection_matrices, std::vector<std::vector<double>> *radial_distortions)

Compute the absolute pose, focal length, and radial distortion of a camera using five 3D-to-2D correspondences [Kukelova]. The method solves for the projection matrix (up to scale) by using a cross product constraint on the standard projection equation. This allows for simple solution to the first two rows of the projection matrix, and the third row (which contains the focal length and distortion parameters) can then be solved with SVD on the remaining constraint equations from the first row of the projection matrix. See the paper for more details.

feature_positions: the 2D location of image features. Exactly five features must be passed in.

world_points: 3D world points corresponding to the features observed. Exactly five points must be passed in.

num_radial_distortion_params: The number of radial distortion paramters to
solve for. Must be 1, 2, or 3.
projection_matrices: Camera projection matrices (that encapsulate focal
length). These solutions are only valid up to scale.

radial_distortions: Each entry of this vector contains a vector with the radial distortion parameters (up to 3, but however many were specified in num_radial_distortion_params).

return: true if successful, false if not.

## Three Point Relative Pose with a Partially Known Rotation¶

void ThreePointRelativePosePartialRotation(const Eigen::Vector3d &rotation_axis, const Eigen::Vector3d image_1_rays[3], const Eigen::Vector3d image_2_rays[3], std::vector<Eigen::Quaterniond> *soln_rotations, std::vector<Eigen::Vector3d> *soln_translations)

Computes the relative pose between two cameras using 3 correspondences and a known vertical direction as a Quadratic Eigenvalue Problem [SweeneyQEP]. Up to 6 solutions are returned such that $$x_2 = R * x_1 + t$$ for rays $$x_1$$ in image 1 and rays $$x_2$$ in image 2. The axis that is passed in as a known axis of rotation (when considering rotations as an angle axis). This is equivalent to aligning the two cameras to a common direction such as the vertical direction, which can be done using IMU data.

## Four Point Relative Pose with a Partially Known Rotation¶

void FourPointRelativePosePartialRotation(const Eigen::Vector3d &rotation_axis, const Eigen::Vector3d image_1_origins[4], const Eigen::Vector3d image_1_rays[4], const Eigen::Vector3d image_2_origins[4], const Eigen::Vector3d image_2_rays[4], std::vector<Eigen::Quaterniond> *soln_rotations, std::vector<Eigen::Vector3d> *soln_translations)

Computes the relative pose between two generalized cameras using 4 correspondences and a known vertical direction as a Quadratic Eigenvalue Problem [SweeneyQEP]. A generalized camera is a camera setup with multiple cameras such that the cameras do not have the same center of projection (e.g., a multi-camera rig mounted on a car). Up to 8 solutions are returned such that $$x_2 = R * x_1 + t$$ for rays $$x_1$$ in image 1 and rays $$x_2$$ in image 2. The axis that is passed in as a known axis of rotation (when considering rotations as an angle axis). This is equivalent to aligning the two cameras to a common direction such as the vertical direction, which can be done using IMU data.