miop.registration

Classes

RegistrationByCorresp()	Class for performing 3D point cloud registration using Procrustes alignment, based on known point correspondences.
RegistrationByICP([threshold, trans_init])	Class for performing 3D point cloud registration using the Iterative Closest Point (ICP) algorithm.

class miop.registration.RegistrationByCorresp

Bases: DAGNode

Class for performing 3D point cloud registration using Procrustes alignment, based on known point correspondences.

pcds_tr

List of registered point clouds. Set after calling eval().

Type:

list of open3d.geometry.PointCloud

compute_point_cloud(camera_position, dense_matches)

Triangulates 3D points from matched 2D correspondences using affine camera models.

Parameters:

• camera_position (list of dict) – List containing intrinsic matrices and rotations for each camera/view.

• dense_matches (list) – 2D point correspondences across image pairs.

Returns:

Reconstructed 3D point clouds for each image pair.

Return type:

list of list of np.ndarray

eval(pcds, camera_position, corresponding_points)

Aligns a set of point clouds using Procrustes registration based on known correspondences.

Parameters:

• pcds (list of list of np.ndarray) – Each element in the outer list corresponds to a face/view. Each inner element is a point cloud (N, 3).

• camera_position (list of dict) – Camera intrinsic and rotation parameters for triangulation.

• corresponding_points (list) – List of corresponding 2D points across views for triangulation.

Returns:

Transformed point clouds aligned to a common frame.

Return type:

list of open3d.geometry.PointCloud

procrustes_3d(X, Y)

Computes the optimal rotation and translation to align Y to X using the Procrustes algorithm.

Parameters:

• X (np.ndarray, shape (N, 3)) – Reference 3D point cloud.

• Y (np.ndarray, shape (N, 3)) – Point cloud to align.

Returns:

• R (np.ndarray, shape (3, 3)) – Optimal rotation matrix.

• t (np.ndarray, shape (3,)) – Optimal translation vector.

solve_pinv(b, A)

Solves the inhomogeneous linear system Ax = b using the Moore-Penrose pseudoinverse.

Parameters:

• b (np.ndarray) – Right-hand side matrix (observations).

• A (np.ndarray) – Design matrix.

Returns:

Solution x such that Ax ≈ b.

Return type:

np.ndarray

class miop.registration.RegistrationByICP(threshold=32.0, trans_init=array([[1., 0., 0., 0.], [0., 1., 0., 0.], [0., 0., 1., 0.], [0., 0., 0., 1.]]))

Bases: DAGNode

Class for performing 3D point cloud registration using the Iterative Closest Point (ICP) algorithm. This class is a wrapper for Open3D’s ICP implementation.

threshold

Distance threshold for ICP correspondence matching. Default is 32.0.

Type:

float

trans_init

Initial transformation matrix used to start the ICP process. Default is identity.

Type:

np.ndarray, shape (4, 4)

pcds_reg

List of registered point clouds after running the eval() method.

Type:

list of open3d.geometry.PointCloud

eval(pcds)

Registers a list of point clouds using the ICP algorithm.

Parameters:

pcds (list of list of np.ndarray) – Each sublist represents a group of point clouds for a given face (or view group). Each point cloud is expected to be an (N, 3) NumPy array.

Returns:

Registered and merged point clouds.

Return type:

list of open3d.geometry.PointCloud

save(path)

Saves the registered point cloud to a PLY file.

Parameters:

path (str) – Path to save the output PLY file. Must end with ‘.ply’.

Raises:

ValueError – If self.pcds_reg is None or if the path does not end with ‘.ply’.

show()

Visualizes the registered point clouds using Open3D.

Raises:

ValueError – If self.pcds_reg is None (i.e., eval() has not been run).