xdem.coreg.ICP

class xdem.coreg.ICP(max_iterations=100, tolerance=0.05, rejection_scale=2.5, num_levels=6, subsample=500000.0)

Iterative closest point registration, based on Besl and McKay (1992), https://doi.org/10.1117/12.57955.

Estimates a rigid transform (rotation + translation) between two elevation datasets.

The estimated transform is stored in the self.meta[“outputs”][“affine”] key “matrix”, with rotation centered on the coordinates in the key “centroid”. The translation parameters are also stored individually in the keys “shift_x”, “shift_y” and “shift_z” (in georeferenced units for horizontal shifts, and unit of the elevation dataset inputs for the vertical shift).

Requires ‘opencv’. See opencv doc for more info: https://docs.opencv.org/master/dc/d9b/classcv_1_1ppf__match__3d_1_1ICP.html

__init__(max_iterations=100, tolerance=0.05, rejection_scale=2.5, num_levels=6, subsample=500000.0)

Instantiate an ICP coregistration object.

Parameters:

• max_iterations (int) – The maximum allowed iterations before stopping.

• tolerance (float) – The residual change threshold after which to stop the iterations.

• rejection_scale (float) – The threshold (std * rejection_scale) to consider points as outliers.

• num_levels (int) – Number of octree levels to consider. A higher number is faster but may be more inaccurate.

• subsample (float | int) – Subsample the input for speed-up. <1 is parsed as a fraction. >1 is a pixel count.

Methods

__init__([max_iterations, tolerance, ...])	Instantiate an ICP coregistration object.
apply(elev[, bias_vars, resample, ...])	Apply the estimated transform to a DEM.
centroid()	Get the centroid of the coregistration, if defined.
copy()	Return an identical copy of the class.
error(reference_elev, to_be_aligned_elev[, ...])	Calculate the error of a coregistration approach.
fit(reference_elev, to_be_aligned_elev[, ...])	Estimate the coregistration transform on the given DEMs.
fit_and_apply(reference_elev, to_be_aligned_elev)	Estimate and apply the coregistration to a pair of elevation data.
from_matrix(matrix)	Instantiate a generic Coreg class from a transformation matrix.
from_rotations([x_rot, y_rot, z_rot])	Instantiate a generic Coreg class from a X/Y/Z rotation.
from_translations([x_off, y_off, z_off])	Instantiate a generic Coreg class from a X/Y/Z translation.
info([as_str])	Summarize information about this coregistration.
residuals(reference_elev, to_be_aligned_elev)	Calculate the residual offsets (the difference) between two DEMs after applying the transformation.
to_matrix()	Convert the transform to a 4x4 transformation matrix.
to_rotations()	Extract X/Y/Z euler rotations (extrinsic convention) from the affine transformation matrix.
to_translations()	Extract X/Y/Z translations from the affine transformation matrix.

Attributes

is_affine	Check if the transform be explained by a 3D affine transform.
is_translation
meta	Metadata dictionary of the coregistration.